From e04f07eacf09670454de9b0924cbc091fd2b695c Mon Sep 17 00:00:00 2001 From: Yedidia AGNIMO Date: Mon, 18 Mar 2024 10:39:42 +0100 Subject: [PATCH 01/16] feat: start working on Deep Belief Network (DBN). * Rename experiments notebook. * Start working on DBN in the notebook. --- ...ipal_RBM_alpha.ipynb => experiments.ipynb} | 459 ++++++++++++------ src/principal_dbn_alpha.py | 0 2 files changed, 313 insertions(+), 146 deletions(-) rename notebook/{principal_RBM_alpha.ipynb => experiments.ipynb} (61%) create mode 100644 src/principal_dbn_alpha.py diff --git a/notebook/principal_RBM_alpha.ipynb b/notebook/experiments.ipynb similarity index 61% rename from notebook/principal_RBM_alpha.ipynb rename to notebook/experiments.ipynb index 96bbc14..c59db62 100644 --- a/notebook/principal_RBM_alpha.ipynb +++ b/notebook/experiments.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Restricted Botlzman Machines (RBM)" + "## Restricted Botlzman Machines (RBM)" ] }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -302,7 +302,7 @@ "35 Z 39" ] }, - "execution_count": 164, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -340,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -389,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -441,31 +441,14 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 41, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(78, 320)\n" - ] - }, { "data": { - 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", 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", - "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -509,25 +492,38 @@ " flattened_images = np.array([image.flatten() for image in char_data.flatten()])\n", " return flattened_images\n", "\n", - "char = [20, \"Z\"]\n", - "data = read_alpha_digit(char, ALPHA_DIGIT_PATH)\n", - "print(data.shape)\n", - "plt.imshow(data[0].reshape(20, 16), cmap=\"gray\")\n", - "plt.show()\n", - "plt.imshow(data[40].reshape(20, 16), cmap=\"gray\")\n", - "plt.show()\n" + "def plot_characters(chars, data):\n", + " num_chars = len(chars)\n", + " num_images_per_char = data.shape[0] // num_chars\n", + " fig, ax = plt.subplots(1, num_chars, figsize=(num_chars * 2, 2))\n", + "\n", + " for i, char in enumerate(chars):\n", + " # Find the index of the first image corresponding to the current char\n", + " start_index = i * num_images_per_char\n", + " image = data[start_index].reshape(20, 16)\n", + " ax[i].imshow(image, cmap='gray')\n", + " ax[i].set_title(f'Char: {char}')\n", + " ax[i].axis('off')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# Example\n", + "chars = [0, \"K\", 7, \"Z\"]\n", + "data = read_alpha_digit(chars, data=data, use_data=True)\n", + "plot_characters(chars, data)" ] }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "data shape: (78, 320)\n" + "data shape: (156, 320)\n" ] } ], @@ -537,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -559,6 +555,9 @@ " self.rng = np.random.default_rng(random_state)\n", " self.W = 1e-4 * self.rng.standard_normal(size=(n_visible, n_hidden))\n", "\n", + " def __repr__(self) -> str:\n", + " return f\"RBM(n_visible={self.n_visible}, n_hidden={self.n_hidden})\"\n", + "\n", " def _sigmoid(self, x: np.ndarray) -> np.ndarray:\n", " \"\"\"\n", " Sigmoid activation function.\n", @@ -584,7 +583,7 @@ " \"\"\"\n", " return np.round(np.power(image - input, 2).mean(), 3)\n", "\n", - " def entree_sortie(self, data: np.ndarray) -> np.ndarray:\n", + " def input_output(self, data: np.ndarray) -> np.ndarray:\n", " \"\"\"\n", " Compute hidden units given visible units.\n", "\n", @@ -596,7 +595,7 @@ " \"\"\"\n", " return self._sigmoid(data @ self.W + self.b)\n", "\n", - " def sortie_entree(self, data_h: np.ndarray) -> np.ndarray:\n", + " def output_input(self, data_h: np.ndarray) -> np.ndarray:\n", " \"\"\"\n", " Compute visible units given hidden units.\n", "\n", @@ -626,9 +625,9 @@ " self.rng.shuffle(data)\n", " for i in tqdm(range(0, n_samples, batch_size), desc=f\"Epoch {epoch}\"):\n", " batch = data[i:i+batch_size]\n", - " pos_h_probs = self.entree_sortie(batch)\n", - " pos_v_probs = self.sortie_entree(pos_h_probs)\n", - " neg_h_probs = self.entree_sortie(pos_v_probs)\n", + " pos_h_probs = self.input_output(batch)\n", + " pos_v_probs = self.output_input(pos_h_probs)\n", + " neg_h_probs = self.input_output(pos_v_probs)\n", " \n", " # Update weights and biases\n", " self.W += learning_rate * (batch.T @ pos_h_probs - pos_v_probs.T @ neg_h_probs) / batch_size\n", @@ -641,7 +640,7 @@ "\n", " return self\n", "\n", - " def generer_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", + " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", " \"\"\"\n", " Generate samples from the RBM using Gibbs sampling.\n", "\n", @@ -663,19 +662,19 @@ " v_probs = self._sigmoid(h @ self.W.T + self.a)\n", " v = self.rng.binomial(1, v_probs)\n", " samples[i] = v\n", - " return samples\n" + " return samples" ] }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 999.83it/s]\n" + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 333.32it/s]\n" ] }, { @@ -689,16 +688,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 1333.64it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 1000.67it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 999.89it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 998.94it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 999.89it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 799.37it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 499.96it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 800.36it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 798.80it/s]\n", - "Epoch 10: 100%|██████████| 4/4 [00:00<00:00, 799.56it/s]\n" + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 499.92it/s]\n", + "Epoch 2: 0%| | 0/4 [00:00 \"DBN\":\n", + " \"\"\"\n", + " Train the DBN using Greedy layer-wise procedure.\n", + "\n", + " Parameters:\n", + " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", + " - n_epochs (int): Number of training epochs. Default is 10.\n", + " - batch_size (int): Size of mini-batches. Default is 10.\n", + " - print_each: Print reconstruction error each `print_each` epochs.\n", + "\n", + " Returns:\n", + " - DBN: Trained DBN instance.\n", + " \"\"\"\n", + " input_data = data\n", + " for rbm in self.rbms:\n", + " rbm.train(\n", + " input_data,\n", + " learning_rate=learning_rate,\n", + " n_epochs=n_epochs,\n", + " batch_size=batch_size,\n", + " print_each=print_each,\n", + " )\n", + " # Update input data for the next RBM\n", + " input_data = rbm.entree_sortie(input_data)\n", + "\n", + " return self\n", + "\n", + " def generate_image(self, n_samples: int = 1, n_gibbs_steps: int = 100) -> np.ndarray:\n", + " \"\"\"\n", + " Generate samples from the DBN using Gibbs sampling.\n", + "\n", + " Parameters:\n", + " - n_samples (int): Number of samples to generate. Default is 1.\n", + " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100.\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", + " \"\"\"\n", + " samples = np.zeros((n_samples, self.n_visible))\n", + "\n", + " # Matrix of initialization value of Gibbs samples for each sample.\n", + " V = self.rng.binomial(1, self.rng.random(), size=n_samples * self.n_visible).reshape((n_samples, self.n_visible))\n", + " for i in range(n_samples):\n", + " for _ in range(n_gibbs_steps):\n", + " for rbm in self.rbms:\n", + " h_probs = rbm.entree_sortie(V[i])\n", + " h = self.rng.binomial(1, h_probs)\n", + " v_probs = rbm.sortie_entree(h)\n", + " V[i] = self.rng.binomial(1, v_probs)\n", + " samples[i] = V[i]\n", + "\n", + " return samples\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "dbn = DBN(5, [6, 20, 20, 20, 20])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DBN(n_visible=5, hidden_layer_sizes=[6, 20, 20, 20], rbms=[RBM(n_visible=5, n_hidden=6), RBM(n_visible=6, n_hidden=20), RBM(n_visible=20, n_hidden=20), RBM(n_visible=20, n_hidden=20)])\n" + ] + } + ], + "source": [ + "print(dbn)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/src/principal_dbn_alpha.py b/src/principal_dbn_alpha.py new file mode 100644 index 0000000..e69de29 From 90e065b50cc49d40f092616e61672b21b0ed681a Mon Sep 17 00:00:00 2001 From: Yedidia AGNIMO Date: Thu, 28 Mar 2024 22:21:40 +0100 Subject: [PATCH 02/16] refactor: normalize method's names. * English names --- notebook/experiments.ipynb | 408 ++++++++++++++++++++++++------------- src/principal_rbm_alpha.py | 26 ++- src/tests/test_rbm.py | 62 ++++++ 3 files changed, 350 insertions(+), 146 deletions(-) create mode 100644 src/tests/test_rbm.py diff --git a/notebook/experiments.ipynb b/notebook/experiments.ipynb index c59db62..479f54a 100644 --- a/notebook/experiments.ipynb +++ b/notebook/experiments.ipynb @@ -12,6 +12,15 @@ "execution_count": 1, "metadata": {}, "outputs": [], + "source": [ + "#FIXME: Review the generation process (theoretically) and fix the implementation " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], "source": [ "import os\n", "from typing import List, Dict, Tuple, Literal, Optional, Union, Iterable\n", @@ -28,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -302,7 +311,7 @@ "35 Z 39" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -340,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -389,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -441,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -510,13 +519,13 @@ "\n", "# Example\n", "chars = [0, \"K\", 7, \"Z\"]\n", - "data = read_alpha_digit(chars, data=data, use_data=True)\n", + "data = read_alpha_digit(chars, data=alphadigit_data, use_data=True)\n", "plot_characters(chars, data)" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -533,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -667,14 +676,14 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 333.32it/s]\n" + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 666.74it/s]\n" ] }, { @@ -688,23 +697,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 499.92it/s]\n", - "Epoch 2: 0%| | 0/4 [00:00 \"DBN\":\n", " \"\"\"\n", " Train the DBN using Greedy layer-wise procedure.\n", @@ -1044,6 +1050,7 @@ " - n_epochs (int): Number of training epochs. Default is 10.\n", " - batch_size (int): Size of mini-batches. Default is 10.\n", " - print_each: Print reconstruction error each `print_each` epochs.\n", + " - verbose\n", "\n", " Returns:\n", " - DBN: Trained DBN instance.\n", @@ -1058,11 +1065,11 @@ " print_each=print_each,\n", " )\n", " # Update input data for the next RBM\n", - " input_data = rbm.entree_sortie(input_data)\n", + " input_data = rbm.input_output(input_data)\n", "\n", " return self\n", "\n", - " def generate_image(self, n_samples: int = 1, n_gibbs_steps: int = 100) -> np.ndarray:\n", + " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=100) -> np.ndarray:\n", " \"\"\"\n", " Generate samples from the DBN using Gibbs sampling.\n", "\n", @@ -1080,9 +1087,9 @@ " for i in range(n_samples):\n", " for _ in range(n_gibbs_steps):\n", " for rbm in self.rbms:\n", - " h_probs = rbm.entree_sortie(V[i])\n", + " h_probs = rbm.input_output(V[i])\n", " h = self.rng.binomial(1, h_probs)\n", - " v_probs = rbm.sortie_entree(h)\n", + " v_probs = rbm.output_input(h)\n", " V[i] = self.rng.binomial(1, v_probs)\n", " samples[i] = V[i]\n", "\n", @@ -1092,23 +1099,133 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 666.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.184.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 666.77it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 108.12it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 363.73it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 571.78it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 666.82it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 800.25it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 800.25it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 1000.25it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1000.43it/s]\n", + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 4004.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.045.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 4000.29it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 3999.34it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 2002.29it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1999.43it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 4007.94it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 3993.62it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 2002.53it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 4001.24it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1998.48it/s]\n", + "Epoch 0: 100%|██████████| 4/4 [00:00 #WARNING there might be something wrong with the function +# --- Other Tags . +# FIXME: This function is returning incorrect results for negative input values. +# BUG: Division by zero error occurs in certain cases. +# HACK: This code temporarily fixes the issue, but needs a proper solution. +# OPTIMIZE: Improve the efficiency of this loop """ import os @@ -169,7 +181,7 @@ def _reconstruction_error( """ return np.round(np.power(output_img - input_img, 2).mean(), 3) - def entree_sortie(self, data: np.ndarray) -> np.ndarray: + def input_output(self, data: np.ndarray) -> np.ndarray: """ Compute hidden units given visible units. @@ -181,7 +193,7 @@ def entree_sortie(self, data: np.ndarray) -> np.ndarray: """ return self._sigmoid(data @ self.W + self.b) - def sortie_entree(self, data_h: np.ndarray) -> np.ndarray: + def output_input(self, data_h: np.ndarray) -> np.ndarray: """ Compute visible units given hidden units. @@ -218,9 +230,9 @@ def train( self.rng.shuffle(data) for i in tqdm(range(0, n_samples, batch_size), desc=f"Epoch {epoch}"): batch = data[i: i + batch_size] - pos_h_probs = self.entree_sortie(batch) - pos_v_probs = self.sortie_entree(pos_h_probs) - neg_h_probs = self.entree_sortie(pos_v_probs) + pos_h_probs = self.input_output(batch) + pos_v_probs = self.output_input(pos_h_probs) + neg_h_probs = self.input_output(pos_v_probs) # Update weights and biases self.W += ( @@ -242,7 +254,7 @@ def train( return self - def generer_image(self, n_samples: int = 1, n_gibbs_steps: int = 1) -> np.ndarray: + def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray: """ Generate samples from the RBM using Gibbs sampling. @@ -260,8 +272,8 @@ def generer_image(self, n_samples: int = 1, n_gibbs_steps: int = 1) -> np.ndarra 1, self.rng.random(), size=n_samples * self.n_visible ).reshape((n_samples, self.n_visible)) for i in range(n_samples): + h_probs = self._sigmoid(V[i] @ self.W + self.b) for _ in range(n_gibbs_steps): - h_probs = self._sigmoid(V[i] @ self.W + self.b) h = self.rng.binomial(1, h_probs) v_probs = self._sigmoid(h @ self.W.T + self.a) v = self.rng.binomial(1, v_probs) diff --git a/src/tests/test_rbm.py b/src/tests/test_rbm.py new file mode 100644 index 0000000..a349913 --- /dev/null +++ b/src/tests/test_rbm.py @@ -0,0 +1,62 @@ +import os +import numpy as np +import scipy.io +import unittest +from rbm import RBM, _load_data, _map_character_to_index, read_alpha_digit + +DATA_FOLDER = "../data/" +ALPHA_DIGIT_PATH = os.path.join(DATA_FOLDER, "binaryalphadigs.mat") + + +class TestRBM(unittest.TestCase): + def setUp(self): + # Load alpha_digit data for testing + self.data = read_alpha_digit(file_path=ALPHA_DIGIT_PATH, character='A') + self.n_samples = self.data.shape[0] + self.n_visible = self.data.shape[1] + self.n_hidden = 100 + self.rbm = RBM(n_visible=self.n_visible, n_hidden=self.n_hidden) + + def test__sigmoid(self): + # Test _sigmoid method with positive and negative values + x = np.array([1, -1, 0]) + sigmoid_x = self.rbm._sigmoid(x) + self.assertTrue(np.allclose(sigmoid_x, [0.73105858, 0.26894142, 0.5])) + + def test__reconstruction_error(self): + # Test _reconstruction_error method with arrays containing zeros + input_img = np.zeros_like(self.data) + output_img = np.zeros_like(self.data) + error = self.rbm._reconstruction_error(input_img, output_img) + self.assertEqual(error, 0.0) + + def test_entree_sortie(self): + # Test entree_sortie method with a small input array + input_data = np.ones((2, self.n_visible)) + output = self.rbm.entree_sortie(input_data) + self.assertEqual(output.shape, (2, self.n_hidden)) + + def test_sortie_entree(self): + # Test sortie_entree method with a small input array + input_data = np.ones((2, self.n_hidden)) + output = self.rbm.sortie_entree(input_data) + self.assertEqual(output.shape, (2, self.n_visible)) + + def test_train(self): + # Test train method + trained_rbm = self.rbm.train( + self.data[:100], learning_rate=0.1, n_epochs=1, batch_size=10 + ) + self.assertTrue(hasattr(trained_rbm, 'W')) + self.assertTrue(hasattr(trained_rbm, 'a')) + self.assertTrue(hasattr(trained_rbm, 'b')) + + def test_generer_image(self): + # Test generer_image method + samples = self.rbm.generer_image(n_samples=2, n_gibbs_steps=10) + self.assertEqual(samples.shape, (2, self.n_visible)) + self.assertTrue(np.all((samples == 0) | (samples == 1))) + + +if __name__ == "__main__": + unittest.main() From f3091b7b10c9484da6559c563e392b702923c97b Mon Sep 17 00:00:00 2001 From: Yedidia AGNIMO Date: Fri, 29 Mar 2024 14:21:37 +0100 Subject: [PATCH 03/16] feat!: Implement Deep Belief Network (DBN) * src/principal_dbn_alpha.py - Initialize DBN as a list of RBMs. - Train RBMs sequentially in forward direction. - Implement image generation using Gibbs sampling between the last two layers (generating images with the last RBM). Recursively sample layer L-1 using conditional probabilities on layer L: h_{i-1} = Bernoulli(output_input(h_i)). --- notebook/experiments.ipynb | 1771 +++++++++++++++++++++++++++--------- src/principal_dbn_alpha.py | 125 +++ src/principal_rbm_alpha.py | 9 +- 3 files changed, 1493 insertions(+), 412 deletions(-) diff --git a/notebook/experiments.ipynb b/notebook/experiments.ipynb index 479f54a..a22fd8d 100644 --- a/notebook/experiments.ipynb +++ b/notebook/experiments.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -311,7 +311,7 @@ "35 Z 39" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -398,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -450,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -525,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -542,18 +542,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "class RBM:\n", - " def __init__(self, n_visible: int, n_hidden: int, random_state=None) -> None:\n", + " def __init__(self, n_visible: int, n_hidden: int=100, random_state=None) -> None:\n", " \"\"\"\n", " Initialize the Restricted Boltzmann Machine.\n", "\n", " Parameters:\n", " - n_visible (int): Number of visible units.\n", - " - n_hidden (int): Number of hidden units.\n", + " - n_hidden (int): Number of hidden units. Default 100.\n", " - random_state: Random seed for reproducibility.\n", " \"\"\"\n", " self.n_visible = n_visible\n", @@ -590,7 +590,7 @@ " Returns:\n", " - float: Reconstruction error.\n", " \"\"\"\n", - " return np.round(np.power(image - input, 2).mean(), 3)\n", + " return np.round(np.power(image - input, 2).mean(), 5)\n", "\n", " def input_output(self, data: np.ndarray) -> np.ndarray:\n", " \"\"\"\n", @@ -616,7 +616,13 @@ " \"\"\"\n", " return self._sigmoid(data_h @ self.W.T + self.a)\n", "\n", - " def train(self, data: np.ndarray, learning_rate: float=0.1, n_epochs: int=10, batch_size: int=10, print_each=10) -> 'RBM':\n", + " def train(self, \n", + " data: np.ndarray,\n", + " learning_rate: float=0.1,\n", + " n_epochs: int=10,\n", + " batch_size: int=10,\n", + " print_each=10\n", + " ) -> 'RBM':\n", " \"\"\"\n", " Train the RBM using Contrastive Divergence.\n", "\n", @@ -654,8 +660,8 @@ " Generate samples from the RBM using Gibbs sampling.\n", "\n", " Parameters:\n", - " - n_samples (int): Number of samples to generate. Default is 1.\n", - " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100.\n", + " - n_samples (int): Number of samples to generate. Default is 10.\n", + " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 1.\n", "\n", " Returns:\n", " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", @@ -663,10 +669,10 @@ " samples = np.zeros((n_samples, self.n_visible))\n", " \n", " # Matrix of initlization value of Gibbs samples for each sample. \n", - " V = self.rng.binomial(1, self.rng.random(), size=n_samples * self.n_visible).reshape((n_samples, self.n_visible))\n", + " V = self.rng.binomial(1, self.rng.random(), size=n_samples*self.n_visible).reshape((n_samples, self.n_visible))\n", " for i in range(n_samples):\n", " for _ in range(n_gibbs_steps):\n", - " h_probs = self._sigmoid(V[i] @ self.W + self.b)\n", + " h_probs = self._sigmoid(V[i] @ self.W + self.b) # vector\n", " h = self.rng.binomial(1, h_probs)\n", " v_probs = self._sigmoid(h @ self.W.T + self.a)\n", " v = self.rng.binomial(1, v_probs)\n", @@ -676,90 +682,107 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the alpha_digit data\n", + "data = read_alpha_digit(file_path=ALPHA_DIGIT_PATH, characters=['Z'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RBM(n_visible=320, n_hidden=200)\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 666.74it/s]\n" + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 666.77it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.163.\n" + "Reconstruction error: 0.16569.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 666.74it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 85.11it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 63.49it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 235.31it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 571.02it/s]\n", - "Epoch 6: 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"Reconstruction error: 0.141.\n" + "Reconstruction error: 0.13308.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 11: 100%|██████████| 4/4 [00:00<00:00, 804.32it/s]\n", - "Epoch 12: 100%|██████████| 4/4 [00:00<00:00, 830.10it/s]\n", - "Epoch 13: 100%|██████████| 4/4 [00:00<00:00, 861.65it/s]\n", - "Epoch 14: 100%|██████████| 4/4 [00:00<00:00, 798.95it/s]\n", - "Epoch 15: 100%|██████████| 4/4 [00:00<00:00, 800.36it/s]\n", - "Epoch 16: 100%|██████████| 4/4 [00:00<00:00, 800.78it/s]\n", - "Epoch 17: 100%|██████████| 4/4 [00:00<00:00, 800.44it/s]\n", - "Epoch 18: 100%|██████████| 4/4 [00:00<00:00, 800.33it/s]\n", - "Epoch 19: 100%|██████████| 4/4 [00:00<00:00, 799.91it/s]\n", - "Epoch 20: 100%|██████████| 4/4 [00:00<00:00, 772.47it/s]\n" + "Epoch 11: 100%|██████████| 4/4 [00:00<00:00, 571.53it/s]\n", + "Epoch 12: 100%|██████████| 4/4 [00:00<00:00, 500.01it/s]\n", + "Epoch 13: 100%|██████████| 4/4 [00:00<00:00, 799.98it/s]\n", + "Epoch 14: 100%|██████████| 4/4 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800.02it/s]\n" ] }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Load the alpha_digit data\n", - "data = read_alpha_digit(file_path=ALPHA_DIGIT_PATH, characters='Z')\n", - "\n", - "# Initialize RBM\n", - "n_visible = data.shape[1] # Number of visible units (size of each image)\n", - "n_hidden = 100 # Number of hidden units (hyperparameter)\n", - "rbm = RBM(n_visible=n_visible, n_hidden=n_hidden, random_state=42)\n", - "repr(rbm)\n", - "\n", - "# Train RBM\n", - "rbm.train(data, learning_rate=0.1, n_epochs=100, batch_size=10)\n", - "\n", - "# Generate samples\n", - "generated_samples = rbm.generate_image(n_samples=10, n_gibbs_steps=1)\n", - "\n", - "# Plot original and generated samples\n", - "plt.figure(figsize=(12, 6))\n", - "for i in range(10):\n", - " plt.subplot(2, 10, i + 1)\n", - " plt.imshow(data[i].reshape(20, 16), cmap='gray')\n", - " plt.title('Original')\n", - " plt.axis('off')\n", - " \n", - " plt.subplot(2, 10, i + 11)\n", - " plt.imshow(generated_samples[i].reshape(20, 16), cmap='gray')\n", - " plt.title('Generated')\n", - " plt.axis('off')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RBM(n_visible=320, n_hidden=100)\n" + "Reconstruction error: 0.01225.\n" ] - } - ], - "source": [ - "print(rbm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2 Implementing a Deep Belief Network (DBN) and test on Binary AlphaDigits" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "class DBN:\n", - " def __init__(self, n_visible: int, hidden_layer_sizes: list[int], random_state=None):\n", - " \"\"\"\n", - " Initialize the Deep Belief Network.\n", - "\n", - " Parameters:\n", - " - n_visible (int): Number of visible units.\n", - " - hidden_layer_sizes (list[int]): List of sizes for each hidden layer.\n", - " - random_state: Random seed for reproducibility.\n", - " \"\"\"\n", - " self.n_visible = n_visible\n", - " self.hidden_layer_sizes = hidden_layer_sizes\n", - " self.rbms = []\n", - " self.rng = np.random.default_rng(random_state)\n", - "\n", - " # Initialize the first RBM\n", - " first_rbm = RBM(\n", - " n_visible=n_visible,\n", - " n_hidden=hidden_layer_sizes[0],\n", - " random_state=random_state,\n", - " )\n", - " self.rbms.append(first_rbm)\n", - "\n", - " # Initialize RBMs for subsequent hidden layers\n", - " for i, size in enumerate(hidden_layer_sizes[1:], start=1):\n", - " rbm = RBM(\n", - " n_visible=hidden_layer_sizes[i - 1],\n", - " n_hidden=size,\n", - " random_state=random_state,\n", - " )\n", - " self.rbms.append(rbm)\n", - "\n", - "\n", - " def __getitem__(self, key):\n", - " return self.rbms[key]\n", - " \n", - "\n", - " def __repr__(self):\n", - " \"\"\"\n", - " Return a string representation of the DBN object.\n", - " \"\"\"\n", - " rbm_reprs = [repr(rbm) for rbm in self.rbms]\n", - " join_rbm_reprs = ',\\n '.join(rbm_reprs)\n", - " return f\"DBN([\\n {join_rbm_reprs}\\n])\"\n", - "\n", - "\n", - " def train(self,\n", - " data: np.ndarray,\n", - " learning_rate: float=0.1,\n", - " n_epochs: int=10,\n", - " batch_size: int=10,\n", - " print_each: int=10,\n", - " ) -> \"DBN\":\n", - " \"\"\"\n", - " Train the DBN using Greedy layer-wise procedure.\n", - "\n", - " Parameters:\n", - " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", - " - n_epochs (int): Number of training epochs. Default is 10.\n", - " - batch_size (int): Size of mini-batches. Default is 10.\n", - " - print_each: Print reconstruction error each `print_each` epochs.\n", - " - verbose\n", - "\n", - " Returns:\n", - " - DBN: Trained DBN instance.\n", - " \"\"\"\n", - " input_data = data\n", - " for rbm in self.rbms:\n", - " rbm.train(\n", - " input_data,\n", - " learning_rate=learning_rate,\n", - " n_epochs=n_epochs,\n", - " batch_size=batch_size,\n", - " print_each=print_each,\n", - " )\n", - " # Update input data for the next RBM\n", - " input_data = rbm.input_output(input_data)\n", - "\n", - " return self\n", - "\n", - " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=100) -> np.ndarray:\n", - " \"\"\"\n", - " Generate samples from the DBN using Gibbs sampling.\n", - "\n", - " Parameters:\n", - " - n_samples (int): Number of samples to generate. Default is 1.\n", - " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100.\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", - " \"\"\"\n", - " samples = np.zeros((n_samples, self.n_visible))\n", - "\n", - " # Matrix of initialization value of Gibbs samples for each sample.\n", - " V = self.rng.binomial(1, self.rng.random(), size=n_samples * self.n_visible).reshape((n_samples, self.n_visible))\n", - " for i in range(n_samples):\n", - " for _ in range(n_gibbs_steps):\n", - " for rbm in self.rbms:\n", - " h_probs = rbm.input_output(V[i])\n", - " h = self.rng.binomial(1, h_probs)\n", - " v_probs = rbm.output_input(h)\n", - " V[i] = self.rng.binomial(1, v_probs)\n", - " samples[i] = V[i]\n", - "\n", - " return samples\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ + }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 666.66it/s]\n" + "Epoch 101: 100%|██████████| 4/4 [00:00<00:00, 800.17it/s]\n", + "Epoch 102: 100%|██████████| 4/4 [00:00<00:00, 1000.79it/s]\n", + "Epoch 103: 100%|██████████| 4/4 [00:00<00:00, 800.25it/s]\n", + "Epoch 104: 100%|██████████| 4/4 [00:00<00:00, 800.02it/s]\n", + "Epoch 105: 100%|██████████| 4/4 [00:00<00:00, 800.36it/s]\n", + "Epoch 106: 100%|██████████| 4/4 [00:00<00:00, 799.94it/s]\n", + "Epoch 107: 100%|██████████| 4/4 [00:00<00:00, 800.13it/s]\n", + "Epoch 108: 100%|██████████| 4/4 [00:00<00:00, 800.17it/s]\n", + "Epoch 109: 100%|██████████| 4/4 [00:00<00:00, 800.13it/s]\n", + "Epoch 110: 100%|██████████| 4/4 [00:00<00:00, 1000.19it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.184.\n" + "Reconstruction error: 0.00928.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 666.77it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 108.12it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 363.73it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 571.78it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 666.82it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 800.25it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 800.25it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 1000.25it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1000.43it/s]\n", - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 4004.11it/s]\n" + "Epoch 111: 100%|██████████| 4/4 [00:00<00:00, 666.66it/s]\n", + "Epoch 112: 100%|██████████| 4/4 [00:00<00:00, 1000.73it/s]\n", + "Epoch 113: 100%|██████████| 4/4 [00:00<00:00, 666.74it/s]\n", + "Epoch 114: 100%|██████████| 4/4 [00:00<00:00, 800.13it/s]\n", + "Epoch 115: 100%|██████████| 4/4 [00:00<00:00, 800.06it/s]\n", + "Epoch 116: 100%|██████████| 4/4 [00:00<00:00, 800.02it/s]\n", + "Epoch 117: 100%|██████████| 4/4 [00:00<00:00, 666.71it/s]\n", + "Epoch 118: 100%|██████████| 4/4 [00:00<00:00, 799.71it/s]\n", + "Epoch 119: 100%|██████████| 4/4 [00:00<00:00, 571.57it/s]\n", + "Epoch 120: 100%|██████████| 4/4 [00:00<00:00, 666.85it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.045.\n" + "Reconstruction error: 0.00509.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 4000.29it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 3999.34it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 2002.29it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1999.43it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 4007.94it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 3993.62it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 2002.53it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 4001.24it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1998.48it/s]\n", - "Epoch 0: 100%|██████████| 4/4 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate samples\n", + "generated_samples = rbm.generate_image(n_samples=10, n_gibbs_steps=1)\n", + "\n", + "# Plot original and generated samples\n", + "plt.figure(figsize=(12, 6))\n", + "for i in range(10):\n", + " plt.subplot(2, 10, i + 1)\n", + " plt.imshow(data[i].reshape(20, 16), cmap='gray')\n", + " plt.title('Original')\n", + " plt.axis('off')\n", + " \n", + " plt.subplot(2, 10, i + 11)\n", + " plt.imshow(generated_samples[i].reshape(20, 16), cmap='gray')\n", + " plt.title('Generated')\n", + " plt.axis('off')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RBM(n_visible=320, n_hidden=200)\n" + ] + } + ], + "source": [ + "print(rbm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Implementing a Deep Belief Network (DBN) and test on Binary AlphaDigits" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "class DBN:\n", + " def __init__(self, n_visible: int, hidden_layer_sizes: list[int], random_state=None):\n", + " \"\"\"\n", + " Initialize the Deep Belief Network.\n", + "\n", + " Parameters:\n", + " - n_visible (int): Number of visible units.\n", + " - hidden_layer_sizes (list[int]): List of sizes for each hidden layer.\n", + " - random_state: Random seed for reproducibility.\n", + " \"\"\"\n", + " self.n_visible = n_visible\n", + " self.hidden_layer_sizes = hidden_layer_sizes\n", + " self.rbms: List[RBM] = []\n", + " self.rng = np.random.default_rng(random_state)\n", + "\n", + " # Initialize the first RBM\n", + " first_rbm = RBM(\n", + " n_visible=n_visible,\n", + " n_hidden=hidden_layer_sizes[0],\n", + " random_state=random_state,\n", + " )\n", + " self.rbms.append(first_rbm)\n", + "\n", + " # Initialize RBMs for subsequent hidden layers\n", + " for i, size in enumerate(hidden_layer_sizes[1:], start=1):\n", + " rbm = RBM(\n", + " n_visible=hidden_layer_sizes[i - 1],\n", + " n_hidden=size,\n", + " random_state=random_state,\n", + " )\n", + " self.rbms.append(rbm)\n", + "\n", + "\n", + " def __getitem__(self, key):\n", + " return self.rbms[key]\n", + " \n", + "\n", + " def __repr__(self):\n", + " \"\"\"\n", + " Return a string representation of the DBN object.\n", + " \"\"\"\n", + " rbm_reprs = [repr(rbm) for rbm in self.rbms]\n", + " join_rbm_reprs = ',\\n '.join(rbm_reprs)\n", + " return f\"DBN([\\n {join_rbm_reprs}\\n])\"\n", + "\n", + "\n", + " def train(self,\n", + " data: np.ndarray,\n", + " learning_rate: float=0.1,\n", + " n_epochs: int=10,\n", + " batch_size: int=10,\n", + " print_each: int=10,\n", + " ) -> \"DBN\":\n", + " \"\"\"\n", + " Train the DBN using Greedy layer-wise procedure.\n", + "\n", + " Parameters:\n", + " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", + " - n_epochs (int): Number of training epochs. Default is 10.\n", + " - batch_size (int): Size of mini-batches. Default is 10.\n", + " - print_each: Print reconstruction error each `print_each` epochs.\n", + " - verbose\n", + "\n", + " Returns:\n", + " - DBN: Trained DBN instance.\n", + " \"\"\"\n", + " input_data = data\n", + " for rbm in self.rbms:\n", + " rbm.train(\n", + " input_data,\n", + " learning_rate=learning_rate,\n", + " n_epochs=n_epochs,\n", + " batch_size=batch_size,\n", + " print_each=print_each,\n", + " )\n", + " # Update input data for the next RBM\n", + " input_data = rbm.input_output(input_data)\n", + "\n", + " return self\n", + "\n", + " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", + " \"\"\"\n", + " Generate samples from the DBN using Gibbs sampling.\n", + "\n", + " Parameters:\n", + " - n_samples (int): Number of samples to generate. Default is 1.\n", + " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100.\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", + " \"\"\"\n", + " # samples = np.zeros((n_samples, self.n_visible))\n", + "\n", + " # Generate samples using the first RBM in the DBN\n", + " samples = self.rbms[-1].generate_image(n_samples, n_gibbs_steps)\n", + " for rbm in reversed(self.rbms[:-1]):\n", + " # Sample from the conditional probability of layer l-1 given layer l: p(h_{s-1}|h_{s}).\n", + " h_probs = rbm.output_input(samples)\n", + " h = self.rng.binomial(1, p=h_probs) \n", + " samples = h\n", + " return samples" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 1000.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.18102.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 999.77it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 666.34it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 1000.13it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 800.63it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 999.77it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 666.85it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 799.75it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 800.44it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 800.17it/s]\n", + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 3993.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.01524.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 2000.86it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 1999.91it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 2000.14it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1998.95it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 3997.43it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 3988.88it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 1331.31it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 4016.57it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 4012.73it/s]\n", + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 1999.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.00416.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 2001.58it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 4011.77it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 4000.29it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 4003.15it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 4003.15it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 4000.29it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 3998.38it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 4004.11it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 4003.15it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "DBN([\n", + " RBM(n_visible=320, n_hidden=100),\n", + " RBM(n_visible=100, n_hidden=50),\n", + " RBM(n_visible=50, n_hidden=25)\n", + "])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_visible=data.shape[1]\n", + "hidden_layer_sizes = [100, 50, 25]\n", + "\n", + "dbn = DBN(n_visible=n_visible, hidden_layer_sizes=hidden_layer_sizes, random_state=42)\n", + "dbn.train(data, learning_rate=0.1, n_epochs=10, batch_size=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[RBM(n_visible=100, n_hidden=50), RBM(n_visible=50, n_hidden=25)]\n" + ] + } + ], + "source": [ + "# Check if the RBM are accessibles via a slicing \n", + "print(dbn[1:3])" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "dbn[2]" + "# # Generate images\n", + "generated_images = dbn.generate_image(n_samples=5, n_gibbs_steps=1)\n", + "\n", + "# Display generated images\n", + "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(12, 4))\n", + "for i in range(5):\n", + " axes[i].imshow(generated_images[i].reshape(20, 16), cmap='gray')\n", + " axes[i].set_title(f\"Image {i+1}\")\n", + " axes[i].axis('off')\n", + "plt.tight_layout()\n", + "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src/principal_dbn_alpha.py b/src/principal_dbn_alpha.py index e69de29..69bcb20 100644 --- a/src/principal_dbn_alpha.py +++ b/src/principal_dbn_alpha.py @@ -0,0 +1,125 @@ +""" +Module: dbn.py +Module providing implementation of Deep Belief Network (DBN). +""" + +from typing import List + +import numpy as np +from principal_rbm_alpha import RBM + + +class DBN: + """ + Implementation of a Deep Belief Network (DBN). + + Attributes: + - n_visible (int): Number of visible units. + - hidden_layer_sizes (List[int]): List of sizes for each hidden layer. + - rbms (List[RBM]): List of Restricted Boltzmann Machines (RBMs) forming the DBN. + - rng (numpy.random.Generator): Random number generator for sampling. + """ + + def __init__( + self, + n_visible: int, + hidden_layer_sizes: List[int], + random_state=None + ): + """ + Initialize the Deep Belief Network. + + Parameters: + - n_visible (int): Number of visible units. + - hidden_layer_sizes (List[int]): List of sizes for each hidden layer. + - random_state: Random seed for reproducibility. + """ + self.n_visible = n_visible + self.hidden_layer_sizes = hidden_layer_sizes + self.rbms: List[RBM] = [] + self.rng = np.random.default_rng(random_state) + + # Initialize the first RBM + first_rbm = RBM( + n_visible=n_visible, + n_hidden=hidden_layer_sizes[0], + random_state=random_state, + ) + self.rbms.append(first_rbm) + + # Initialize RBMs for subsequent hidden layers + for i, size in enumerate(hidden_layer_sizes[1:], start=1): + rbm = RBM( + n_visible=hidden_layer_sizes[i - 1], + n_hidden=size, + random_state=random_state, + ) + self.rbms.append(rbm) + + def __getitem__(self, key): + return self.rbms[key] + + def __repr__(self): + """ + Return a string representation of the DBN object. + """ + rbm_reprs = [repr(rbm) for rbm in self.rbms] + join_rbm_reprs = ",\n ".join(rbm_reprs) + return f"DBN([\n {join_rbm_reprs}\n])" + + def train( + self, + data: np.ndarray, + learning_rate: float = 0.1, + n_epochs: int = 10, + batch_size: int = 10, + print_each: int = 10, + ) -> "DBN": + """ + Train the DBN using Greedy layer-wise procedure. + + Parameters: + - data (numpy.ndarray): Input data, shape (n_samples, n_visible). + - learning_rate (float): Learning rate for gradient descent. Default is 0.1. + - n_epochs (int): Number of training epochs. Default is 10. + - batch_size (int): Size of mini-batches. Default is 10. + - print_each: Print reconstruction error each `print_each` epochs. + + Returns: + - DBN: Trained DBN instance. + """ + input_data = data + for rbm in self.rbms: + rbm.train( + input_data, + learning_rate=learning_rate, + n_epochs=n_epochs, + batch_size=batch_size, + print_each=print_each, + ) + # Update input data for the next RBM + input_data = rbm.input_output(input_data) + + return self + + def generate_image(self, n_samples: int = 1, n_gibbs_steps: int = 1) -> np.ndarray: + """ + Generate samples from the DBN using Gibbs sampling. + + Parameters: + - n_samples (int): Number of samples to generate. Default is 1. + - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100. + + Returns: + - numpy.ndarray: Generated samples, shape (n_samples, n_visible). + """ + # samples = np.zeros((n_samples, self.n_visible)) + + # Generate samples using the first RBM in the DBN + samples = self.rbms[-1].generate_image(n_samples, n_gibbs_steps) + for rbm in reversed(self.rbms[:-1]): + # Sample from the conditional probability of layer l-1 given layer l: p(h_{s-1}|h_{s}). + h_probs = rbm.output_input(samples) + h = self.rng.binomial(1, p=h_probs) + samples = h + return samples diff --git a/src/principal_rbm_alpha.py b/src/principal_rbm_alpha.py index e7dc818..cff9ef3 100644 --- a/src/principal_rbm_alpha.py +++ b/src/principal_rbm_alpha.py @@ -6,11 +6,10 @@ #TODO: check relevance of using the RBM's RNG for generation phase (look inside the gibbs sampling). # If a seed has been define, the gibbs sampling step will return the same sample for each it will # sample the same h from the binomial -> #WARNING there might be something wrong with the function -# --- Other Tags . -# FIXME: This function is returning incorrect results for negative input values. -# BUG: Division by zero error occurs in certain cases. -# HACK: This code temporarily fixes the issue, but needs a proper solution. -# OPTIMIZE: Improve the efficiency of this loop +# --------------------------- Other Tags (Example usage) ---------------------. +# FIXME: Example: This function is returning incorrect results for negative input values. +# BUG: Example: Division by zero error occurs in certain cases. +# HACK: Example: This code temporarily fixes the issue, but needs a proper solution. """ import os From 362c81164f462594df552af3aca8b9eab305b655 Mon Sep 17 00:00:00 2001 From: Yedidia AGNIMO Date: Fri, 29 Mar 2024 14:53:47 +0100 Subject: [PATCH 04/16] chores: setup package configuration --- setup.py | 12 ++++++++++++ src/__init__.py | 0 2 files changed, 12 insertions(+) create mode 100644 setup.py create mode 100644 src/__init__.py diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..d57a7ef --- /dev/null +++ b/setup.py @@ -0,0 +1,12 @@ +from setuptools import setup, find_packages + +setup( + name='generative_model', + version='0.1', + author='Yedidia AGNIMO & C. Yann Éric CHOHO', + author_email='yedidia.agnimo@ensae.fr // chohoyanneric.choho@ensae.fr', + description='Implement basics deep learning architecture for generative models.', + packages=find_packages(where='src'), + package_dir={'': 'src'}, + python_requires='>=3.10', +) diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000..e69de29 From c50df1d25e1b6b03dd0623f7e244495ebac28bfb Mon Sep 17 00:00:00 2001 From: Yedidia AGNIMO Date: Mon, 18 Mar 2024 10:39:42 +0100 Subject: [PATCH 05/16] feat: start working on Deep Belief Network (DBN). * Rename experiments notebook. * Start working on DBN in the notebook. --- ...ipal_RBM_alpha.ipynb => experiments.ipynb} | 459 ++++++++++++------ src/principal_dbn_alpha.py | 0 2 files changed, 313 insertions(+), 146 deletions(-) rename notebook/{principal_RBM_alpha.ipynb => experiments.ipynb} (61%) create mode 100644 src/principal_dbn_alpha.py diff --git a/notebook/principal_RBM_alpha.ipynb b/notebook/experiments.ipynb similarity index 61% rename from notebook/principal_RBM_alpha.ipynb rename to notebook/experiments.ipynb index 96bbc14..c59db62 100644 --- a/notebook/principal_RBM_alpha.ipynb +++ b/notebook/experiments.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Restricted Botlzman Machines (RBM)" + "## Restricted Botlzman Machines (RBM)" ] }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -302,7 +302,7 @@ "35 Z 39" ] }, - "execution_count": 164, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -340,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -389,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 177, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -441,31 +441,14 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 41, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(78, 320)\n" - ] - }, { "data": { - "image/png": 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", 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Vr1+/sMY4nU4lJiYGNACIVREN6pEjRyolJUUVFRX+fT6fT/v27VN2dnbQMfHx8Zo8eXLAmM7OTlVUVIQcAwDXlXA/fWxubjY1NTWmpqbGSDLvvPOOqampMQ0NDcYYY9544w2TlJRkNm3aZL755hsze/ZsM3LkSPPjjz/6z/Hggw+aFStW+LfXrl1rnE6nKSsrM99995155plnTFJSkvF4PFdVE9/6oEl864Nmd+vOtz7CfnVv3749aBGFhYXGmH9/RW/p0qUmOTnZOJ1OM23aNHPkyJGAc6Snp5vi4uKAfStWrDDDhw838fHxJjMz01RVVV11TQQ1TSKoaXa37gS14z8vgD7N5/PJ5XL1dhnoZTHwUu4Sz6Pu27xe7zV/ntYnv/UBANcTVnhBr4vlK2GughEJXFEDgOUIagCwHEENAJYjqAHAcgQ1AFiOoAYAyxHUAGA5ghoALEdQA4DlCGoAsBxBDQCWI6gBwHIENQBYjqAGAMsR1ABgOYIaACxHUAOA5QhqALAcS3FdZ2J52atIYgkt2IQragCwHEENAJYjqAHAcgQ1AFiOoAYAyxHUAGA5ghoALEdQA4DlCGoAsBxBDQCWI6gBwHIENQBYjqAGAMsR1ABgubCDeteuXZo1a5bcbrccDofKy8v9xy5evKjFixdrwoQJGjhwoNxut5588kmdPHmyy3MuW7ZMDocjoI0bNy7sXwYAYlHYQd3a2qqMjAyVlpZeduz8+fOqrq7W0qVLVV1drU8//VRHjhzRww8/fMXzjh8/XqdOnfK33bt3h1saAMSksBcOyMvLU15eXtBjLpdLW7duDdj33nvvKTMzU42NjRo+fHjoQvr3V0pKSrjlAEDMi/oKL16vVw6HQ0lJSV32O3r0qNxutxISEpSdna2SkpKQwd7W1qa2tjb/ts/ni2TJ1mFVlqvDqiyIVVH9MPHChQtavHixCgoKlJiYGLJfVlaWysrKtGXLFq1cuVL19fWaOnWqmpubg/YvKSmRy+Xyt7S0tGj9CgDQ+0w3SDIbN24Meqy9vd3MmjXLTJo0yXi93rDO+8MPP5jExETz17/+NejxCxcuGK/X62/Hjx83kmK24er09t8TjdZVCzcH/1dUbn1cvHhRc+bMUUNDg7788ssur6aDSUpK0pgxY1RXVxf0uNPplNPpjESpAGC9iN/6uBTSR48e1bZt23TzzTeHfY6WlhYdO3ZMqampkS4PAPqcsIO6paVFtbW1qq2tlSTV19ertrZWjY2Nunjxon7zm9/o66+/1t/+9jd1dHTI4/HI4/Govb3df45p06bpvffe82+/+OKL2rlzp77//nvt3btXjzzyiPr166eCgoLu/4YA0NeFe69k+/btQe+/FBYWmvr6+pD3Z7Zv3+4/R3p6uikuLvZvz50716Smppr4+Hhzyy23mLlz55q6urqrrsnr9fb6/adoNlyd3v57otG6at25R+34zwu8T/P5fHK5XL1dRtTEwF9Rj+DrebCZ1+sN+/O6S3jWBwBYjqAGAMsR1ABgOYIaACxHUAOA5QhqALAcQQ0AliOoAcByBDUAWI6gBgDLEdQAYLmoL8V1PeMZHVeHZ3QAXeOKGgAsR1ADgOUIagCwHEENAJYjqAHAcgQ1AFiOoAYAyxHUAGA5ghoALEdQA4DlCGoAsBxBDQCWI6gBwHIENQBYjqAGAMsR1ABgOYIaACzHCi/XGVZTAfoerqgBwHIENQBYjqAGAMsR1ABgOYIaACwXdlDv2rVLs2bNktvtlsPhUHl5ecDxefPmyeFwBLQZM2Zc8bylpaUaMWKEEhISlJWVpf3794dbGgDEpLCDurW1VRkZGSotLQ3ZZ8aMGTp16pS/rVmzpstzrlu3TosWLVJxcbGqq6uVkZGh3NxcnT59OtzyACD2mG6QZDZu3Biwr7Cw0MyePTus82RmZpqioiL/dkdHh3G73aakpOSqxnu9XiPJumaj3p4TGu16bV6v95rft1G5R71jxw4NHTpUY8eO1cKFC3X27NmQfdvb23XgwAHl5OT498XFxSknJ0eVlZVBx7S1tcnn8wU0AIhVEQ/qGTNm6OOPP1ZFRYXefPNN7dy5U3l5eero6Aja/8yZM+ro6FBycnLA/uTkZHk8nqBjSkpK5HK5/C0tLS3SvwYAWCPi/4X8scce8/95woQJmjhxokaPHq0dO3Zo2rRpEfkZS5Ys0aJFi/zbPp+PsAYQs6L+9bxRo0Zp8ODBqqurC3p88ODB6tevn5qamgL2NzU1KSUlJegYp9OpxMTEgAYAsSrqQX3ixAmdPXtWqampQY/Hx8dr8uTJqqio8O/r7OxURUWFsrOzo10eAFgv7KBuaWlRbW2tamtrJUn19fWqra1VY2OjWlpa9Ic//EFVVVX6/vvvVVFRodmzZ+vWW29Vbm6u/xzTpk3Te++9599etGiR/vKXv+ijjz7S4cOHtXDhQrW2tup3v/td939DAOjrwv2ayPbt24N+9aSwsNCcP3/eTJ8+3QwZMsQMGDDApKenm6efftp4PJ6Ac6Snp5vi4uKAfStWrDDDhw838fHxJjMz01RVVV11TXw97+r19pzQaNdr687X8xz/efP2aT6fTy6Xq7fLuIyNU8vzqIHe4fV6r/nzNJ71AQCWI6gBwHIsxfUTNt6uAHB944oaACxHUAOA5QhqALAcQQ0AliOoAcByBDUAWI6gBgDLEdQAYDmCGgAsR1ADgOUIagCwHEENAJYjqAHAcgQ1AFiOoAYAyxHUAGA5ghoALBdTK7x0Z/FIm7EgLSKJVYx6ViQW3+aKGgAsR1ADgOUIagCwHEENAJYjqAHAcgQ1AFiOoAYAyxHUAGA5ghoALEdQA4DlCGoAsBxBDQCWI6gBwHIENQBYLuyg3rVrl2bNmiW32y2Hw6Hy8vKA4w6HI2h7++23Q55z2bJll/UfN25c2L8MAMSisIO6tbVVGRkZKi0tDXr81KlTAe3DDz+Uw+HQr3/96y7PO378+IBxu3fvDrc0AIhJYS8ckJeXp7y8vJDHU1JSArY3bdqkBx54QKNGjeq6kP79LxsLAIjyPeqmpiZ9/vnnmj9//hX7Hj16VG63W6NGjdLjjz+uxsbGkH3b2trk8/kCGgDEqqguxfXRRx9p0KBB+tWvftVlv6ysLJWVlWns2LE6deqUXn31VU2dOlWHDh3SoEGDLutfUlKiV199NVplW4elk4Drm8N0IwUcDoc2btyo/Pz8oMfHjRunhx56SCtWrAjrvOfOnVN6erreeeedoFfjbW1tamtr82/7fD6lpaXF7JqJAPquS2smdiefonZF/c9//lNHjhzRunXrwh6blJSkMWPGqK6uLuhxp9Mpp9PZ3RIBoE+I2j3qDz74QJMnT1ZGRkbYY1taWnTs2DGlpqZGoTIA6FvCDuqWlhbV1taqtrZWklRfX6/a2tqAD/98Pp82bNigBQsWBD3HtGnT9N577/m3X3zxRe3cuVPff/+99u7dq0ceeUT9+vVTQUFBuOUBQMwJ+9bH119/rQceeMC/vWjRIklSYWGhysrKJElr166VMSZk0B47dkxnzpzxb584cUIFBQU6e/ashgwZovvuu09VVVUaMmRIuOUBQMzp1oeJtojEzXoAiIZI5BPP+gAAyxHUAGA5ghoALEdQA4DlCGoAsBxBDQCWI6gBwHIENQBYjqAGAMsR1ABgOYIaACxHUAOA5QhqALAcQQ0AliOoAcByBDUAWI6gBgDLEdQAYDmCGgAsR1ADgOUIagCwHEENAJYjqAHAcgQ1AFiOoAYAyxHUAGA5ghoALEdQA4DlCGoAsFz/3i4gEowxkiSfz9fLlQBAoEu5dCmnrkVMBHVzc7MkKS0trZcrAYDgmpub5XK5rmmsw3Qn5i3R2dmpkydPatCgQXI4HCH7+Xw+paWl6fjx40pMTOzBCruHuntWX61b6ru1x3Ldxhg1NzfL7XYrLu7a7jbHxBV1XFychg0bdtX9ExMT+9SL4RLq7ll9tW6p79Yeq3Vf65X0JXyYCACWI6gBwHLXVVA7nU4VFxfL6XT2dilhoe6e1Vfrlvpu7dTdtZj4MBEAYtl1dUUNAH0RQQ0AliOoAcByBDUAWC7mgrq0tFQjRoxQQkKCsrKytH///i77b9iwQePGjVNCQoImTJigzZs391Cl/1ZSUqK7775bgwYN0tChQ5Wfn68jR450OaasrEwOhyOgJSQk9FDF/7Zs2bLLahg3blyXY3p7riVpxIgRl9XtcDhUVFQUtH9vzvWuXbs0a9Ysud1uORwOlZeXBxw3xuiVV15RamqqbrjhBuXk5Ojo0aNXPG+475FI1n3x4kUtXrxYEyZM0MCBA+V2u/Xkk0/q5MmTXZ7zWl5vkaxbkubNm3dZDTNmzLjieSMx3zEV1OvWrdOiRYtUXFys6upqZWRkKDc3V6dPnw7af+/evSooKND8+fNVU1Oj/Px85efn69ChQz1W886dO1VUVKSqqipt3bpVFy9e1PTp09Xa2trluMTERJ06dcrfGhoaeqji/xo/fnxADbt37w7Z14a5lqSvvvoqoOatW7dKkh599NGQY3prrltbW5WRkaHS0tKgx9966y29++67ev/997Vv3z4NHDhQubm5unDhQshzhvseiXTd58+fV3V1tZYuXarq6mp9+umnOnLkiB5++OErnjec11uk675kxowZATWsWbOmy3NGbL5NDMnMzDRFRUX+7Y6ODuN2u01JSUnQ/nPmzDEzZ84M2JeVlWWeffbZqNbZldOnTxtJZufOnSH7rFq1yrhcrp4rKoji4mKTkZFx1f1tnGtjjPn9739vRo8ebTo7O4Met2GujTFGktm4caN/u7Oz06SkpJi3337bv+/cuXPG6XSaNWvWhDxPuO+RSNcdzP79+40k09DQELJPuK+37gpWd2FhoZk9e3ZY54nUfMfMFXV7e7sOHDignJwc/764uDjl5OSosrIy6JjKysqA/pKUm5sbsn9P8Hq9kqSbbrqpy34tLS1KT09XWlqaZs+erW+//bYnygtw9OhRud1ujRo1So8//rgaGxtD9rVxrtvb27V69Wo99dRTXT7My4a5/qn6+np5PJ6AOXW5XMrKygo5p9fyHukJXq9XDodDSUlJXfYL5/UWLTt27NDQoUM1duxYLVy4UGfPng3ZN5LzHTNBfebMGXV0dCg5OTlgf3JysjweT9AxHo8nrP7R1tnZqeeff1733nuv7rzzzpD9xo4dqw8//FCbNm3S6tWr1dnZqSlTpujEiRM9VmtWVpbKysq0ZcsWrVy5UvX19Zo6dar/kbM/ZdtcS1J5ebnOnTunefPmhexjw1wHc2newpnTa3mPRNuFCxe0ePFiFRQUdPlQo3Bfb9EwY8YMffzxx6qoqNCbb76pnTt3Ki8vTx0dHUH7R3K+Y+LpebGiqKhIhw4duuK9t+zsbGVnZ/u3p0yZottvv11//vOf9frrr0e7TElSXl6e/88TJ05UVlaW0tPTtX79es2fP79HauiuDz74QHl5eXK73SH72DDXserixYuaM2eOjDFauXJll31teL099thj/j9PmDBBEydO1OjRo7Vjxw5NmzYtqj87Zq6oBw8erH79+qmpqSlgf1NTk1JSUoKOSUlJCat/ND333HP67LPPtH379rAe2SpJAwYM0KRJk1RXVxel6q4sKSlJY8aMCVmDTXMtSQ0NDdq2bZsWLFgQ1jgb5lqSf97CmdNreY9Ey6WQbmho0NatW8N+tOmVXm89YdSoURo8eHDIGiI53zET1PHx8Zo8ebIqKir8+zo7O1VRURFwRfS/srOzA/pL0tatW0P2jwZjjJ577jlt3LhRX375pUaOHBn2OTo6OnTw4EGlpqZGocKr09LSomPHjoWswYa5/l+rVq3S0KFDNXPmzLDG2TDXkjRy5EilpKQEzKnP59O+fftCzum1vEei4VJIHz16VNu2bdPNN98c9jmu9HrrCSdOnNDZs2dD1hDR+Q7ro0fLrV271jidTlNWVma+++4788wzz5ikpCTj8XiMMcY88cQT5qWXXvL337Nnj+nfv79Zvny5OXz4sCkuLjYDBgwwBw8e7LGaFy5caFwul9mxY4c5deqUv50/f97f56d1v/rqq+aLL74wx44dMwcOHDCPPfaYSUhIMN9++22P1f3CCy+YHTt2mPr6erNnzx6Tk5NjBg8ebE6fPh20Zhvm+pKOjg4zfPhws3jx4suO2TTXzc3NpqamxtTU1BhJ5p133jE1NTX+b0e88cYbJikpyWzatMl88803Zvbs2WbkyJHmxx9/9J/jwQcfNCtWrPBvX+k9Eu2629vbzcMPP2yGDRtmamtrA17zbW1tIeu+0ust2nU3NzebF1980VRWVpr6+nqzbds2c9ddd5nbbrvNXLhwIWTdkZrvmApqY4xZsWKFGT58uImPjzeZmZmmqqrKf+z+++83hYWFAf3Xr19vxowZY+Lj48348ePN559/3qP1SgraVq1aFbLu559/3v87Jicnm1/84hemurq6R+ueO3euSU1NNfHx8eaWW24xc+fONXV1dSFrNqb35/qSL774wkgyR44cueyYTXO9ffv2oK+NS/V1dnaapUuXmuTkZON0Os20adMu+53S09NNcXFxwL6u3iPRrru+vj7ka3779u0h677S6y3adZ8/f95Mnz7dDBkyxAwYMMCkp6ebp59++rLAjdZ885hTALBczNyjBoBYRVADgOUIagCwHEENAJYjqAHAcgQ1AFiOoAYAyxHUAGA5ghoALEdQA4DlCGoAsBxBDQCW+38oKWAqVJcDrgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -509,25 +492,38 @@ " flattened_images = np.array([image.flatten() for image in char_data.flatten()])\n", " return flattened_images\n", "\n", - "char = [20, \"Z\"]\n", - "data = read_alpha_digit(char, ALPHA_DIGIT_PATH)\n", - "print(data.shape)\n", - "plt.imshow(data[0].reshape(20, 16), cmap=\"gray\")\n", - "plt.show()\n", - "plt.imshow(data[40].reshape(20, 16), cmap=\"gray\")\n", - "plt.show()\n" + "def plot_characters(chars, data):\n", + " num_chars = len(chars)\n", + " num_images_per_char = data.shape[0] // num_chars\n", + " fig, ax = plt.subplots(1, num_chars, figsize=(num_chars * 2, 2))\n", + "\n", + " for i, char in enumerate(chars):\n", + " # Find the index of the first image corresponding to the current char\n", + " start_index = i * num_images_per_char\n", + " image = data[start_index].reshape(20, 16)\n", + " ax[i].imshow(image, cmap='gray')\n", + " ax[i].set_title(f'Char: {char}')\n", + " ax[i].axis('off')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# Example\n", + "chars = [0, \"K\", 7, \"Z\"]\n", + "data = read_alpha_digit(chars, data=data, use_data=True)\n", + "plot_characters(chars, data)" ] }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "data shape: (78, 320)\n" + "data shape: (156, 320)\n" ] } ], @@ -537,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -559,6 +555,9 @@ " self.rng = np.random.default_rng(random_state)\n", " self.W = 1e-4 * self.rng.standard_normal(size=(n_visible, n_hidden))\n", "\n", + " def __repr__(self) -> str:\n", + " return f\"RBM(n_visible={self.n_visible}, n_hidden={self.n_hidden})\"\n", + "\n", " def _sigmoid(self, x: np.ndarray) -> np.ndarray:\n", " \"\"\"\n", " Sigmoid activation function.\n", @@ -584,7 +583,7 @@ " \"\"\"\n", " return np.round(np.power(image - input, 2).mean(), 3)\n", "\n", - " def entree_sortie(self, data: np.ndarray) -> np.ndarray:\n", + " def input_output(self, data: np.ndarray) -> np.ndarray:\n", " \"\"\"\n", " Compute hidden units given visible units.\n", "\n", @@ -596,7 +595,7 @@ " \"\"\"\n", " return self._sigmoid(data @ self.W + self.b)\n", "\n", - " def sortie_entree(self, data_h: np.ndarray) -> np.ndarray:\n", + " def output_input(self, data_h: np.ndarray) -> np.ndarray:\n", " \"\"\"\n", " Compute visible units given hidden units.\n", "\n", @@ -626,9 +625,9 @@ " self.rng.shuffle(data)\n", " for i in tqdm(range(0, n_samples, batch_size), desc=f\"Epoch {epoch}\"):\n", " batch = data[i:i+batch_size]\n", - " pos_h_probs = self.entree_sortie(batch)\n", - " pos_v_probs = self.sortie_entree(pos_h_probs)\n", - " neg_h_probs = self.entree_sortie(pos_v_probs)\n", + " pos_h_probs = self.input_output(batch)\n", + " pos_v_probs = self.output_input(pos_h_probs)\n", + " neg_h_probs = self.input_output(pos_v_probs)\n", " \n", " # Update weights and biases\n", " self.W += learning_rate * (batch.T @ pos_h_probs - pos_v_probs.T @ neg_h_probs) / batch_size\n", @@ -641,7 +640,7 @@ "\n", " return self\n", "\n", - " def generer_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", + " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", " \"\"\"\n", " Generate samples from the RBM using Gibbs sampling.\n", "\n", @@ -663,19 +662,19 @@ " v_probs = self._sigmoid(h @ self.W.T + self.a)\n", " v = self.rng.binomial(1, v_probs)\n", " samples[i] = v\n", - " return samples\n" + " return samples" ] }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 999.83it/s]\n" + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 333.32it/s]\n" ] }, { @@ -689,16 +688,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 1333.64it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 1000.67it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 999.89it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 998.94it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 999.89it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 799.37it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 499.96it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 800.36it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 798.80it/s]\n", - "Epoch 10: 100%|██████████| 4/4 [00:00<00:00, 799.56it/s]\n" + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 499.92it/s]\n", + "Epoch 2: 0%| | 0/4 [00:00 \"DBN\":\n", + " \"\"\"\n", + " Train the DBN using Greedy layer-wise procedure.\n", + "\n", + " Parameters:\n", + " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", + " - n_epochs (int): Number of training epochs. Default is 10.\n", + " - batch_size (int): Size of mini-batches. Default is 10.\n", + " - print_each: Print reconstruction error each `print_each` epochs.\n", + "\n", + " Returns:\n", + " - DBN: Trained DBN instance.\n", + " \"\"\"\n", + " input_data = data\n", + " for rbm in self.rbms:\n", + " rbm.train(\n", + " input_data,\n", + " learning_rate=learning_rate,\n", + " n_epochs=n_epochs,\n", + " batch_size=batch_size,\n", + " print_each=print_each,\n", + " )\n", + " # Update input data for the next RBM\n", + " input_data = rbm.entree_sortie(input_data)\n", + "\n", + " return self\n", + "\n", + " def generate_image(self, n_samples: int = 1, n_gibbs_steps: int = 100) -> np.ndarray:\n", + " \"\"\"\n", + " Generate samples from the DBN using Gibbs sampling.\n", + "\n", + " Parameters:\n", + " - n_samples (int): Number of samples to generate. Default is 1.\n", + " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100.\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", + " \"\"\"\n", + " samples = np.zeros((n_samples, self.n_visible))\n", + "\n", + " # Matrix of initialization value of Gibbs samples for each sample.\n", + " V = self.rng.binomial(1, self.rng.random(), size=n_samples * self.n_visible).reshape((n_samples, self.n_visible))\n", + " for i in range(n_samples):\n", + " for _ in range(n_gibbs_steps):\n", + " for rbm in self.rbms:\n", + " h_probs = rbm.entree_sortie(V[i])\n", + " h = self.rng.binomial(1, h_probs)\n", + " v_probs = rbm.sortie_entree(h)\n", + " V[i] = self.rng.binomial(1, v_probs)\n", + " samples[i] = V[i]\n", + "\n", + " return samples\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "dbn = DBN(5, [6, 20, 20, 20, 20])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DBN(n_visible=5, hidden_layer_sizes=[6, 20, 20, 20], rbms=[RBM(n_visible=5, n_hidden=6), RBM(n_visible=6, n_hidden=20), RBM(n_visible=20, n_hidden=20), RBM(n_visible=20, n_hidden=20)])\n" + ] + } + ], + "source": [ + "print(dbn)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/src/principal_dbn_alpha.py b/src/principal_dbn_alpha.py new file mode 100644 index 0000000..e69de29 From 4efc8a469f2ce8f432f3becef3b04e2759a5c3a5 Mon Sep 17 00:00:00 2001 From: Yedidia AGNIMO Date: Thu, 28 Mar 2024 22:21:40 +0100 Subject: [PATCH 06/16] refactor: normalize method's names. * English names --- notebook/experiments.ipynb | 408 ++++++++++++++++++++++++------------- src/principal_rbm_alpha.py | 26 ++- src/tests/test_rbm.py | 62 ++++++ 3 files changed, 350 insertions(+), 146 deletions(-) create mode 100644 src/tests/test_rbm.py diff --git a/notebook/experiments.ipynb b/notebook/experiments.ipynb index c59db62..479f54a 100644 --- a/notebook/experiments.ipynb +++ b/notebook/experiments.ipynb @@ -12,6 +12,15 @@ "execution_count": 1, "metadata": {}, "outputs": [], + "source": [ + "#FIXME: Review the generation process (theoretically) and fix the implementation " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], "source": [ "import os\n", "from typing import List, Dict, Tuple, Literal, Optional, Union, Iterable\n", @@ -28,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -302,7 +311,7 @@ "35 Z 39" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -340,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -389,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -441,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -510,13 +519,13 @@ "\n", "# Example\n", "chars = [0, \"K\", 7, \"Z\"]\n", - "data = read_alpha_digit(chars, data=data, use_data=True)\n", + "data = read_alpha_digit(chars, data=alphadigit_data, use_data=True)\n", "plot_characters(chars, data)" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -533,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -667,14 +676,14 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 333.32it/s]\n" + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 666.74it/s]\n" ] }, { @@ -688,23 +697,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 499.92it/s]\n", - "Epoch 2: 0%| | 0/4 [00:00 \"DBN\":\n", " \"\"\"\n", " Train the DBN using Greedy layer-wise procedure.\n", @@ -1044,6 +1050,7 @@ " - n_epochs (int): Number of training epochs. Default is 10.\n", " - batch_size (int): Size of mini-batches. Default is 10.\n", " - print_each: Print reconstruction error each `print_each` epochs.\n", + " - verbose\n", "\n", " Returns:\n", " - DBN: Trained DBN instance.\n", @@ -1058,11 +1065,11 @@ " print_each=print_each,\n", " )\n", " # Update input data for the next RBM\n", - " input_data = rbm.entree_sortie(input_data)\n", + " input_data = rbm.input_output(input_data)\n", "\n", " return self\n", "\n", - " def generate_image(self, n_samples: int = 1, n_gibbs_steps: int = 100) -> np.ndarray:\n", + " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=100) -> np.ndarray:\n", " \"\"\"\n", " Generate samples from the DBN using Gibbs sampling.\n", "\n", @@ -1080,9 +1087,9 @@ " for i in range(n_samples):\n", " for _ in range(n_gibbs_steps):\n", " for rbm in self.rbms:\n", - " h_probs = rbm.entree_sortie(V[i])\n", + " h_probs = rbm.input_output(V[i])\n", " h = self.rng.binomial(1, h_probs)\n", - " v_probs = rbm.sortie_entree(h)\n", + " v_probs = rbm.output_input(h)\n", " V[i] = self.rng.binomial(1, v_probs)\n", " samples[i] = V[i]\n", "\n", @@ -1092,23 +1099,133 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 666.66it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.184.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 666.77it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 108.12it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 363.73it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 571.78it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 666.82it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 800.25it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 800.25it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 1000.25it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1000.43it/s]\n", + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 4004.11it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.045.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 4000.29it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 3999.34it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 2002.29it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1999.43it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 4007.94it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 3993.62it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 2002.53it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 4001.24it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1998.48it/s]\n", + "Epoch 0: 100%|██████████| 4/4 [00:00 #WARNING there might be something wrong with the function +# --- Other Tags . +# FIXME: This function is returning incorrect results for negative input values. +# BUG: Division by zero error occurs in certain cases. +# HACK: This code temporarily fixes the issue, but needs a proper solution. +# OPTIMIZE: Improve the efficiency of this loop """ import os @@ -169,7 +181,7 @@ def _reconstruction_error( """ return np.round(np.power(output_img - input_img, 2).mean(), 3) - def entree_sortie(self, data: np.ndarray) -> np.ndarray: + def input_output(self, data: np.ndarray) -> np.ndarray: """ Compute hidden units given visible units. @@ -181,7 +193,7 @@ def entree_sortie(self, data: np.ndarray) -> np.ndarray: """ return self._sigmoid(data @ self.W + self.b) - def sortie_entree(self, data_h: np.ndarray) -> np.ndarray: + def output_input(self, data_h: np.ndarray) -> np.ndarray: """ Compute visible units given hidden units. @@ -218,9 +230,9 @@ def train( self.rng.shuffle(data) for i in tqdm(range(0, n_samples, batch_size), desc=f"Epoch {epoch}"): batch = data[i: i + batch_size] - pos_h_probs = self.entree_sortie(batch) - pos_v_probs = self.sortie_entree(pos_h_probs) - neg_h_probs = self.entree_sortie(pos_v_probs) + pos_h_probs = self.input_output(batch) + pos_v_probs = self.output_input(pos_h_probs) + neg_h_probs = self.input_output(pos_v_probs) # Update weights and biases self.W += ( @@ -242,7 +254,7 @@ def train( return self - def generer_image(self, n_samples: int = 1, n_gibbs_steps: int = 1) -> np.ndarray: + def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray: """ Generate samples from the RBM using Gibbs sampling. @@ -260,8 +272,8 @@ def generer_image(self, n_samples: int = 1, n_gibbs_steps: int = 1) -> np.ndarra 1, self.rng.random(), size=n_samples * self.n_visible ).reshape((n_samples, self.n_visible)) for i in range(n_samples): + h_probs = self._sigmoid(V[i] @ self.W + self.b) for _ in range(n_gibbs_steps): - h_probs = self._sigmoid(V[i] @ self.W + self.b) h = self.rng.binomial(1, h_probs) v_probs = self._sigmoid(h @ self.W.T + self.a) v = self.rng.binomial(1, v_probs) diff --git a/src/tests/test_rbm.py b/src/tests/test_rbm.py new file mode 100644 index 0000000..a349913 --- /dev/null +++ b/src/tests/test_rbm.py @@ -0,0 +1,62 @@ +import os +import numpy as np +import scipy.io +import unittest +from rbm import RBM, _load_data, _map_character_to_index, read_alpha_digit + +DATA_FOLDER = "../data/" +ALPHA_DIGIT_PATH = os.path.join(DATA_FOLDER, "binaryalphadigs.mat") + + +class TestRBM(unittest.TestCase): + def setUp(self): + # Load alpha_digit data for testing + self.data = read_alpha_digit(file_path=ALPHA_DIGIT_PATH, character='A') + self.n_samples = self.data.shape[0] + self.n_visible = self.data.shape[1] + self.n_hidden = 100 + self.rbm = RBM(n_visible=self.n_visible, n_hidden=self.n_hidden) + + def test__sigmoid(self): + # Test _sigmoid method with positive and negative values + x = np.array([1, -1, 0]) + sigmoid_x = self.rbm._sigmoid(x) + self.assertTrue(np.allclose(sigmoid_x, [0.73105858, 0.26894142, 0.5])) + + def test__reconstruction_error(self): + # Test _reconstruction_error method with arrays containing zeros + input_img = np.zeros_like(self.data) + output_img = np.zeros_like(self.data) + error = self.rbm._reconstruction_error(input_img, output_img) + self.assertEqual(error, 0.0) + + def test_entree_sortie(self): + # Test entree_sortie method with a small input array + input_data = np.ones((2, self.n_visible)) + output = self.rbm.entree_sortie(input_data) + self.assertEqual(output.shape, (2, self.n_hidden)) + + def test_sortie_entree(self): + # Test sortie_entree method with a small input array + input_data = np.ones((2, self.n_hidden)) + output = self.rbm.sortie_entree(input_data) + self.assertEqual(output.shape, (2, self.n_visible)) + + def test_train(self): + # Test train method + trained_rbm = self.rbm.train( + self.data[:100], learning_rate=0.1, n_epochs=1, batch_size=10 + ) + self.assertTrue(hasattr(trained_rbm, 'W')) + self.assertTrue(hasattr(trained_rbm, 'a')) + self.assertTrue(hasattr(trained_rbm, 'b')) + + def test_generer_image(self): + # Test generer_image method + samples = self.rbm.generer_image(n_samples=2, n_gibbs_steps=10) + self.assertEqual(samples.shape, (2, self.n_visible)) + self.assertTrue(np.all((samples == 0) | (samples == 1))) + + +if __name__ == "__main__": + unittest.main() From 001a6dbb3103ef7318a5f1236adff4a6a24974c4 Mon Sep 17 00:00:00 2001 From: Yedidia AGNIMO Date: Fri, 29 Mar 2024 14:21:37 +0100 Subject: [PATCH 07/16] feat!: Implement Deep Belief Network (DBN) * src/principal_dbn_alpha.py - Initialize DBN as a list of RBMs. - Train RBMs sequentially in forward direction. - Implement image generation using Gibbs sampling between the last two layers (generating images with the last RBM). Recursively sample layer L-1 using conditional probabilities on layer L: h_{i-1} = Bernoulli(output_input(h_i)). --- notebook/experiments.ipynb | 1771 +++++++++++++++++++++++++++--------- src/principal_dbn_alpha.py | 125 +++ src/principal_rbm_alpha.py | 9 +- 3 files changed, 1493 insertions(+), 412 deletions(-) diff --git a/notebook/experiments.ipynb b/notebook/experiments.ipynb index 479f54a..a22fd8d 100644 --- a/notebook/experiments.ipynb +++ b/notebook/experiments.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -311,7 +311,7 @@ "35 Z 39" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -398,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -450,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -525,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -542,18 +542,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "class RBM:\n", - " def __init__(self, n_visible: int, n_hidden: int, random_state=None) -> None:\n", + " def __init__(self, n_visible: int, n_hidden: int=100, random_state=None) -> None:\n", " \"\"\"\n", " Initialize the Restricted Boltzmann Machine.\n", "\n", " Parameters:\n", " - n_visible (int): Number of visible units.\n", - " - n_hidden (int): Number of hidden units.\n", + " - n_hidden (int): Number of hidden units. Default 100.\n", " - random_state: Random seed for reproducibility.\n", " \"\"\"\n", " self.n_visible = n_visible\n", @@ -590,7 +590,7 @@ " Returns:\n", " - float: Reconstruction error.\n", " \"\"\"\n", - " return np.round(np.power(image - input, 2).mean(), 3)\n", + " return np.round(np.power(image - input, 2).mean(), 5)\n", "\n", " def input_output(self, data: np.ndarray) -> np.ndarray:\n", " \"\"\"\n", @@ -616,7 +616,13 @@ " \"\"\"\n", " return self._sigmoid(data_h @ self.W.T + self.a)\n", "\n", - " def train(self, data: np.ndarray, learning_rate: float=0.1, n_epochs: int=10, batch_size: int=10, print_each=10) -> 'RBM':\n", + " def train(self, \n", + " data: np.ndarray,\n", + " learning_rate: float=0.1,\n", + " n_epochs: int=10,\n", + " batch_size: int=10,\n", + " print_each=10\n", + " ) -> 'RBM':\n", " \"\"\"\n", " Train the RBM using Contrastive Divergence.\n", "\n", @@ -654,8 +660,8 @@ " Generate samples from the RBM using Gibbs sampling.\n", "\n", " Parameters:\n", - " - n_samples (int): Number of samples to generate. Default is 1.\n", - " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100.\n", + " - n_samples (int): Number of samples to generate. Default is 10.\n", + " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 1.\n", "\n", " Returns:\n", " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", @@ -663,10 +669,10 @@ " samples = np.zeros((n_samples, self.n_visible))\n", " \n", " # Matrix of initlization value of Gibbs samples for each sample. \n", - " V = self.rng.binomial(1, self.rng.random(), size=n_samples * self.n_visible).reshape((n_samples, self.n_visible))\n", + " V = self.rng.binomial(1, self.rng.random(), size=n_samples*self.n_visible).reshape((n_samples, self.n_visible))\n", " for i in range(n_samples):\n", " for _ in range(n_gibbs_steps):\n", - " h_probs = self._sigmoid(V[i] @ self.W + self.b)\n", + " h_probs = self._sigmoid(V[i] @ self.W + self.b) # vector\n", " h = self.rng.binomial(1, h_probs)\n", " v_probs = self._sigmoid(h @ self.W.T + self.a)\n", " v = self.rng.binomial(1, v_probs)\n", @@ -676,90 +682,107 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the alpha_digit data\n", + "data = read_alpha_digit(file_path=ALPHA_DIGIT_PATH, characters=['Z'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RBM(n_visible=320, n_hidden=200)\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 666.74it/s]\n" + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 666.77it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.163.\n" + "Reconstruction error: 0.16569.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 666.74it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 85.11it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 63.49it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 235.31it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 571.02it/s]\n", - "Epoch 6: 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"Reconstruction error: 0.141.\n" + "Reconstruction error: 0.13308.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 11: 100%|██████████| 4/4 [00:00<00:00, 804.32it/s]\n", - "Epoch 12: 100%|██████████| 4/4 [00:00<00:00, 830.10it/s]\n", - "Epoch 13: 100%|██████████| 4/4 [00:00<00:00, 861.65it/s]\n", - "Epoch 14: 100%|██████████| 4/4 [00:00<00:00, 798.95it/s]\n", - "Epoch 15: 100%|██████████| 4/4 [00:00<00:00, 800.36it/s]\n", - "Epoch 16: 100%|██████████| 4/4 [00:00<00:00, 800.78it/s]\n", - "Epoch 17: 100%|██████████| 4/4 [00:00<00:00, 800.44it/s]\n", - "Epoch 18: 100%|██████████| 4/4 [00:00<00:00, 800.33it/s]\n", - "Epoch 19: 100%|██████████| 4/4 [00:00<00:00, 799.91it/s]\n", - "Epoch 20: 100%|██████████| 4/4 [00:00<00:00, 772.47it/s]\n" + "Epoch 11: 100%|██████████| 4/4 [00:00<00:00, 571.53it/s]\n", + "Epoch 12: 100%|██████████| 4/4 [00:00<00:00, 500.01it/s]\n", + "Epoch 13: 100%|██████████| 4/4 [00:00<00:00, 799.98it/s]\n", + "Epoch 14: 100%|██████████| 4/4 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800.02it/s]\n" ] }, - { - "data": { - "image/png": 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cAAAAFM6VXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4il8AAACKp/gFAACgeIpfAAAAiqf4zWj58uWxcePG3JvRd+Sej+zzkX0ecs9H9nnIPR/Z5yH3fKaT/bSL361bt8Z73vOeeMELXhBz586NuXPnxlFHHRXvfve74+67757uYlvnlltuiYsuuij3ZoySez6yz0f2ecg9H9nnIfd8ZJ+H3PORfR6zpvOi733ve/GXf/mXMWvWrDjjjDPiRS96UcyYMSN++ctfxk033RSf/exnY+vWrbFs2bK6t7dxt9xyS1x11VWtGDS55yP7fGSfh9zzkX0ecs9H9nnIPR/Z5zPl4ve+++6L0047LZYtWxb/8i//EkuWLBn380svvTQ+85nPxIwZ7fyL6scffzzmzZuXezOmTO75yD4f2ech93xkn4fc85F9HnLPR/aZpSk6++yzU0Sk//zP/+z4Nf/zP/+T3vSmN6VFixalwcHBtGbNmvQP//AP455z7bXXpohId9xxR3rf+96XFi9enObOnZve+MY3ph07dkxY5i233JKOPfbYNHfu3DR//vx00kknpXvuuWfcczZs2JDmzZuXtmzZkl73utel+fPnpze84Q0ppZR++MMfplNOOSU973nPSwcccEB67nOfm84///z0hz/8YdzrI2LCfyP27NmTPv3pT6ejjjoqDQ4OpoMPPjidffbZadeuXeO2Y3h4OH3yk59MS5cuTXPmzEnHH398uueee9KyZcvShg0bOspQ7nlyT0n2su+/7OVuzvdb9nI35/ste7mb8/2YfUopTbn4PfTQQ9MRRxzR8fPvueeetHDhwnTUUUelSy+9NF155ZXpuOOOSwMDA+mmm24afd7IgL34xS9Or3rVq9IVV1yR3v/+96eZM2emU089ddwyr7vuujQwMJBe+9rXpiuuuCJdeumlafny5ek5z3lO2rp16+jzNmzYkAYHB9OKFSvShg0b0uc+97l03XXXpZRSOu+889JJJ52ULr744rRp06Z05plnppkzZ6ZTTjll9PX//u//nk488cQUEekrX/nK6H8j3vGOd6RZs2als846K33uc59LH/rQh9K8efPSS1/60vSnP/1p9Hkf+chHUkSkk046KV155ZXp7W9/ezr00EPT4sWLOx4wuefJPSXZy77/spe7Od9v2cvdnO+37OVuzvdj9ilNsfh95JFHUkSkN77xjRN+9vDDD6cHH3xw9L+Rqv/Vr351Wr16dfrjH/84+tzh4eF09NFHp5UrV44+NjJga9euTcPDw6OPv+9970szZ85Mu3fvTiml9Nhjj6XnPOc56ayzzhq3/v/7v/9LCxcuHPf4yG8bPvzhD0/Y3n1/KzHikksuSQMDA+n+++8ffezd7373uN9QjPjRj36UIiJdf/314x7//ve/P+7xHTt2pAMOOCCdfPLJ4/brwgsvTBHR0YDJfUyTuack+33JfkzJ2ct9jDk/puTs5T7GnB9TcvZyH2POjyk9+xFT+mPyRx99NCIi5s+fP+Fnxx9/fAwNDY3+d9VVV8WuXbviX//1X+PUU0+Nxx57LHbu3Bk7d+6Mhx56KNatWxebN2+O7du3j1vO2WefHQMDA6P/fsUrXhF79uyJ+++/PyIibrvttti9e3ecfvrpo8vbuXNnzJw5M17+8pfH7bffPmHb3vnOd054bM6cOaP///jjj8fOnTvj6KOPjpRS3Hnnnc+axY033hgLFy6ME088cdx2rFmzJubPnz+6HT/4wQ/iT3/6U5x33nnj9uv8889/1nWMkPuYJnOPkP2+ZN8f2ct9jDnfH9nLfYw53x/Zy32MOd8/2Y+YUsOrgw46KCIifv/730/42aZNm+Kxxx6LBx54IN7ylrdERMSWLVsipRQf/ehH46Mf/eiky9yxY0csXbp09N/Pf/7zx/180aJFERHx8MMPR0TE5s2bIyLiVa961aTLW7Bgwbh/z5o1K5773OdOeN5vfvOb+Lu/+7v4x3/8x9Flj3jkkUcmXfa+Nm/eHI888kgcfPDBk/58x44dERGjE23lypXjfj40NDS6b89G7mOazD1C9vuS/UQlZi/3Meb8RCVmL/cx5vxEJWYv9zHm/ESlZj9iSsXvwoULY8mSJXHPPfdM+NnLX/7yiIjYtm3b6GPDw8MREXHBBRfEunXrJl3mEUccMe7fM2fOnPR5KaVxy/zKV74ShxxyyITnzZo1fpcGBwcndEvbs2dPnHjiibFr16740Ic+FKtWrYp58+bF9u3bY+PGjaPreCbDw8Nx8MEHx/XXXz/pz4eGhp51GZ2S+5gmc4+Q/b5k3x/Zy32MOd8f2ct9jDnfH9nLfYw53z/Zj5jyVx2dfPLJcc0118R//dd/xcte9rJnfO7hhx8eERGzZ8+OtWvXTm8L97NixYqIiDj44IOnvcyf//zn8atf/Sq+/OUvx9ve9rbRx2+77bYJz9338vr+2/GDH/wgjjnmmHGX/fc38v1cmzdvHs0jIuLBBx+c8FuSZyL3se1oMvcI2e+7HbKful7MXu5j22HOT10vZi/3se0w56euF7OX+9h2mPNT16vZR0RM+QukPvjBD8bcuXPj7W9/ezzwwAMTfj7yW4WIvaEef/zxsWnTpvjf//3fCc998MEHp7r6WLduXSxYsCAuvvjiePLJJ6e1zJHfiOy7rSmluPzyyyc8d+R7rHbv3j3u8VNPPTX27NkTn/zkJye85qmnnhp9/tq1a2P27NlxxRVXjFvfZZdd9qzbuS+579V07hGyHyH7/sle7nuZ8/2Tvdz3Muf7J3u572XO91f2EdO48rty5cq44YYb4vTTT48jjzwyzjjjjHjRi14UKaXYunVr3HDDDTFjxozRvw2/6qqr4thjj43Vq1fHWWedFYcffng88MAD8R//8R/x29/+Nu66664prX/BggXx2c9+Nt761rfGS17ykjjttNNiaGgofvOb38TNN98cxxxzTFx55ZXPuIxVq1bFihUr4oILLojt27fHggUL4lvf+takvz1Ys2ZNRES8973vjXXr1sXMmTPjtNNOi1e+8pVxzjnnxCWXXBI/+9nP4jWveU3Mnj07Nm/eHDfeeGNcfvnlccopp8TQ0FBccMEFcckll8T69evjpJNOijvvvDNuvfXWWLx4ccf7Lfc8uUfIXvb9l73czfl+y17u5ny/ZS93c74fs4+ISfpOd2jLli3pne98ZzriiCPSgQcemObMmZNWrVqVzj333PSzn/1s3HPvu+++9La3vS0dcsghafbs2Wnp0qVp/fr16Zvf/Oboc0bac//4xz8e99rbb789RUS6/fbbJzy+bt26tHDhwnTggQemFStWpI0bN6af/OQno88Z+WLmydx7771p7dq1af78+Wnx4sXprLPOSnfddVeKiHTttdeOPu+pp55K5513XhoaGkoDAwMTWnVfffXVac2aNWnOnDnpoIMOSqtXr04f/OAH0+9+97vR5+zZsyd9/OMfT0uWLKn8xcxy36vp3FOS/QjZ90/2ct/LnO+f7OW+lznfP9nLfS9zvn+yH0hpn+vHAAAAUKAp3/MLAAAAvUbxCwAAQPEUvwAAABRP8QsAAEDxFL8AAAAUT/ELAABA8WZVefHAwEBHz5vs25Q6fW2dOv1Wpyr71eny6v6GqU7XkSP3Nqmae5ty3n+9da8z1/HSqVzr7USO8e+Wus9fVbJp8zfzNXGer6ot5/8mzpl1z9EmxrKT9VZ5D2riuM15Xqpbt99jq8j1GbLK8tqsiWPo6VRZT7dzznWu7vb5xpVfAAAAiqf4BQAAoHiKXwAAAIqn+AUAAKB4A6ltHTkK1EQjgbY0P+iFpi9V9Nr+tblBF53L1eSmKd3e7l7NpW7dPh+0qZFjP45vN7Wt4VVbyKU9jEU+db+3dPs878ovAAAAxVP8AgAAUDzFLwAAAMVT/AIAAFC8Sg2v2tx4Cdm1bf/b3Iyh7iYEnb62bm0b825qKvcqmfbaeDTRtKMb21P3a9uihH2oav8M2rT/xqdd739NKHV/c87lXjuO6n5fqkLDKwAAAHgail8AAACKp/gFAACgeIpfAAAAild7w6vJ5GiC1aZtq3u9vdZorE1j0Y0mAr3WrKBTbWlMUFWOJkZVsmv7uaHfVJk/ORte9ZNc74mdKmHMcjY6avN7kWO0fm1u8tYNpTYRm0xbjmVXfgEAACie4hcAAIDiKX4BAAAonuIXAACA4s3KteK6m9B08rxcjWSauEm97hvmq4xFyeqet1V00uStibnXxP63ae51e1tyNliqe3sm0+k2Tnf/mjgXtk2OhiklN2mpsm+TvbbOc0auPJt6n2viXD/d8a3yebTtx0Gu99jJcmnLZ5vJ5NyWupvudbtu6lSOzzeu/AIAAFA8xS8AAADFU/wCAABQPMUvAAAAxcvW8GoydTZWaaIhSyk6zb2TTNvUKKukJgTd3pd+OzbqHrPpLr9Knr06FjnmWluOs6lqoinXdI+Ffms405bGhrm0qelgU/pxn+tSpRnc/q+tcv7uhfN8Fd1uutdEk9ccx5krvwAAABRP8QsAAEDxFL8AAAAUT/ELAABA8RppeJXjRui6b6Au5Qb5TnNpS+5V1tu2McvRlKbKstqWX1t0eyzqbibRjXHstSYYdZ+XmjrPVTmv5XgPzNWEponzf1uaH7VpTuSUowFZmxp6tl2O98kmlte2z0X9VOvUOQ9c+QUAAKB4il8AAACKp/gFAACgeIpfAAAAitdIw6u2NAmosh11N5lo803ldWti/JtoqlJ13d3e5yrrrHsu99P87lSuedKUtjRYqqIXztW9Oj+Ynv3Hu8p8bMtnsTrk+AzRRAM/nysnqruhW6k5NaWJhpfd5sovAAAAxVP8AgAAUDzFLwAAAMVT/AIAAFC8Rhpe1X1zdJ03q1dplNTmm7lz6XRs6h7/nM0fmpgHnexL3Q3dqqyjlIYSdR73Vc4rJZ1/StqXHJqYM9NtNtbE+b/T5ZWik8aGjqm96j42Osm+7m2bTL+N5XT3ty3NR7u1zBxN3kptaOrKLwAAAMVT/AIAAFA8xS8AAADFU/wCAABQvEYaXk2myo3bORrs9FvDgemq0lCl0zGs0iSibTfqV2nQkKPxW0nZ7y9H05h+bF7T5iYvvZB9E03suq3uTHvxfDOZ6TY2ZK9uN01romlV28433ZajqWSV5TWliQaAnawjV5O3bnPlFwAAgOIpfgEAACie4hcAAIDi1X7Pb64vZp6uOr8A/emel0uOsWjintC67w1uSt33atW5fyUfB5Pp9jY3cW9YSXIc070wb9u0jfuvt8q2VRnvNmVSRbc/x7CXnOlEyeeaiOnvX5VzdZvvF3blFwAAgOIpfgEAACie4hcAAIDiKX4BAAAoXu0Nr5r4Yubp6rcvI2/Ll2S3udFTHTq9Mb/ThgPTzUuzmWZ00uSt3zKZirbMobrndzfev6qsu+7mg9Pdv7aMN+Wrcgyak/VqS2PSKssvaU5UaWA63de1OXtXfgEAACie4hcAAIDiKX4BAAAonuIXAACA4tXe8KoJ022yVOWG75JufN9fjkZjbWmGkFuV+bf/85po9lH6eOyv384FdWvLObdKU7pOVTnP5VTnNjZxvLTp+KuziUyn6m5k2aY8p0Jzq/bo9nFQdyPCXh3/XpvzdWdf53nUlV8AAACKp/gFAACgeIpfAAAAiqf4BQAAoHjZGl5VuXG5k5uo625cUooqjabqbFZQ9zpzNptpYr50e19KagrRiW7vW7/l+XTa0tyqH+WYg7nOw7nOwTn2t8q49uo5qIk55Jw9fVWOg06e16vNBKtoorlVtxvxdbvOq7IOV34BAAAonuIXAACA4il+AQAAKJ7iFwAAgOI10vCqiRu361R3k6U2NU2ou6lUndqeXTfUmX0TDVna0vSlKaXPv37RRDO9puZKW5oA1t1Usu3njCpNpeocM+ekqamzGVg/fkbpRN2fIdvcNK4pTZwjpzvnm9Dt8XDlFwAAgOIpfgEAACie4hcAAIDiKX4BAAAoXqWGV22+abzKDfh1N/Jok7rHbLo3wzfR+KAXm6pMRY7mNVWa/+TKvtsN3Tpdflsay3VLp/Mgxxyqct5r+/yOaKYZU12vezpVxqPt55Yqy9s/l1xzuVfPSzmUnF8TnyE7WV7d87ZtY1b3+06Oc2Sbzy2u/AIAAFA8xS8AAADFU/wCAABQPMUvAAAAxavU8KqJph85btJue2OaEm6Eb2KdbWtuVWV7ptsIpW5taVZQVZ1NMJpo6lBK7hH1NrFr4lzdCzn3QlOufbX9PTaXTsYsV6O2ps5BbZmjVZQyb+ueG3V+Js3VrDbn/Kw75072udufW3Nx5RcAAIDiKX4BAAAonuIXAACA4il+AQAAKF6lhle5dLuRR6fLz3Xje5sbmVRp/tOWfZiqKs0F6sym00YCuRo0NaHKfnS7QVwJzQCfSd2NN+rcv7ZlVbccTUTadA5vU/PNbjeVqnu/mng/mIo2n0fqbjbWqTY3Dopoz2fyurUp46fT7WOjTef5OrnyCwAAQPEUvwAAABRP8QsAAEDxFL8AAAAUbyCVcOdyA9p003ebtqXb2ravbWq8kCOHNu3/ZHotk7aM6zNpooldCc04urEtTcz76Y5vE+uk/9Q9N3LMtSbOhU3kNBnHaTPachyUeq525RcAAIDiKX4BAAAonuIXAACA4il+AQAAKF6rGl7VeWN1rkYhda+j0/V22hChU000WOhk+Z1qanvbfPN/yY2X2tQEpC25NHX+qbIeTVSqaVP2dTa8Yvrqfv9v2zi2ucFeyXK9x9K5fs++zv135RcAAIDiKX4BAAAonuIXAACA4il+AQAAKF6rGl4BAABAN7jyCwAAQPEUvwAAABRP8QsAAEDxFL8AAAAUT/ELAABA8RS/AAAAFE/xCwAAQPEUvwAAABRP8QsAAEDx/h/ScFlJHos8KwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Load the alpha_digit data\n", - "data = read_alpha_digit(file_path=ALPHA_DIGIT_PATH, characters='Z')\n", - "\n", - "# Initialize RBM\n", - "n_visible = data.shape[1] # Number of visible units (size of each image)\n", - "n_hidden = 100 # Number of hidden units (hyperparameter)\n", - "rbm = RBM(n_visible=n_visible, n_hidden=n_hidden, random_state=42)\n", - "repr(rbm)\n", - "\n", - "# Train RBM\n", - "rbm.train(data, learning_rate=0.1, n_epochs=100, batch_size=10)\n", - "\n", - "# Generate samples\n", - "generated_samples = rbm.generate_image(n_samples=10, n_gibbs_steps=1)\n", - "\n", - "# Plot original and generated samples\n", - "plt.figure(figsize=(12, 6))\n", - "for i in range(10):\n", - " plt.subplot(2, 10, i + 1)\n", - " plt.imshow(data[i].reshape(20, 16), cmap='gray')\n", - " plt.title('Original')\n", - " plt.axis('off')\n", - " \n", - " plt.subplot(2, 10, i + 11)\n", - " plt.imshow(generated_samples[i].reshape(20, 16), cmap='gray')\n", - " plt.title('Generated')\n", - " plt.axis('off')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RBM(n_visible=320, n_hidden=100)\n" + "Reconstruction error: 0.01225.\n" ] - } - ], - "source": [ - "print(rbm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2 Implementing a Deep Belief Network (DBN) and test on Binary AlphaDigits" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "class DBN:\n", - " def __init__(self, n_visible: int, hidden_layer_sizes: list[int], random_state=None):\n", - " \"\"\"\n", - " Initialize the Deep Belief Network.\n", - "\n", - " Parameters:\n", - " - n_visible (int): Number of visible units.\n", - " - hidden_layer_sizes (list[int]): List of sizes for each hidden layer.\n", - " - random_state: Random seed for reproducibility.\n", - " \"\"\"\n", - " self.n_visible = n_visible\n", - " self.hidden_layer_sizes = hidden_layer_sizes\n", - " self.rbms = []\n", - " self.rng = np.random.default_rng(random_state)\n", - "\n", - " # Initialize the first RBM\n", - " first_rbm = RBM(\n", - " n_visible=n_visible,\n", - " n_hidden=hidden_layer_sizes[0],\n", - " random_state=random_state,\n", - " )\n", - " self.rbms.append(first_rbm)\n", - "\n", - " # Initialize RBMs for subsequent hidden layers\n", - " for i, size in enumerate(hidden_layer_sizes[1:], start=1):\n", - " rbm = RBM(\n", - " n_visible=hidden_layer_sizes[i - 1],\n", - " n_hidden=size,\n", - " random_state=random_state,\n", - " )\n", - " self.rbms.append(rbm)\n", - "\n", - "\n", - " def __getitem__(self, key):\n", - " return self.rbms[key]\n", - " \n", - "\n", - " def __repr__(self):\n", - " \"\"\"\n", - " Return a string representation of the DBN object.\n", - " \"\"\"\n", - " rbm_reprs = [repr(rbm) for rbm in self.rbms]\n", - " join_rbm_reprs = ',\\n '.join(rbm_reprs)\n", - " return f\"DBN([\\n {join_rbm_reprs}\\n])\"\n", - "\n", - "\n", - " def train(self,\n", - " data: np.ndarray,\n", - " learning_rate: float=0.1,\n", - " n_epochs: int=10,\n", - " batch_size: int=10,\n", - " print_each: int=10,\n", - " ) -> \"DBN\":\n", - " \"\"\"\n", - " Train the DBN using Greedy layer-wise procedure.\n", - "\n", - " Parameters:\n", - " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", - " - n_epochs (int): Number of training epochs. Default is 10.\n", - " - batch_size (int): Size of mini-batches. Default is 10.\n", - " - print_each: Print reconstruction error each `print_each` epochs.\n", - " - verbose\n", - "\n", - " Returns:\n", - " - DBN: Trained DBN instance.\n", - " \"\"\"\n", - " input_data = data\n", - " for rbm in self.rbms:\n", - " rbm.train(\n", - " input_data,\n", - " learning_rate=learning_rate,\n", - " n_epochs=n_epochs,\n", - " batch_size=batch_size,\n", - " print_each=print_each,\n", - " )\n", - " # Update input data for the next RBM\n", - " input_data = rbm.input_output(input_data)\n", - "\n", - " return self\n", - "\n", - " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=100) -> np.ndarray:\n", - " \"\"\"\n", - " Generate samples from the DBN using Gibbs sampling.\n", - "\n", - " Parameters:\n", - " - n_samples (int): Number of samples to generate. Default is 1.\n", - " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100.\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", - " \"\"\"\n", - " samples = np.zeros((n_samples, self.n_visible))\n", - "\n", - " # Matrix of initialization value of Gibbs samples for each sample.\n", - " V = self.rng.binomial(1, self.rng.random(), size=n_samples * self.n_visible).reshape((n_samples, self.n_visible))\n", - " for i in range(n_samples):\n", - " for _ in range(n_gibbs_steps):\n", - " for rbm in self.rbms:\n", - " h_probs = rbm.input_output(V[i])\n", - " h = self.rng.binomial(1, h_probs)\n", - " v_probs = rbm.output_input(h)\n", - " V[i] = self.rng.binomial(1, v_probs)\n", - " samples[i] = V[i]\n", - "\n", - " return samples\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ + }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 666.66it/s]\n" + "Epoch 101: 100%|██████████| 4/4 [00:00<00:00, 800.17it/s]\n", + "Epoch 102: 100%|██████████| 4/4 [00:00<00:00, 1000.79it/s]\n", + "Epoch 103: 100%|██████████| 4/4 [00:00<00:00, 800.25it/s]\n", + "Epoch 104: 100%|██████████| 4/4 [00:00<00:00, 800.02it/s]\n", + "Epoch 105: 100%|██████████| 4/4 [00:00<00:00, 800.36it/s]\n", + "Epoch 106: 100%|██████████| 4/4 [00:00<00:00, 799.94it/s]\n", + "Epoch 107: 100%|██████████| 4/4 [00:00<00:00, 800.13it/s]\n", + "Epoch 108: 100%|██████████| 4/4 [00:00<00:00, 800.17it/s]\n", + "Epoch 109: 100%|██████████| 4/4 [00:00<00:00, 800.13it/s]\n", + "Epoch 110: 100%|██████████| 4/4 [00:00<00:00, 1000.19it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.184.\n" + "Reconstruction error: 0.00928.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 666.77it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 108.12it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 363.73it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 571.78it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 666.82it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 800.25it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 800.25it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 1000.25it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1000.43it/s]\n", - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 4004.11it/s]\n" + "Epoch 111: 100%|██████████| 4/4 [00:00<00:00, 666.66it/s]\n", + "Epoch 112: 100%|██████████| 4/4 [00:00<00:00, 1000.73it/s]\n", + "Epoch 113: 100%|██████████| 4/4 [00:00<00:00, 666.74it/s]\n", + "Epoch 114: 100%|██████████| 4/4 [00:00<00:00, 800.13it/s]\n", + "Epoch 115: 100%|██████████| 4/4 [00:00<00:00, 800.06it/s]\n", + "Epoch 116: 100%|██████████| 4/4 [00:00<00:00, 800.02it/s]\n", + "Epoch 117: 100%|██████████| 4/4 [00:00<00:00, 666.71it/s]\n", + "Epoch 118: 100%|██████████| 4/4 [00:00<00:00, 799.71it/s]\n", + "Epoch 119: 100%|██████████| 4/4 [00:00<00:00, 571.57it/s]\n", + "Epoch 120: 100%|██████████| 4/4 [00:00<00:00, 666.85it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.045.\n" + "Reconstruction error: 0.00509.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 4000.29it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 3999.34it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 2002.29it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1999.43it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 4007.94it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 3993.62it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 2002.53it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 4001.24it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1998.48it/s]\n", - "Epoch 0: 100%|██████████| 4/4 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate samples\n", + "generated_samples = rbm.generate_image(n_samples=10, n_gibbs_steps=1)\n", + "\n", + "# Plot original and generated samples\n", + "plt.figure(figsize=(12, 6))\n", + "for i in range(10):\n", + " plt.subplot(2, 10, i + 1)\n", + " plt.imshow(data[i].reshape(20, 16), cmap='gray')\n", + " plt.title('Original')\n", + " plt.axis('off')\n", + " \n", + " plt.subplot(2, 10, i + 11)\n", + " plt.imshow(generated_samples[i].reshape(20, 16), cmap='gray')\n", + " plt.title('Generated')\n", + " plt.axis('off')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RBM(n_visible=320, n_hidden=200)\n" + ] + } + ], + "source": [ + "print(rbm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Implementing a Deep Belief Network (DBN) and test on Binary AlphaDigits" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "class DBN:\n", + " def __init__(self, n_visible: int, hidden_layer_sizes: list[int], random_state=None):\n", + " \"\"\"\n", + " Initialize the Deep Belief Network.\n", + "\n", + " Parameters:\n", + " - n_visible (int): Number of visible units.\n", + " - hidden_layer_sizes (list[int]): List of sizes for each hidden layer.\n", + " - random_state: Random seed for reproducibility.\n", + " \"\"\"\n", + " self.n_visible = n_visible\n", + " self.hidden_layer_sizes = hidden_layer_sizes\n", + " self.rbms: List[RBM] = []\n", + " self.rng = np.random.default_rng(random_state)\n", + "\n", + " # Initialize the first RBM\n", + " first_rbm = RBM(\n", + " n_visible=n_visible,\n", + " n_hidden=hidden_layer_sizes[0],\n", + " random_state=random_state,\n", + " )\n", + " self.rbms.append(first_rbm)\n", + "\n", + " # Initialize RBMs for subsequent hidden layers\n", + " for i, size in enumerate(hidden_layer_sizes[1:], start=1):\n", + " rbm = RBM(\n", + " n_visible=hidden_layer_sizes[i - 1],\n", + " n_hidden=size,\n", + " random_state=random_state,\n", + " )\n", + " self.rbms.append(rbm)\n", + "\n", + "\n", + " def __getitem__(self, key):\n", + " return self.rbms[key]\n", + " \n", + "\n", + " def __repr__(self):\n", + " \"\"\"\n", + " Return a string representation of the DBN object.\n", + " \"\"\"\n", + " rbm_reprs = [repr(rbm) for rbm in self.rbms]\n", + " join_rbm_reprs = ',\\n '.join(rbm_reprs)\n", + " return f\"DBN([\\n {join_rbm_reprs}\\n])\"\n", + "\n", + "\n", + " def train(self,\n", + " data: np.ndarray,\n", + " learning_rate: float=0.1,\n", + " n_epochs: int=10,\n", + " batch_size: int=10,\n", + " print_each: int=10,\n", + " ) -> \"DBN\":\n", + " \"\"\"\n", + " Train the DBN using Greedy layer-wise procedure.\n", + "\n", + " Parameters:\n", + " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", + " - n_epochs (int): Number of training epochs. Default is 10.\n", + " - batch_size (int): Size of mini-batches. Default is 10.\n", + " - print_each: Print reconstruction error each `print_each` epochs.\n", + " - verbose\n", + "\n", + " Returns:\n", + " - DBN: Trained DBN instance.\n", + " \"\"\"\n", + " input_data = data\n", + " for rbm in self.rbms:\n", + " rbm.train(\n", + " input_data,\n", + " learning_rate=learning_rate,\n", + " n_epochs=n_epochs,\n", + " batch_size=batch_size,\n", + " print_each=print_each,\n", + " )\n", + " # Update input data for the next RBM\n", + " input_data = rbm.input_output(input_data)\n", + "\n", + " return self\n", + "\n", + " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", + " \"\"\"\n", + " Generate samples from the DBN using Gibbs sampling.\n", + "\n", + " Parameters:\n", + " - n_samples (int): Number of samples to generate. Default is 1.\n", + " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100.\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", + " \"\"\"\n", + " # samples = np.zeros((n_samples, self.n_visible))\n", + "\n", + " # Generate samples using the first RBM in the DBN\n", + " samples = self.rbms[-1].generate_image(n_samples, n_gibbs_steps)\n", + " for rbm in reversed(self.rbms[:-1]):\n", + " # Sample from the conditional probability of layer l-1 given layer l: p(h_{s-1}|h_{s}).\n", + " h_probs = rbm.output_input(samples)\n", + " h = self.rng.binomial(1, p=h_probs) \n", + " samples = h\n", + " return samples" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 1000.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.18102.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 999.77it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 666.34it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 1000.13it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 800.63it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 999.77it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 666.85it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 799.75it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 800.44it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 800.17it/s]\n", + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 3993.62it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.01524.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 2000.86it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 1999.91it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 2000.14it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1998.95it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 3997.43it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 3988.88it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 1331.31it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 4016.57it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 4012.73it/s]\n", + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 1999.91it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.00416.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 2001.58it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 4011.77it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 4000.29it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 4003.15it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 4003.15it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 4000.29it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 3998.38it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 4004.11it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 4003.15it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "DBN([\n", + " RBM(n_visible=320, n_hidden=100),\n", + " RBM(n_visible=100, n_hidden=50),\n", + " RBM(n_visible=50, n_hidden=25)\n", + "])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_visible=data.shape[1]\n", + "hidden_layer_sizes = [100, 50, 25]\n", + "\n", + "dbn = DBN(n_visible=n_visible, hidden_layer_sizes=hidden_layer_sizes, random_state=42)\n", + "dbn.train(data, learning_rate=0.1, n_epochs=10, batch_size=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[RBM(n_visible=100, n_hidden=50), RBM(n_visible=50, n_hidden=25)]\n" + ] + } + ], + "source": [ + "# Check if the RBM are accessibles via a slicing \n", + "print(dbn[1:3])" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "dbn[2]" + "# # Generate images\n", + "generated_images = dbn.generate_image(n_samples=5, n_gibbs_steps=1)\n", + "\n", + "# Display generated images\n", + "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(12, 4))\n", + "for i in range(5):\n", + " axes[i].imshow(generated_images[i].reshape(20, 16), cmap='gray')\n", + " axes[i].set_title(f\"Image {i+1}\")\n", + " axes[i].axis('off')\n", + "plt.tight_layout()\n", + "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src/principal_dbn_alpha.py b/src/principal_dbn_alpha.py index e69de29..69bcb20 100644 --- a/src/principal_dbn_alpha.py +++ b/src/principal_dbn_alpha.py @@ -0,0 +1,125 @@ +""" +Module: dbn.py +Module providing implementation of Deep Belief Network (DBN). +""" + +from typing import List + +import numpy as np +from principal_rbm_alpha import RBM + + +class DBN: + """ + Implementation of a Deep Belief Network (DBN). + + Attributes: + - n_visible (int): Number of visible units. + - hidden_layer_sizes (List[int]): List of sizes for each hidden layer. + - rbms (List[RBM]): List of Restricted Boltzmann Machines (RBMs) forming the DBN. + - rng (numpy.random.Generator): Random number generator for sampling. + """ + + def __init__( + self, + n_visible: int, + hidden_layer_sizes: List[int], + random_state=None + ): + """ + Initialize the Deep Belief Network. + + Parameters: + - n_visible (int): Number of visible units. + - hidden_layer_sizes (List[int]): List of sizes for each hidden layer. + - random_state: Random seed for reproducibility. + """ + self.n_visible = n_visible + self.hidden_layer_sizes = hidden_layer_sizes + self.rbms: List[RBM] = [] + self.rng = np.random.default_rng(random_state) + + # Initialize the first RBM + first_rbm = RBM( + n_visible=n_visible, + n_hidden=hidden_layer_sizes[0], + random_state=random_state, + ) + self.rbms.append(first_rbm) + + # Initialize RBMs for subsequent hidden layers + for i, size in enumerate(hidden_layer_sizes[1:], start=1): + rbm = RBM( + n_visible=hidden_layer_sizes[i - 1], + n_hidden=size, + random_state=random_state, + ) + self.rbms.append(rbm) + + def __getitem__(self, key): + return self.rbms[key] + + def __repr__(self): + """ + Return a string representation of the DBN object. + """ + rbm_reprs = [repr(rbm) for rbm in self.rbms] + join_rbm_reprs = ",\n ".join(rbm_reprs) + return f"DBN([\n {join_rbm_reprs}\n])" + + def train( + self, + data: np.ndarray, + learning_rate: float = 0.1, + n_epochs: int = 10, + batch_size: int = 10, + print_each: int = 10, + ) -> "DBN": + """ + Train the DBN using Greedy layer-wise procedure. + + Parameters: + - data (numpy.ndarray): Input data, shape (n_samples, n_visible). + - learning_rate (float): Learning rate for gradient descent. Default is 0.1. + - n_epochs (int): Number of training epochs. Default is 10. + - batch_size (int): Size of mini-batches. Default is 10. + - print_each: Print reconstruction error each `print_each` epochs. + + Returns: + - DBN: Trained DBN instance. + """ + input_data = data + for rbm in self.rbms: + rbm.train( + input_data, + learning_rate=learning_rate, + n_epochs=n_epochs, + batch_size=batch_size, + print_each=print_each, + ) + # Update input data for the next RBM + input_data = rbm.input_output(input_data) + + return self + + def generate_image(self, n_samples: int = 1, n_gibbs_steps: int = 1) -> np.ndarray: + """ + Generate samples from the DBN using Gibbs sampling. + + Parameters: + - n_samples (int): Number of samples to generate. Default is 1. + - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100. + + Returns: + - numpy.ndarray: Generated samples, shape (n_samples, n_visible). + """ + # samples = np.zeros((n_samples, self.n_visible)) + + # Generate samples using the first RBM in the DBN + samples = self.rbms[-1].generate_image(n_samples, n_gibbs_steps) + for rbm in reversed(self.rbms[:-1]): + # Sample from the conditional probability of layer l-1 given layer l: p(h_{s-1}|h_{s}). + h_probs = rbm.output_input(samples) + h = self.rng.binomial(1, p=h_probs) + samples = h + return samples diff --git a/src/principal_rbm_alpha.py b/src/principal_rbm_alpha.py index e7dc818..cff9ef3 100644 --- a/src/principal_rbm_alpha.py +++ b/src/principal_rbm_alpha.py @@ -6,11 +6,10 @@ #TODO: check relevance of using the RBM's RNG for generation phase (look inside the gibbs sampling). # If a seed has been define, the gibbs sampling step will return the same sample for each it will # sample the same h from the binomial -> #WARNING there might be something wrong with the function -# --- Other Tags . -# FIXME: This function is returning incorrect results for negative input values. -# BUG: Division by zero error occurs in certain cases. -# HACK: This code temporarily fixes the issue, but needs a proper solution. -# OPTIMIZE: Improve the efficiency of this loop +# --------------------------- Other Tags (Example usage) ---------------------. +# FIXME: Example: This function is returning incorrect results for negative input values. +# BUG: Example: Division by zero error occurs in certain cases. +# HACK: Example: This code temporarily fixes the issue, but needs a proper solution. """ import os From 68072c62247fce77dbc3f73a9baed625d82dbdb4 Mon Sep 17 00:00:00 2001 From: Yedidia AGNIMO Date: Fri, 29 Mar 2024 19:41:50 +0100 Subject: [PATCH 08/16] feat: start working on dnn (experiment) --- notebook/experiments.ipynb | 1463 ++++++++++++++++++++++-------------- 1 file changed, 893 insertions(+), 570 deletions(-) diff --git a/notebook/experiments.ipynb b/notebook/experiments.ipynb index 3b2b8f7..d8bcd51 100644 --- a/notebook/experiments.ipynb +++ b/notebook/experiments.ipynb @@ -542,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -559,10 +559,10 @@ " self.n_visible = n_visible\n", " self.n_hidden = n_hidden\n", " \n", - " self.a = np.zeros((1, n_visible))\n", - " self.b = np.zeros((1, n_hidden))\n", + " self.a = np.zeros((1, n_visible)) # visible_bias\n", + " self.b = np.zeros((1, n_hidden)) # hidden_bias\n", " self.rng = np.random.default_rng(random_state)\n", - " self.W = 1e-4 * self.rng.standard_normal(size=(n_visible, n_hidden))\n", + " self.W = 1e-4 * self.rng.standard_normal(size=(n_visible, n_hidden)) # weights\n", "\n", " def __repr__(self) -> str:\n", " return f\"RBM(n_visible={self.n_visible}, n_hidden={self.n_hidden})\"\n", @@ -615,6 +615,56 @@ " - numpy.ndarray: Reconstructed visible units, shape (n_samples, n_visible).\n", " \"\"\"\n", " return self._sigmoid(data_h @ self.W.T + self.a)\n", + " \n", + " def calcul_softmax(self, data: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Calculate softmax probabilities for the output units.\n", + "\n", + " Parameters:\n", + " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Softmax probabilities, shape (n_samples, n_hidden).\n", + " \"\"\"\n", + " # Compute activations for the hidden layer\n", + " hidden_activations = self.input_output(data)\n", + " \n", + " # Compute softmax probabilities for the output layer\n", + " exp_hidden_activations = np.exp(hidden_activations)\n", + " softmax_probs = exp_hidden_activations / np.sum(exp_hidden_activations, axis=1, keepdims=True)\n", + " \n", + " return softmax_probs\n", + "\n", + " def update(\n", + " self, \n", + " batch: np.ndarray,\n", + " learning_rate: float=0.1,\n", + " batch_size: Optional[int]=None,\n", + " return_output: bool=False\n", + " ):\n", + " \"\"\"_summary_\n", + "\n", + " Args:\n", + " batch (np.ndarray): _description_\n", + " learning_rate (float, optional): _description_. Defaults to 0.1.\n", + " batch_size (Optional[int], optional): _description_. Defaults to None.\n", + " return_output (bool, optional): _description_. Defaults to False.\n", + " \"\"\"\n", + " if not batch_size:\n", + " batch_size = batch.shape[0]\n", + " pos_h_probs = self.input_output(batch)\n", + " pos_v_probs = self.output_input(pos_h_probs)\n", + " neg_h_probs = self.input_output(pos_v_probs)\n", + " \n", + " # Update weights and biases\n", + " self.W += learning_rate * (batch.T @ pos_h_probs - pos_v_probs.T @ neg_h_probs) / batch_size\n", + " self.b += learning_rate * (pos_h_probs - neg_h_probs).mean(axis=0)\n", + " self.a += learning_rate * (batch - pos_v_probs).mean(axis=0)\n", + "\n", + " if return_output:\n", + " return self, pos_v_probs\n", + " \n", + " return self \n", "\n", " def train(self, \n", " data: np.ndarray,\n", @@ -640,14 +690,12 @@ " self.rng.shuffle(data)\n", " for i in tqdm(range(0, n_samples, batch_size), desc=f\"Epoch {epoch}\"):\n", " batch = data[i:i+batch_size]\n", - " pos_h_probs = self.input_output(batch)\n", - " pos_v_probs = self.output_input(pos_h_probs)\n", - " neg_h_probs = self.input_output(pos_v_probs)\n", - " \n", - " # Update weights and biases\n", - " self.W += learning_rate * (batch.T @ pos_h_probs - pos_v_probs.T @ neg_h_probs) / batch_size\n", - " self.b += learning_rate * (pos_h_probs.mean(axis=0) - neg_h_probs.mean(axis=0))\n", - " self.a += learning_rate * (batch.mean(axis=0) - pos_v_probs.mean(axis=0))\n", + " _, pos_v_probs = self.update(\n", + " batch=batch,\n", + " learning_rate=learning_rate,\n", + " batch_size=batch_size,\n", + " return_output=True\n", + " )\n", " \n", " if epoch % print_each == 0:\n", " tqdm.write(\n", @@ -682,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -692,7 +740,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -706,7 +754,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 666.50it/s]\n" + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 666.56it/s]\n" ] }, { @@ -720,23 +768,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 571.39it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 666.64it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 666.61it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 571.00it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 666.66it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 571.37it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 571.29it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 500.22it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 571.55it/s]\n", - "Epoch 10: 0%| | 0/4 [00:00,\n", - " ,\n", - " \n", + " RBM(n_visible=320, n_hidden=100),\n", + " RBM(n_visible=100, n_hidden=50),\n", + " RBM(n_visible=50, n_hidden=25)\n", "])" ] }, - "execution_count": 16, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -2173,14 +2223,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[, ]\n" + "[RBM(n_visible=100, n_hidden=50), RBM(n_visible=50, n_hidden=25)]\n" ] } ], @@ -2191,12 +2241,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -2219,12 +2269,285 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "class DNN(DBN):\n", + " def __init__(\n", + " self,\n", + " input_dim: int,\n", + " output_dim: int,\n", + " hidden_layer_sizes: List[int],\n", + " random_state=None\n", + " ):\n", + " \"\"\"\n", + " Initialize the Deep Neural Network (DNN).\n", + "\n", + " Parameters:\n", + " - input_dim (int): Dimension of the input.\n", + " - output_dim (int): Dimension of the output.\n", + " - hidden_layer_sizes (List[int]): List of sizes for each hidden layer.\n", + " - random_state: Random seed for reproducibility.\n", + " \"\"\"\n", + " super().__init__(\n", + " n_visible=input_dim,\n", + " hidden_layer_sizes=hidden_layer_sizes,\n", + " random_state=random_state\n", + " )\n", + " #--> self.rbms contains only the pre-trainable RBMs \n", + " self.clf = RBM(self.rbms[-1].n_hidden, output_dim)\n", + " self.network = self.rbms + [self.clf] # DNN = [DBN + Classifier] ~ [RBM_0,...,RBM_N, RBM_Clf]\n", + "\n", + " def __getitem__(self, key):\n", + " return self.network[key]\n", + " \n", + " def __repr__(self):\n", + " join_repr = \"\\n\".join([f\"{'':4}{repr(rbm)},\" for rbm in self.network])\n", + " return f\"DNN([\\n{join_repr} \\n])\"\n", + " \n", + " \n", + " def pretrain(self, n_epochs: int, learning_rate: float, batch_size: int, data: np.ndarray) -> \"DNN\":\n", + " \"\"\"\n", + " Pretrain the hidden layers of the DNN using the DBN training method.\n", + "\n", + " Parameters:\n", + " - n_epochs (int): Number of training epochs.\n", + " - learning_rate (float): Learning rate for gradient descent.\n", + " - batch_size (int): Size of mini-batches.\n", + " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + "\n", + " Returns:\n", + " - DNN: Pretrained DNN instance.\n", + " \"\"\"\n", + " # NOTE: Use the inherited `train` method to perform pre-training since `self.rbms`\n", + " # only contains the pre-trainable RBMs.\n", + " return self.train(data, n_epochs=n_epochs, learning_rate=learning_rate, batch_size=batch_size)\n", + " \n", + " def input_output(self, input_data: np.ndarray) -> Tuple[List[np.ndarray], np.ndarray]:\n", + " \"\"\"\n", + " Get the outputs on each layer of the DNN and the softmax probabilities on the output layer.\n", + "\n", + " Parameters:\n", + " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + "\n", + " Returns:\n", + " - Tuple[List[np.ndarray], np.ndarray]: Outputs on each layer & softmax probabilities.\n", + " \"\"\"\n", + " layer_outputs = []\n", + " \n", + " # Input layer output\n", + " layer_outputs.append(input_data)\n", + " \n", + " # Hidden layers output\n", + " for rbm in self.rbms:\n", + " layer_outputs.append(rbm.input_output(layer_outputs[-1]))\n", + " \n", + " # Softmax probabilities on the output layer\n", + " output_probs = self.network[-1].calcul_softmax(layer_outputs[-1])\n", + " \n", + " return layer_outputs, output_probs\n", + " \n", + "\n", + " def _cross_entropy(batch_labels: np.ndarray, output_probs: np.ndarray, eps: float = 1e-15) -> float:\n", + " \"\"\"\n", + " Calculate the cross entropy between the batch labels and output probabilities.\n", + "\n", + " Parameters:\n", + " - batch_labels (numpy.ndarray): True labels for the batch, shape (batch_size, n_classes).\n", + " - output_probs (numpy.ndarray): Predicted probabilities for the batch, shape (batch_size, n_classes).\n", + " - eps (float): Small value to avoid numerical instability in logarithm calculation. Default is 1e-15.\n", + "\n", + " Returns:\n", + " - float: Cross entropy value.\n", + " \"\"\"\n", + " return -np.mean(np.sum(batch_labels * np.log(output_probs + eps), axis=1))\n", + "\n", + "\n", + " def backpropagation(\n", + " self,\n", + " input_data: np.ndarray,\n", + " labels: np.ndarray,\n", + " n_epochs: int,\n", + " learning_rate: float,\n", + " batch_size: int,\n", + " eps: float = 1e-15\n", + " ) -> \"DNN\":\n", + " \"\"\"\n", + " Estimate the weights/biases of the network using backpropagation algorithm.\n", + "\n", + " Parameters:\n", + " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + " - labels (numpy.ndarray): Labels for the input data, shape (n_samples, n_classes).\n", + " - n_epochs (int): Number of training epochs.\n", + " - learning_rate (float): Learning rate for gradient descent.\n", + " - batch_size (int): Size of mini-batches.\n", + " - eps (float): Small value to avoid numerical instability in logarithm calculation. Default is 1e-15.\n", + "\n", + " Returns:\n", + " - DNN: Updated DNN instance.\n", + " \"\"\"\n", + " n_samples = input_data.shape[0]\n", + " \n", + " for epoch in tqdm(range(n_epochs), desc=\"Training\", unit=\"epoch\"):\n", + " for batch_start in range(0, n_samples, batch_size):\n", + " batch_end = min(batch_start + batch_size, n_samples)\n", + " batch_input = input_data[batch_start:batch_end]\n", + " batch_labels = labels[batch_start:batch_end]\n", + "\n", + " # Forward pass\n", + " layer_outputs, output_probs = self.input_output(batch_input)\n", + "\n", + " # Backward pass (update weights and biases)\n", + " self.network[-1].update(batch_labels, layer_outputs[-1], learning_rate)\n", + " for i in range(len(self.network) - 2, -1, -1):\n", + " self.network[i].update(layer_outputs[i], layer_outputs[i + 1], self.network[i + 1].weights, learning_rate)\n", + "\n", + " # Calculate cross entropy after each epoch\n", + " loss = self._cross_entropy(batch_labels, output_probs, eps)\n", + " tqdm.write(f\"Epoch {epoch + 1}/{n_epochs}, Cross Entropy: {loss}\")\n", + "\n", + " return self\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 498.09it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.15315.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 499.89it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 571.16it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 499.74it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 499.96it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 500.10it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 499.99it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 571.29it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 444.55it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 499.90it/s]\n", + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 1335.02it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.03169.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 2003.25it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 1999.91it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 3995.53it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1999.43it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 1995.86it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 1331.95it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 666.42it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 1333.96it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1333.75it/s]\n", + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 1333.75it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.00677.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 1335.13it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 2001.82it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 2001.10it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1333.43it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 2004.93it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 1999.19it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 1997.76it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 2003.49it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1998.00it/s]\n" + ] + } + ], + "source": [ + "n_visible=data.shape[1]\n", + "hidden_layer_sizes = [100, 50, 25]\n", + "output_dim = 20\n", + "\n", + "dnn = DNN(input_dim=n_visible, hidden_layer_sizes=hidden_layer_sizes, output_dim=output_dim, random_state=42)\n", + "# keep last RBM's weights for further test.\n", + "weights = dnn[-1].W \n", + "\n", + "dnn.train(data, learning_rate=0.1, n_epochs=10, batch_size=10)\n", + "\n", + "# Check that the last RBM has not been trained.\n", + "np.testing.assert_equal (dnn[-1].a, 0) # visible bias\n", + "np.testing.assert_equal (dnn[-1].b, 0) # hidden bias\n", + "np.testing.assert_equal (dnn[-1].W, weights) # weights" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No. network output = 5\n", + "Input data (0): (39, 320)\n", + "Hidden layer input (1): (39, 100)\n", + "Hidden layer input (2): (39, 50)\n", + "Hidden layer input (3): (39, 25)\n", + "Softmax output (4): (39, 20)\n" + ] + } + ], + "source": [ + "layer_outputs, softmax_output = dnn.input_output(data)\n", + "n_layers_net = len(layer_outputs) + 1\n", + "print(\"No. network output =\", n_layers_net)\n", + "\n", + "print(f\"Input data (0): {layer_outputs[0].shape}\")\n", + "for idx, layer_output in enumerate(layer_outputs[1:]):\n", + " print(f\"Hidden layer input ({idx+1}): {layer_output.shape}\")\n", + "\n", + "print(f\"Softmax output ({n_layers_net - 1}):\", softmax_output.shape)" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "dnn." + ] } ], "metadata": { From 75219bead4218491cd9715276b1aa130e2c5351b Mon Sep 17 00:00:00 2001 From: Yann CHOHO Date: Sat, 30 Mar 2024 01:17:51 +0100 Subject: [PATCH 09/16] chore : tested hyperparameters tuning on alpha digit --- notebook/experiments_ALPHA_DIGITS.ipynb | 22236 ++++++++++++++++ requirements.txt | 5 +- .../100_Units_2_Layers/Units_100_Chars_A.npy | Bin 0 -> 2688 bytes .../100_Units_2_Layers/Units_100_Chars_Y.npy | Bin 0 -> 2688 bytes .../200_Units_2_Layers/Units_200_Chars_A.npy | Bin 0 -> 2688 bytes .../200_Units_2_Layers/Units_200_Chars_Y.npy | Bin 0 -> 2688 bytes 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create mode 100644 resultat/dbn/200_Units_2_Layers/Units_200_Chars_Y.npy create mode 100644 resultat/dbn/300_Units_2_Layers/Units_300_Chars_A.npy create mode 100644 resultat/dbn/300_Units_2_Layers/Units_300_Chars_Y.npy create mode 100644 resultat/dbn/400_Units_2_Layers/Units_400_Chars_A.npy create mode 100644 resultat/dbn/400_Units_2_Layers/Units_400_Chars_Y.npy create mode 100644 resultat/dbn/500_Units_2_Layers/Units_500_Chars_A.npy create mode 100644 resultat/dbn/500_Units_2_Layers/Units_500_Chars_Y.npy create mode 100644 resultat/dbn/600_Units_2_Layers/Units_600_Chars_A.npy create mode 100644 resultat/dbn/600_Units_2_Layers/Units_600_Chars_Y.npy create mode 100644 resultat/dbn/700_Units_2_Layers/Units_700_Chars_A.npy create mode 100644 resultat/dbn/700_Units_2_Layers/Units_700_Chars_Y.npy diff --git a/notebook/experiments_ALPHA_DIGITS.ipynb b/notebook/experiments_ALPHA_DIGITS.ipynb new file mode 100644 index 0000000..81eadfe --- /dev/null +++ b/notebook/experiments_ALPHA_DIGITS.ipynb @@ -0,0 +1,22236 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Restricted Botlzman Machines (RBM)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting tqdm\n", + " Using cached tqdm-4.66.2-py3-none-any.whl.metadata (57 kB)\n", + "Requirement already satisfied: colorama in c:\\users\\choho\\desktop\\master ds\\deep learning ii\\github\\.venv\\lib\\site-packages (from tqdm) (0.4.6)\n", + "Using cached tqdm-4.66.2-py3-none-any.whl (78 kB)\n", + "Installing collected packages: tqdm\n", + "Successfully installed tqdm-4.66.2\n" + ] + } + ], + "source": [ + "#FIXME: Review the generation process (theoretically) and fix the implementation " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from typing import List, Dict, Tuple, Literal, Optional, Union, Iterable\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import scipy.io\n", + "from tqdm import tqdm\n", + "from numpy._typing import ArrayLike\n", + "\n", + "ArrayLike = Union[List, Tuple, np.ndarray]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "DATA_FOLDER = \"../data/\"\n", + "ALPHA_DIGIT_PATH = os.path.join(DATA_FOLDER, \"binaryalphadigs.mat\")\n", + "MNIST_PATH = os.path.join(DATA_FOLDER, \"mnist_all.mat\")\n", + "\n", + "if not os.path.exists(ALPHA_DIGIT_PATH):\n", + " raise FileNotFoundError(f\"The file {ALPHA_DIGIT_PATH} does not exist.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Implementing a RBM and testing on Binary AlphaDigits" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def _load_data(file_path: str) -> Dict[str, np.ndarray]:\n", + " \"\"\"\n", + " Load Binary AlphaDigits data from a .mat file.\n", + "\n", + " Parameters:\n", + " - file_path (str): Path to the .mat file containing the data.\n", + "\n", + " Returns:\n", + " - data (dict): Loaded data dictionary.\n", + " \"\"\"\n", + " if file_path is None:\n", + " raise ValueError(\"File path must be provided.\")\n", + "\n", + " return scipy.io.loadmat(file_path)\n", + "\n", + "\n", + "data = _load_data(ALPHA_DIGIT_PATH)\n", + "class_labels = data[\"classlabels\"].flatten() \n", + "class_count = data[\"classcounts\"].flatten()\n", + "df = pd.DataFrame(\n", + " {\n", + " \"Class Labels\": class_labels,\n", + " \"Class Count\": class_count\n", + " }\n", + ")\n", + "df[\"Class Labels\"] = df[\"Class Labels\"].apply(lambda x: x[0])\n", + "df[\"Class Count\"] = df[\"Class Count\"].apply(lambda x: x[0][0])\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def _load_data(file_path: str, which: Literal[\"alphadigit\", \"mnist\"]=\"alphadigit\") -> Dict[str, np.ndarray]:\n", + " \"\"\"\n", + " Load Binary AlphaDigits data from a .mat file.\n", + "\n", + " Parameters:\n", + " - file_path (str): Path to the .mat file containing the data.\n", + " - which (Literal[\"alphadigit\", \"mnist\"], optional): Specifies \n", + " which data to load. The default value is \"alphadigit\".\n", + "\n", + " Returns:\n", + " - data (dict): A dictionary containing the loaded data.\n", + "\n", + " Raises:\n", + " - ValueError: If the file_path parameter is None.\n", + " - ValueError: If the which parameter is not \"alphadigit\".\n", + "\n", + " Example Usage:\n", + " ```python\n", + " data = _load_data(\"data.mat\", \"alphadigit\")\n", + " ```\n", + " \"\"\"\n", + " if file_path is None:\n", + " raise ValueError(\"File path must be provided.\")\n", + " \n", + " if which == \"alphadigit\":\n", + " return scipy.io.loadmat(file_path)[\"dat\"]\n", + " \n", + " raise ValueError(\"MNIST NOT YET AVAILABLE.\")\n", + "\n", + "alphadigit_data = _load_data(ALPHA_DIGIT_PATH) \n", + "print(alphadigit_data.shape)\n", + "print(alphadigit_data[0][0].shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 > map to > [0]\n", + "10 > map to > [10]\n", + "A > map to > [10]\n", + "[1, 'C'] > map to > [[1], [12]]\n", + "36 > no mapping available, out of range\n" + ] + } + ], + "source": [ + "def _map_characters_to_indices(characters: Union[str, int, List[Union[str, int]]]) -> List[int]:\n", + " \"\"\"\n", + " Map alphanumeric character to its corresponding index.\n", + "\n", + " Parameters:\n", + " - character (str, int, list of str or int): Alphanumeric character or its index.\n", + "\n", + " Returns:\n", + " - char_index (int): Corresponding index for the character.\n", + " \"\"\"\n", + " if isinstance(characters, list):\n", + " return [_map_characters_to_indices(char) for char in characters]\n", + " if isinstance(characters, int) and 0 <= characters <= 35:\n", + " return [characters]\n", + " if (isinstance(characters, str) and characters.isdigit()\n", + " and 0 <= int(characters) <= 9):\n", + " return [int(characters)]\n", + " if (isinstance(characters, str) and characters.isalpha()\n", + " and 'A' <= characters.upper() <= 'Z'):\n", + " return [ord(characters.upper()) - ord('A') + 10]\n", + " \n", + " raise ValueError(\n", + " \"Invalid character input. It should be an alphanumeric\" \n", + " \"character '[0-9|A-Z]' or its index representing '[0-35]'.\"\n", + " )\n", + "\n", + "for char in [0, 10, \"A\", [1, \"C\"], 36]:\n", + " try:\n", + " map = _map_characters_to_indices(char)\n", + " print(f\"{char} > map to > {map}\")\n", + " except:\n", + " print(f\"{char} > no mapping available, out of range\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def read_alpha_digit(characters: Optional[Union[str, int, List[Union[str, int]]]] = None,\n", + " file_path: Optional[str] = ALPHA_DIGIT_PATH,\n", + " data: Optional[Dict[str, np.ndarray]] = None,\n", + " use_data: bool = False,\n", + " ) -> np.ndarray:\n", + " \"\"\"\n", + " Reads binary AlphaDigits data from a .mat file or uses already loaded data. \n", + " It extracts the data for a specified alphanumeric character or its index, and \n", + " flattens the images into one-dimensional vectors.\n", + "\n", + " Parameters:\n", + " - characters (Union[str, int, List[Union[str, int]]], optional): Alphanumeric character \n", + " or its index whose data needs to be extracted. It can be a single character or \n", + " a list of characters. Default is None.\n", + " - file_path (str, optional): Path to the .mat file containing the data. \n", + " Default is None.\n", + " - data (dict, optional): Already loaded data dictionary. \n", + " Default is None.\n", + " - use_data (bool): Flag to indicate whether to use already loaded data.\n", + " Default is False.\n", + "\n", + " Returns:\n", + " - flattened_images (numpy.ndarray): Flattened images for the specified character(s).\n", + " \"\"\"\n", + " if not use_data:\n", + " data = _load_data(file_path, which=\"alphadigit\")\n", + "\n", + " char_indices = _map_characters_to_indices(characters)\n", + "\n", + " # Select the rows corresponding to the characters indices.\n", + " char_data: np.ndarray = data[char_indices]\n", + " \n", + " # Flatten each image into a one-dimensional vector.\n", + " flattened_images = np.array([image.flatten() for image in char_data.flatten()])\n", + " return flattened_images\n", + "\n", + "def plot_characters(chars, data):\n", + " num_chars = len(chars)\n", + " num_images_per_char = data.shape[0] // num_chars\n", + " fig, ax = plt.subplots(1, num_chars, figsize=(num_chars * 2, 2))\n", + "\n", + " for i, char in enumerate(chars):\n", + " # Find the index of the first image corresponding to the current char\n", + " start_index = i * num_images_per_char\n", + " image = data[start_index].reshape(20, 16)\n", + " ax[i].imshow(image, cmap='gray')\n", + " ax[i].set_title(f'Char: {char}')\n", + " ax[i].axis('off')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# Example\n", + "chars = [0, \"K\", 7, \"Z\"]\n", + "data = read_alpha_digit(chars, data=alphadigit_data, use_data=True)\n", + "plot_characters(chars, data)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data shape: (156, 320)\n" + ] + } + ], + "source": [ + "print(\"data shape:\", data.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "class RBM:\n", + " def __init__(self, n_visible: int, n_hidden: int=100, random_state=None) -> None:\n", + " \"\"\"\n", + " Initialize the Restricted Boltzmann Machine.\n", + "\n", + " Parameters:\n", + " - n_visible (int): Number of visible units.\n", + " - n_hidden (int): Number of hidden units. Default 100.\n", + " - random_state: Random seed for reproducibility.\n", + " \"\"\"\n", + " self.n_visible = n_visible\n", + " self.n_hidden = n_hidden\n", + " \n", + " self.a = np.zeros((1, n_visible)) # visible_bias\n", + " self.b = np.zeros((1, n_hidden)) # hidden_bias\n", + " self.rng = np.random.default_rng(random_state)\n", + " self.W = 1e-4 * self.rng.standard_normal(size=(n_visible, n_hidden)) # weights\n", + "\n", + " def __repr__(self) -> str:\n", + " return f\"RBM(n_visible={self.n_visible}, n_hidden={self.n_hidden})\"\n", + "\n", + " def _sigmoid(self, x: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Sigmoid activation function.\n", + "\n", + " Parameters:\n", + " - x (numpy.ndarray): Input array.\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Result of applying the sigmoid function to the input.\n", + " \"\"\"\n", + " return 1 / (1 + np.exp(-x))\n", + " \n", + " def _reconstruction_error(self, input: np.ndarray, image: np.ndarray) -> float:\n", + " \"\"\"\n", + " Compute reconstruction error.\n", + "\n", + " Parameters:\n", + " - input (numpy.ndarray): Original input data.\n", + " - image (numpy.ndarray): Reconstructed image.\n", + "\n", + " Returns:\n", + " - float: Reconstruction error.\n", + " \"\"\"\n", + " return np.round(np.power(image - input, 2).mean(), 5)\n", + "\n", + " def input_output(self, data: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Compute hidden units given visible units.\n", + "\n", + " Parameters:\n", + " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Hidden unit activations, shape (n_samples, n_hidden).\n", + " \"\"\"\n", + " return self._sigmoid(data @ self.W + self.b)\n", + "\n", + " def output_input(self, data_h: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Compute visible units given hidden units.\n", + "\n", + " Parameters:\n", + " - data_h (numpy.ndarray): Hidden unit activations, shape (n_samples, n_hidden).\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Reconstructed visible units, shape (n_samples, n_visible).\n", + " \"\"\"\n", + " return self._sigmoid(data_h @ self.W.T + self.a)\n", + " \n", + " def calcul_softmax(self, data: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Calculate softmax probabilities for the output units.\n", + "\n", + " Parameters:\n", + " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Softmax probabilities, shape (n_samples, n_hidden).\n", + " \"\"\"\n", + " # Compute activations for the hidden layer\n", + " hidden_activations = self.input_output(data)\n", + " \n", + " # Compute softmax probabilities for the output layer\n", + " exp_hidden_activations = np.exp(hidden_activations)\n", + " softmax_probs = exp_hidden_activations / np.sum(exp_hidden_activations, axis=1, keepdims=True)\n", + " \n", + " return softmax_probs\n", + "\n", + " def update(\n", + " self, \n", + " batch: np.ndarray,\n", + " learning_rate: float=0.1,\n", + " batch_size: Optional[int]=None,\n", + " return_output: bool=False\n", + " ):\n", + " \"\"\"_summary_\n", + "\n", + " Args:\n", + " batch (np.ndarray): _description_\n", + " learning_rate (float, optional): _description_. Defaults to 0.1.\n", + " batch_size (Optional[int], optional): _description_. Defaults to None.\n", + " return_output (bool, optional): _description_. Defaults to False.\n", + " \"\"\"\n", + " if not batch_size:\n", + " batch_size = batch.shape[0]\n", + " pos_h_probs = self.input_output(batch)\n", + " pos_v_probs = self.output_input(pos_h_probs)\n", + " neg_h_probs = self.input_output(pos_v_probs)\n", + " \n", + " # Update weights and biases\n", + " self.W += learning_rate * (batch.T @ pos_h_probs - pos_v_probs.T @ neg_h_probs) / batch_size\n", + " self.b += learning_rate * (pos_h_probs - neg_h_probs).mean(axis=0)\n", + " self.a += learning_rate * (batch - pos_v_probs).mean(axis=0)\n", + "\n", + " if return_output:\n", + " return self, pos_v_probs\n", + " \n", + " return self \n", + "\n", + " def train(self, \n", + " data: np.ndarray,\n", + " learning_rate: float=0.1,\n", + " n_epochs: int=10,\n", + " batch_size: int=10,\n", + " print_each=10\n", + " ) -> 'RBM':\n", + " \"\"\"\n", + " Train the RBM using Contrastive Divergence.\n", + "\n", + " Parameters:\n", + " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", + " - n_epochs (int): Number of training epochs. Default is 10.\n", + " - batch_size (int): Size of mini-batches. Default is 10.\n", + "\n", + " Returns:\n", + " - RBM: Trained RBM instance.\n", + " \"\"\"\n", + " n_samples = data.shape[0]\n", + " for epoch in range(n_epochs):\n", + " self.rng.shuffle(data)\n", + " for i in tqdm(range(0, n_samples, batch_size), desc=f\"Epoch {epoch}\"):\n", + " batch = data[i:i+batch_size]\n", + " _, pos_v_probs = self.update(\n", + " batch=batch,\n", + " learning_rate=learning_rate,\n", + " batch_size=batch_size,\n", + " return_output=True\n", + " )\n", + " \n", + " if epoch % print_each == 0:\n", + " tqdm.write(\n", + " f\"Reconstruction error: {self._reconstruction_error(batch, pos_v_probs)}.\")\n", + "\n", + " return self\n", + "\n", + " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", + " \"\"\"\n", + " Generate samples from the RBM using Gibbs sampling.\n", + "\n", + " Parameters:\n", + " - n_samples (int): Number of samples to generate. Default is 10.\n", + " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 1.\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", + " \"\"\"\n", + " samples = np.zeros((n_samples, self.n_visible))\n", + " \n", + " # Matrix of initlization value of Gibbs samples for each sample. \n", + " V = self.rng.binomial(1, self.rng.random(), size=n_samples*self.n_visible).reshape((n_samples, self.n_visible))\n", + " for i in range(n_samples):\n", + " for _ in range(n_gibbs_steps):\n", + " h_probs = self._sigmoid(V[i] @ self.W + self.b) # vector\n", + " h = self.rng.binomial(1, h_probs)\n", + " v_probs = self._sigmoid(h @ self.W.T + self.a)\n", + " v = self.rng.binomial(1, v_probs)\n", + " samples[i] = v\n", + " return samples" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the alpha_digit data\n", + "data = read_alpha_digit(file_path=ALPHA_DIGIT_PATH, characters=['Z'])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RBM(n_visible=320, n_hidden=200)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 557.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.16569.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 952.71it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 530.37it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 557.38it/s]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 530.64it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 571.37it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 999.83it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 570.96it/s]\n", + "Epoch 8: 100%|██████████| 4/4 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100%|██████████| 4/4 [00:00<00:00, 1328.36it/s]\n", + "Epoch 486: 100%|██████████| 4/4 [00:00<00:00, 1333.75it/s]\n", + "Epoch 487: 100%|██████████| 4/4 [00:00<00:00, 1333.32it/s]\n", + "Epoch 488: 100%|██████████| 4/4 [00:00<00:00, 1337.26it/s]\n", + "Epoch 489: 100%|██████████| 4/4 [00:00<00:00, 1332.37it/s]\n", + "Epoch 490: 100%|██████████| 4/4 [00:00<00:00, 1141.46it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 8e-05.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 491: 100%|██████████| 4/4 [00:00<00:00, 1333.01it/s]\n", + "Epoch 492: 100%|██████████| 4/4 [00:00<00:00, 1593.13it/s]\n", + "Epoch 493: 100%|██████████| 4/4 [00:00<00:00, 2008.53it/s]\n", + "Epoch 494: 100%|██████████| 4/4 [00:00<00:00, 1334.38it/s]\n", + "Epoch 495: 100%|██████████| 4/4 [00:00<00:00, 1000.25it/s]\n", + "Epoch 496: 100%|██████████| 4/4 [00:00<00:00, 882.96it/s]\n", + "Epoch 497: 100%|██████████| 4/4 [00:00<00:00, 1328.26it/s]\n", + "Epoch 498: 100%|██████████| 4/4 [00:00<00:00, 1331.10it/s]\n", + "Epoch 499: 100%|██████████| 4/4 [00:00<00:00, 1998.95it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "RBM(n_visible=320, n_hidden=200)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Parameters\n", + "n_visible = data.shape[1] # Number of visible units (size of each image)\n", + "n_hidden = 200 # Number of hidden units (hyperparameter)\n", + "\n", + "# Initialize RBM\n", + "rbm = RBM(n_visible=n_visible, n_hidden=n_hidden, random_state=42)\n", + "print(rbm)\n", + "\n", + "# Train RBM\n", + "rbm.train(data, learning_rate=0.1, n_epochs=500, batch_size=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "np.testing.assert_allclose(rbm.calcul_softmax(data).sum(1), 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate samples\n", + "generated_samples = rbm.generate_image(n_samples=10, n_gibbs_steps=1)\n", + "\n", + "# Plot original and generated samples\n", + "plt.figure(figsize=(12, 6))\n", + "for i in range(10):\n", + " plt.subplot(2, 10, i + 1)\n", + " plt.imshow(data[i].reshape(20, 16), cmap='gray')\n", + " plt.title('Original')\n", + " plt.axis('off')\n", + " \n", + " plt.subplot(2, 10, i + 11)\n", + " plt.imshow(generated_samples[i].reshape(20, 16), cmap='gray')\n", + " plt.title('Generated')\n", + " plt.axis('off')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RBM(n_visible=320, n_hidden=200)\n" + ] + } + ], + "source": [ + "print(rbm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Implementing a Deep Belief Network (DBN) and test on Binary AlphaDigits" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "class DBN:\n", + " def __init__(self, n_visible: int, hidden_layer_sizes: list[int], random_state=None):\n", + " \"\"\"\n", + " Initialize the Deep Belief Network.\n", + "\n", + " Parameters:\n", + " - n_visible (int): Number of visible units.\n", + " - hidden_layer_sizes (list[int]): List of sizes for each hidden layer.\n", + " - random_state: Random seed for reproducibility.\n", + " \"\"\"\n", + " self.n_visible = n_visible\n", + " self.hidden_layer_sizes = hidden_layer_sizes\n", + " self.rbms: List[RBM] = []\n", + " self.rng = np.random.default_rng(random_state)\n", + "\n", + " # Initialize the first RBM\n", + " first_rbm = RBM(\n", + " n_visible=n_visible,\n", + " n_hidden=hidden_layer_sizes[0],\n", + " random_state=random_state,\n", + " )\n", + " self.rbms.append(first_rbm)\n", + "\n", + " # Initialize RBMs for subsequent hidden layers\n", + " for i, size in enumerate(hidden_layer_sizes[1:], start=1):\n", + " rbm = RBM(\n", + " n_visible=hidden_layer_sizes[i - 1],\n", + " n_hidden=size,\n", + " random_state=random_state,\n", + " )\n", + " self.rbms.append(rbm)\n", + "\n", + "\n", + " def __getitem__(self, key):\n", + " return self.rbms[key]\n", + " \n", + "\n", + " def __repr__(self):\n", + " \"\"\"\n", + " Return a string representation of the DBN object.\n", + " \"\"\"\n", + " rbm_reprs = [f\"{'':4}{repr(rbm)}\" for rbm in self.rbms]\n", + " join_rbm_reprs = ',\\n'.join(rbm_reprs)\n", + " return f\"DBN([\\n{join_rbm_reprs}\\n])\"\n", + "\n", + "\n", + " def train(self,\n", + " data: np.ndarray,\n", + " learning_rate: float=0.1,\n", + " n_epochs: int=10,\n", + " batch_size: int=10,\n", + " print_each: int=10,\n", + " ) -> \"DBN\":\n", + " \"\"\"\n", + " Train the DBN using Greedy layer-wise procedure.\n", + "\n", + " Parameters:\n", + " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", + " - n_epochs (int): Number of training epochs. Default is 10.\n", + " - batch_size (int): Size of mini-batches. Default is 10.\n", + " - print_each: Print reconstruction error each `print_each` epochs.\n", + " - verbose\n", + "\n", + " Returns:\n", + " - DBN: Trained DBN instance.\n", + " \"\"\"\n", + " input_data = data\n", + " for rbm in self.rbms:\n", + " rbm.train(\n", + " input_data,\n", + " learning_rate=learning_rate,\n", + " n_epochs=n_epochs,\n", + " batch_size=batch_size,\n", + " print_each=print_each,\n", + " )\n", + " # Update input data for the next RBM\n", + " input_data = rbm.input_output(input_data)\n", + "\n", + " return self\n", + "\n", + " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", + " \"\"\"\n", + " Generate samples from the DBN using Gibbs sampling.\n", + "\n", + " Parameters:\n", + " - n_samples (int): Number of samples to generate. Default is 1.\n", + " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100.\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", + " \"\"\"\n", + " # samples = np.zeros((n_samples, self.n_visible))\n", + "\n", + " # Generate samples using the first RBM in the DBN\n", + " samples = self.rbms[-1].generate_image(n_samples, n_gibbs_steps)\n", + " for rbm in reversed(self.rbms[:-1]):\n", + " # Sample from the conditional probability of layer l-1 given layer l: p(h_{s-1}|h_{s}).\n", + " h_probs = rbm.output_input(samples)\n", + " h = self.rng.binomial(1, p=h_probs) \n", + " samples = h\n", + " return samples" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# from principal_dbn_alpha import DBN" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 467.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.15919.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 467.94it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 531.53it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 783.51it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1129.93it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 887.92it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 801.05it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 887.31it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 793.66it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1141.23it/s]\n", + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 4005.06it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.01702.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 49344.75it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 1997.29it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 4007.94it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 3981.30it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# # Generate images\n", + "generated_images = dbn.generate_image(n_samples=5, n_gibbs_steps=1)\n", + "\n", + "# Display generated images\n", + "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(12, 4))\n", + "for i in range(5):\n", + " axes[i].imshow(generated_images[i].reshape(20, 16), cmap='gray')\n", + " axes[i].set_title(f\"Image {i+1}\")\n", + " axes[i].axis('off')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "class DNN(DBN):\n", + " def __init__(\n", + " self,\n", + " input_dim: int,\n", + " output_dim: int,\n", + " hidden_layer_sizes: List[int],\n", + " random_state=None\n", + " ):\n", + " \"\"\"\n", + " Initialize the Deep Neural Network (DNN).\n", + "\n", + " Parameters:\n", + " - input_dim (int): Dimension of the input.\n", + " - output_dim (int): Dimension of the output.\n", + " - hidden_layer_sizes (List[int]): List of sizes for each hidden layer.\n", + " - random_state: Random seed for reproducibility.\n", + " \"\"\"\n", + " super().__init__(\n", + " n_visible=input_dim,\n", + " hidden_layer_sizes=hidden_layer_sizes,\n", + " random_state=random_state\n", + " )\n", + " #--> self.rbms contains only the pre-trainable RBMs \n", + " self.clf = RBM(self.rbms[-1].n_hidden, output_dim)\n", + " self.network = self.rbms + [self.clf] # DNN = [DBN + Classifier] ~ [RBM_0,...,RBM_N, RBM_Clf]\n", + "\n", + " def __getitem__(self, key):\n", + " return self.network[key]\n", + " \n", + " def __repr__(self):\n", + " join_repr = \"\\n\".join([f\"{'':4}{repr(rbm)},\" for rbm in self.network])\n", + " return f\"DNN([\\n{join_repr} \\n])\"\n", + " \n", + " \n", + " def pretrain(self, n_epochs: int, learning_rate: float, batch_size: int, data: np.ndarray) -> \"DNN\":\n", + " \"\"\"\n", + " Pretrain the hidden layers of the DNN using the DBN training method.\n", + "\n", + " Parameters:\n", + " - n_epochs (int): Number of training epochs.\n", + " - learning_rate (float): Learning rate for gradient descent.\n", + " - batch_size (int): Size of mini-batches.\n", + " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + "\n", + " Returns:\n", + " - DNN: Pretrained DNN instance.\n", + " \"\"\"\n", + " # NOTE: Use the inherited `train` method to perform pre-training since `self.rbms`\n", + " # only contains the pre-trainable RBMs.\n", + " return self.train(data, n_epochs=n_epochs, learning_rate=learning_rate, batch_size=batch_size)\n", + " \n", + " def input_output(self, input_data: np.ndarray) -> Tuple[List[np.ndarray], np.ndarray]:\n", + " \"\"\"\n", + " Get the outputs on each layer of the DNN and the softmax probabilities on the output layer.\n", + "\n", + " Parameters:\n", + " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + "\n", + " Returns:\n", + " - Tuple[List[np.ndarray], np.ndarray]: Outputs on each layer & softmax probabilities.\n", + " \"\"\"\n", + " layer_outputs = []\n", + " \n", + " # Input layer output\n", + " layer_outputs.append(input_data)\n", + " \n", + " # Hidden layers output\n", + " for rbm in self.rbms:\n", + " layer_outputs.append(rbm.input_output(layer_outputs[-1]))\n", + " \n", + " # Softmax probabilities on the output layer\n", + " output_probs = self.network[-1].calcul_softmax(layer_outputs[-1])\n", + " \n", + " return layer_outputs, output_probs\n", + " \n", + "\n", + " def _cross_entropy(batch_labels: np.ndarray, output_probs: np.ndarray, eps: float = 1e-15) -> float:\n", + " \"\"\"\n", + " Calculate the cross entropy between the batch labels and output probabilities.\n", + "\n", + " Parameters:\n", + " - batch_labels (numpy.ndarray): True labels for the batch, shape (batch_size, n_classes).\n", + " - output_probs (numpy.ndarray): Predicted probabilities for the batch, shape (batch_size, n_classes).\n", + " - eps (float): Small value to avoid numerical instability in logarithm calculation. Default is 1e-15.\n", + "\n", + " Returns:\n", + " - float: Cross entropy value.\n", + " \"\"\"\n", + " return -np.mean(np.sum(batch_labels * np.log(output_probs + eps), axis=1))\n", + "\n", + "\n", + " def backpropagation(\n", + " self,\n", + " input_data: np.ndarray,\n", + " labels: np.ndarray,\n", + " n_epochs: int,\n", + " learning_rate: float,\n", + " batch_size: int,\n", + " eps: float = 1e-15\n", + " ) -> \"DNN\":\n", + " \"\"\"\n", + " Estimate the weights/biases of the network using backpropagation algorithm.\n", + "\n", + " Parameters:\n", + " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + " - labels (numpy.ndarray): Labels for the input data, shape (n_samples, n_classes).\n", + " - n_epochs (int): Number of training epochs.\n", + " - learning_rate (float): Learning rate for gradient descent.\n", + " - batch_size (int): Size of mini-batches.\n", + " - eps (float): Small value to avoid numerical instability in logarithm calculation. Default is 1e-15.\n", + "\n", + " Returns:\n", + " - DNN: Updated DNN instance.\n", + " \"\"\"\n", + " n_samples = input_data.shape[0]\n", + " \n", + " for epoch in tqdm(range(n_epochs), desc=\"Training\", unit=\"epoch\"):\n", + " for batch_start in range(0, n_samples, batch_size):\n", + " batch_end = min(batch_start + batch_size, n_samples)\n", + " batch_input = input_data[batch_start:batch_end]\n", + " batch_labels = labels[batch_start:batch_end]\n", + "\n", + " # Forward pass\n", + " layer_outputs, output_probs = self.input_output(batch_input)\n", + "\n", + " # Backward pass (update weights and biases)\n", + " self.network[-1].update(batch_labels, layer_outputs[-1], learning_rate)\n", + " for i in range(len(self.network) - 2, -1, -1):\n", + " self.network[i].update(layer_outputs[i], layer_outputs[i + 1], self.network[i + 1].weights, learning_rate)\n", + "\n", + " # Calculate cross entropy after each epoch\n", + " loss = self._cross_entropy(batch_labels, output_probs, eps)\n", + " tqdm.write(f\"Epoch {epoch + 1}/{n_epochs}, Cross Entropy: {loss}\")\n", + "\n", + " return self\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_visible=data.shape[1]\n", + "hidden_layer_sizes = [100, 50, 25]\n", + "output_dim = 20\n", + "\n", + "dnn = DNN(input_dim=n_visible, hidden_layer_sizes=hidden_layer_sizes, output_dim=output_dim, random_state=42)\n", + "# keep last RBM's weights for further test.\n", + "weights = dnn[-1].W \n", + "\n", + "dnn.train(data, learning_rate=0.1, n_epochs=10, batch_size=10)\n", + "\n", + "# Check that the last RBM has not been trained.\n", + "np.testing.assert_equal (dnn[-1].a, 0) # visible bias\n", + "np.testing.assert_equal (dnn[-1].b, 0) # hidden bias\n", + "np.testing.assert_equal (dnn[-1].W, weights) # weights" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No. network output = 5\n", + "Input data (0): (39, 320)\n", + "Hidden layer input (1): (39, 100)\n", + "Hidden layer input (2): (39, 50)\n", + "Hidden layer input (3): (39, 25)\n", + "Softmax output (4): (39, 20)\n" + ] + } + ], + "source": [ + "layer_outputs, softmax_output = dnn.input_output(data)\n", + "n_layers_net = len(layer_outputs) + 1\n", + "print(\"No. network output =\", n_layers_net)\n", + "\n", + "print(f\"Input data (0): {layer_outputs[0].shape}\")\n", + "for idx, layer_output in enumerate(layer_outputs[1:]):\n", + " print(f\"Hidden layer input ({idx+1}): {layer_output.shape}\")\n", + "\n", + "print(f\"Softmax output ({n_layers_net - 1}):\", softmax_output.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analysis on ALPHA DIGITS " + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_symmetric_configurations(min_layers, max_layers, min_neurons, max_neurons, step_neurons):\n", + " \"\"\"\n", + " Générer des configurations symétriques pour les couches cachées du DBN.\n", + "\n", + " Args:\n", + " min_layers (int): Nombre minimum de couches cachées.\n", + " max_layers (int): Nombre maximum de couches cachées.\n", + " min_neurons (int): Nombre minimum de neurones par couche.\n", + " max_neurons (int): Nombre maximum de neurones par couche.\n", + " step_neurons (int): Pas d'augmentation du nombre de neurones.\n", + "\n", + " Returns:\n", + " List[List[int]]: Liste des configurations symétriques des couches cachées.\n", + " \"\"\"\n", + " configurations = []\n", + " for num_layers in range(min_layers, max_layers + 1):\n", + " for num_neurons in range(min_neurons, max_neurons + 1, step_neurons):\n", + " half = num_layers // 2\n", + " config = [num_neurons] * half + [num_neurons] * (num_layers - half)\n", + " configurations.append(config)\n", + " return configurations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def run_experiment(hidden_layers_sizes, n_epochs=100, characters=['A', 'B', '1', '2']):\n", + " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", + "\n", + " # Déterminer le nombre maximum de couches et le nombre unique d'unités\n", + " max_layers = max(len(sizes) for sizes in hidden_layers_sizes)\n", + " unique_units = sorted({sizes[0] for sizes in hidden_layers_sizes})\n", + "\n", + " # Préparer une grille de subplots\n", + " fig, axes = plt.subplots(len(unique_units), max_layers, figsize=(max_layers * 3, len(unique_units) * 3), squeeze=False)\n", + "\n", + " # Initialiser tous les axes comme invisibles; ils seront activés lorsqu'utilisés\n", + " for ax_row in axes:\n", + " for ax in ax_row:\n", + " ax.set_visible(False)\n", + "\n", + " for layer_sizes in hidden_layers_sizes:\n", + " print(f\"\\nTraining DBN with hidden layers: {layer_sizes}\")\n", + " dbn = DBN(n_visible=data.shape[1], hidden_layer_sizes=layer_sizes, random_state=42)\n", + " dbn.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=10, print_each=1000000)\n", + "\n", + " # Génération et affichage d'une image\n", + " generated_image = dbn.generate_image(n_samples=1)\n", + " unit_idx = unique_units.index(layer_sizes[0])\n", + " layer_idx = len(layer_sizes) - 2 # Index 0 pour 2 couches, index 1 pour 3 couches, etc.\n", + "\n", + " ax = axes[unit_idx][layer_idx]\n", + " ax.set_visible(True)\n", + " ax.imshow(generated_image[0].reshape(20, 16), cmap='gray')\n", + " ax.set_title(f\"N_Layers: {len(layer_sizes)}, N_Units: {layer_sizes[0]}\")\n", + " ax.axis('off')\n", + "\n", + " # Enregistrement de l'image générée\n", + " directory = f\"../resultat/dbn/{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers\"\n", + " os.makedirs(directory, exist_ok=True)\n", + " np.save(f\"{directory}/Units_{layer_sizes[0]}_Chars_{''.join(characters)}.npy\", generated_image[0])\n", + "\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Training DBN with hidden layers: [100, 100]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 4/4 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Exemple d'utilisation avec des configurations générées\n", + "configurations = generate_symmetric_configurations(min_layers = 2, max_layers = 2, min_neurons = 100, max_neurons = 700, step_neurons = 100)\n", + "run_experiment(configurations, n_epochs=1000, characters=['Y'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_experiment(configurations, n_epochs=500, characters=['E', 'Y'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_experiment(configurations, n_epochs=500, characters=['E', 'Y', '2'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_experiment(configurations, n_epochs=500, characters=['E', 'Y', 'G', '2'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_experiment(configurations, n_epochs=500, characters=['E', 'Y', 'G', '2', '7'])" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "def run_rbm_experiment(hidden_units_sizes, n_epochs=100, character_sets=[['A', 'B'], ['1', '2', '3', '4'], ['A', 'B', '1', '2']]):\n", + " # Déterminer le nombre unique d'unités\n", + " unique_units = sorted(hidden_units_sizes)\n", + "\n", + " # Préparer une grille de subplots\n", + " fig, axes = plt.subplots(len(character_sets), len(unique_units), figsize=(len(unique_units) * 3, len(character_sets) * 3), squeeze=False)\n", + "\n", + " for row_idx, characters in enumerate(character_sets):\n", + " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", + "\n", + " for col_idx, num_units in enumerate(unique_units):\n", + " print(f\"\\nTraining RBM with {num_units} hidden units on characters {characters}\")\n", + " rbm = RBM(n_visible=data.shape[1], n_hidden=num_units, random_state=42)\n", + " rbm.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=10, print_each=5000)\n", + "\n", + " # Génération et affichage d'une image\n", + " generated_image = rbm.generate_image(n_samples=1)\n", + " ax = axes[row_idx, col_idx]\n", + " ax.imshow(generated_image[0].reshape(20, 16), cmap='plasma')\n", + " ax.set_title(f\"Units: {num_units}, N_Chars: {len(characters)}\")\n", + " ax.axis('off')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Training RBM with 100 hidden units on characters ['A', 'B']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 8/8 [00:00<00:00, 870.59it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.22267.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 8/8 [00:00<00:00, 982.70it/s]\n", + "Epoch 2: 100%|██████████| 8/8 [00:00<00:00, 632.95it/s]\n", + "Epoch 3: 100%|██████████| 8/8 [00:00<00:00, 629.82it/s]\n", + "Epoch 4: 100%|██████████| 8/8 [00:00<00:00, 693.75it/s]\n", + "Epoch 5: 100%|██████████| 8/8 [00:00<00:00, 922.94it/s]\n", + "Epoch 6: 100%|██████████| 8/8 [00:00<00:00, 831.54it/s]\n", + "Epoch 7: 100%|██████████| 8/8 [00:00<00:00, 715.60it/s]\n", + "Epoch 8: 100%|██████████| 8/8 [00:00<00:00, 728.45it/s]\n", + "Epoch 9: 100%|██████████| 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'cividis')\n", + " plt.title(\"Loaded Image\")\n", + " plt.axis('off') # Désactiver les axes pour une meilleure visualisation\n", + " plt.show()\n", + "\n", + "# Exemple d'utilisation\n", + "file_path = \"../resultat/dbn/100_Units_2_Layers/Units_100_Chars_A.npy\"\n", + "load_and_display_image(file_path)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements.txt b/requirements.txt index 011b590..b796049 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,6 @@ numpy scipy -numba \ No newline at end of file +numba +matplotlib +pandas +tqdm \ No newline at end of file diff --git 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zrRQSzkaB1bTrQ_`cupxt>Ny_G!Q*nwQa%K!iX literal 0 HcmV?d00001 From fa9d42c3906144b4faeb766933c51325d05f20c9 Mon Sep 17 00:00:00 2001 From: Yann CHOHO Date: Sat, 30 Mar 2024 16:27:49 +0100 Subject: [PATCH 10/16] chore : produce alpha digit analysis image --- notebook/experiments_ALPHA_DIGITS.ipynb | 35223 +++++++--------- notebook/gan and other.ipynb | 15267 +++++++ requirements.txt | 3 +- .../100_Units_2_Layers/Units_100_Chars_EY.npy | Bin 0 -> 2688 bytes .../Units_100_Chars_EY2.npy | Bin 0 -> 2688 bytes .../Units_100_Chars_EYG2.npy | Bin 0 -> 2688 bytes .../Units_100_Chars_EYG27.npy | Bin 0 -> 2688 bytes .../100_Units_2_Layers/Units_100_Chars_Y.npy | Bin 2688 -> 2688 bytes .../100_Units_3_Layers/Units_100_Chars_Y.npy | Bin 0 -> 2688 bytes .../100_Units_4_Layers/Units_100_Chars_Y.npy | Bin 0 -> 2688 bytes .../100_Units_5_Layers/Units_100_Chars_Y.npy | Bin 0 -> 2688 bytes .../200_Units_2_Layers/Units_200_Chars_EY.npy | Bin 0 -> 2688 bytes .../Units_200_Chars_EY2.npy | Bin 0 -> 2688 bytes 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resultat/dbn/700_Units_5_Layers/Units_700_Chars_Y.npy create mode 100644 resultat/images/dbn/dbn_1_chars_700_Units_5_Layers.png create mode 100644 resultat/images/rbm/rbm_1_chars_Units_700_Layers_['2'].png create mode 100644 resultat/images/rbm/rbm_1_chars_Units_700_Layers_['Y'].png create mode 100644 resultat/rbm/100_Units_1_Chars.npy create mode 100644 resultat/rbm/200_Units_1_Chars.npy create mode 100644 resultat/rbm/300_Units_1_Chars.npy create mode 100644 resultat/rbm/400_Units_1_Chars.npy create mode 100644 resultat/rbm/500_Units_1_Chars.npy create mode 100644 resultat/rbm/600_Units_1_Chars.npy create mode 100644 resultat/rbm/700_Units_1_Chars.npy diff --git a/notebook/experiments_ALPHA_DIGITS.ipynb b/notebook/experiments_ALPHA_DIGITS.ipynb index 81eadfe..037be4e 100644 --- a/notebook/experiments_ALPHA_DIGITS.ipynb +++ b/notebook/experiments_ALPHA_DIGITS.ipynb @@ -9,29 +9,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting tqdm\n", - " Using cached tqdm-4.66.2-py3-none-any.whl.metadata (57 kB)\n", - "Requirement already satisfied: colorama in c:\\users\\choho\\desktop\\master ds\\deep learning ii\\github\\.venv\\lib\\site-packages (from tqdm) (0.4.6)\n", - "Using cached tqdm-4.66.2-py3-none-any.whl (78 kB)\n", - "Installing collected packages: tqdm\n", - "Successfully installed tqdm-4.66.2\n" - ] - } - ], + "outputs": [], "source": [ "#FIXME: Review the generation process (theoretically) and fix the implementation " ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -147,21 +134,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 > map to > [0]\n", - "10 > map to > [10]\n", - "A > map to > [10]\n", - "[1, 'C'] > map to > [[1], [12]]\n", - "36 > no mapping available, out of range\n" - ] - } - ], + "outputs": [], "source": [ "def _map_characters_to_indices(characters: Union[str, int, List[Union[str, int]]]) -> List[int]:\n", " \"\"\"\n", @@ -199,20 +174,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "

" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def read_alpha_digit(characters: Optional[Union[str, int, List[Union[str, int]]]] = None,\n", " file_path: Optional[str] = ALPHA_DIGIT_PATH,\n", @@ -274,24 +238,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "data shape: (156, 320)\n" - ] - } - ], + "outputs": [], "source": [ "print(\"data shape:\", data.shape)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -479,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -489,1189 +445,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RBM(n_visible=320, n_hidden=200)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 557.79it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.16569.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 952.71it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 530.37it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 557.38it/s]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 530.64it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 571.37it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 999.83it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 570.96it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 1000.37it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 913.05it/s]\n", - "Epoch 10: 100%|██████████| 4/4 [00:00<00:00, 1335.13it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.13308.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 11: 100%|██████████| 4/4 [00:00<00:00, 1001.27it/s]\n", - "Epoch 12: 100%|██████████| 4/4 [00:00<00:00, 799.94it/s]\n", - "Epoch 13: 100%|██████████| 4/4 [00:00<00:00, 614.42it/s]\n", - "Epoch 14: 100%|██████████| 4/4 [00:00<00:00, 961.17it/s]\n", - "Epoch 15: 100%|██████████| 4/4 [00:00<00:00, 1332.05it/s]\n", - "Epoch 16: 100%|██████████| 4/4 [00:00<00:00, 963.05it/s]\n", - "Epoch 17: 100%|██████████| 4/4 [00:00<00:00, 858.74it/s]\n", - "Epoch 18: 100%|██████████| 4/4 [00:00<00:00, 1333.43it/s]\n", - "Epoch 19: 100%|██████████| 4/4 [00:00<00:00, 1302.98it/s]\n", - "Epoch 20: 100%|██████████| 4/4 [00:00<00:00, 1333.54it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.11447.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 21: 100%|██████████| 4/4 [00:00<00:00, 999.77it/s]\n", - "Epoch 22: 100%|██████████| 4/4 [00:00<00:00, 968.05it/s]\n", - "Epoch 23: 100%|██████████| 4/4 [00:00<00:00, 1998.72it/s]\n", - "Epoch 24: 100%|██████████| 4/4 [00:00<00:00, 885.43it/s]\n", - "Epoch 25: 100%|██████████| 4/4 [00:00<00:00, 890.89it/s]\n", - "Epoch 26: 100%|██████████| 4/4 [00:00<00:00, 798.19it/s]\n", - "Epoch 27: 100%|██████████| 4/4 [00:00<00:00, 997.93it/s]\n", - "Epoch 28: 100%|██████████| 4/4 [00:00<00:00, 613.31it/s]\n", - "Epoch 29: 100%|██████████| 4/4 [00:00<00:00, 724.72it/s]\n", - "Epoch 30: 100%|██████████| 4/4 [00:00<00:00, 1333.75it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.0752.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 31: 100%|██████████| 4/4 [00:00<00:00, 832.08it/s]\n", - "Epoch 32: 100%|██████████| 4/4 [00:00<00:00, 1140.38it/s]\n", - "Epoch 33: 100%|██████████| 4/4 [00:00<00:00, 1082.61it/s]\n", - "Epoch 34: 100%|██████████| 4/4 [00:00<00:00, 810.06it/s]\n", - "Epoch 35: 100%|██████████| 4/4 [00:00<00:00, 749.65it/s]\n", - "Epoch 36: 100%|██████████| 4/4 [00:00<00:00, 794.30it/s]\n", - "Epoch 37: 100%|██████████| 4/4 [00:00<00:00, 1226.49it/s]\n", - "Epoch 38: 100%|██████████| 4/4 [00:00<00:00, 1332.69it/s]\n", - "Epoch 39: 100%|██████████| 4/4 [00:00<00:00, 1136.51it/s]\n", - "Epoch 40: 100%|██████████| 4/4 [00:00<00:00, 1253.72it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.05648.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 41: 100%|██████████| 4/4 [00:00<00:00, 999.18it/s]\n", - "Epoch 42: 100%|██████████| 4/4 [00:00<00:00, 1093.90it/s]\n", - "Epoch 43: 100%|██████████| 4/4 [00:00<00:00, 1033.78it/s]\n", - "Epoch 44: 100%|██████████| 4/4 [00:00<00:00, 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[00:00<00:00, 1331.10it/s]\n", - "Epoch 499: 100%|██████████| 4/4 [00:00<00:00, 1998.95it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "RBM(n_visible=320, n_hidden=200)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Parameters\n", "n_visible = data.shape[1] # Number of visible units (size of each image)\n", @@ -1687,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1696,20 +472,9 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Generate samples\n", "generated_samples = rbm.generate_image(n_samples=10, n_gibbs_steps=1)\n", @@ -1732,17 +497,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RBM(n_visible=320, n_hidden=200)\n" - ] - } - ], + "outputs": [], "source": [ "print(rbm)" ] @@ -1756,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1860,114 +617,24 @@ " # Sample from the conditional probability of layer l-1 given layer l: p(h_{s-1}|h_{s}).\n", " h_probs = rbm.output_input(samples)\n", " h = self.rng.binomial(1, p=h_probs) \n", - " samples = h\n", - " return samples" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# from principal_dbn_alpha import DBN" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 467.58it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.15919.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 467.94it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 531.53it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 783.51it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1129.93it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 887.92it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 801.05it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 887.31it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 793.66it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1141.23it/s]\n", - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 4005.06it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.01702.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 49344.75it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 1997.29it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 4007.94it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 3981.30it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# # Generate images\n", "generated_images = dbn.generate_image(n_samples=5, n_gibbs_steps=1)\n", @@ -2026,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2191,22 +839,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No. network output = 5\n", - "Input data (0): (39, 320)\n", - "Hidden layer input (1): (39, 100)\n", - "Hidden layer input (2): (39, 50)\n", - "Hidden layer input (3): (39, 25)\n", - "Softmax output (4): (39, 20)\n" - ] - } - ], + "outputs": [], "source": [ "layer_outputs, softmax_output = dnn.input_output(data)\n", "n_layers_net = len(layer_outputs) + 1\n", @@ -2228,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2255,10259 +890,1093 @@ " return configurations" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.RBM launch function" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "def run_experiment(hidden_layers_sizes, n_epochs=100, characters=['A', 'B', '1', '2']):\n", - " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", - "\n", - " # Déterminer le nombre maximum de couches et le nombre unique d'unités\n", - " max_layers = max(len(sizes) for sizes in hidden_layers_sizes)\n", - " unique_units = sorted({sizes[0] for sizes in hidden_layers_sizes})\n", + "def run_rbm_experiment(hidden_units_sizes, n_epochs=100, character_sets=[['A', 'B'], ['1', '2', '3', '4'], ['A', 'B', '1', '2']]):\n", + " # Déterminer le nombre unique d'unités\n", + " unique_units = sorted(hidden_units_sizes)\n", "\n", " # Préparer une grille de subplots\n", - " fig, axes = plt.subplots(len(unique_units), max_layers, figsize=(max_layers * 3, len(unique_units) * 3), squeeze=False)\n", - "\n", - " # Initialiser tous les axes comme invisibles; ils seront activés lorsqu'utilisés\n", - " for ax_row in axes:\n", - " for ax in ax_row:\n", - " ax.set_visible(False)\n", - "\n", - " for layer_sizes in hidden_layers_sizes:\n", - " print(f\"\\nTraining DBN with hidden layers: {layer_sizes}\")\n", - " dbn = DBN(n_visible=data.shape[1], hidden_layer_sizes=layer_sizes, random_state=42)\n", - " dbn.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=10, print_each=1000000)\n", - "\n", - " # Génération et affichage d'une image\n", - " generated_image = dbn.generate_image(n_samples=1)\n", - " unit_idx = unique_units.index(layer_sizes[0])\n", - " layer_idx = len(layer_sizes) - 2 # Index 0 pour 2 couches, index 1 pour 3 couches, etc.\n", - "\n", - " ax = axes[unit_idx][layer_idx]\n", - " ax.set_visible(True)\n", - " ax.imshow(generated_image[0].reshape(20, 16), cmap='gray')\n", - " ax.set_title(f\"N_Layers: {len(layer_sizes)}, N_Units: {layer_sizes[0]}\")\n", - " ax.axis('off')\n", - "\n", - " # Enregistrement de l'image générée\n", - " directory = f\"../resultat/dbn/{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers\"\n", - " os.makedirs(directory, exist_ok=True)\n", - " np.save(f\"{directory}/Units_{layer_sizes[0]}_Chars_{''.join(characters)}.npy\", generated_image[0])\n", + " fig, axes = plt.subplots(len(character_sets), len(unique_units), figsize=(len(unique_units) * 3, len(character_sets) * 3), squeeze=False)\n", "\n", + " for row_idx, characters in enumerate(character_sets):\n", + " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Training DBN with hidden layers: [100, 100]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Exemple d'utilisation avec des configurations générées\n", - "configurations = generate_symmetric_configurations(min_layers = 2, max_layers = 2, min_neurons = 100, max_neurons = 700, step_neurons = 100)\n", - "run_experiment(configurations, n_epochs=1000, characters=['Y'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_experiment(configurations, n_epochs=500, characters=['E', 'Y'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_experiment(configurations, n_epochs=500, characters=['E', 'Y', '2'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_experiment(configurations, n_epochs=500, characters=['E', 'Y', 'G', '2'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_experiment(configurations, n_epochs=500, characters=['E', 'Y', 'G', '2', '7'])" - ] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [], - "source": [ - "def run_rbm_experiment(hidden_units_sizes, n_epochs=100, character_sets=[['A', 'B'], ['1', '2', '3', '4'], ['A', 'B', '1', '2']]):\n", - " # Déterminer le nombre unique d'unités\n", - " unique_units = sorted(hidden_units_sizes)\n", - "\n", - " # Préparer une grille de subplots\n", - " fig, axes = plt.subplots(len(character_sets), len(unique_units), figsize=(len(unique_units) * 3, len(character_sets) * 3), squeeze=False)\n", - "\n", - " for row_idx, characters in enumerate(character_sets):\n", - " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", - "\n", - " for col_idx, num_units in enumerate(unique_units):\n", - " print(f\"\\nTraining RBM with {num_units} hidden units on characters {characters}\")\n", - " rbm = RBM(n_visible=data.shape[1], n_hidden=num_units, random_state=42)\n", - " rbm.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=10, print_each=5000)\n", - "\n", - " # Génération et affichage d'une image\n", - " generated_image = rbm.generate_image(n_samples=1)\n", - " ax = axes[row_idx, col_idx]\n", - " ax.imshow(generated_image[0].reshape(20, 16), cmap='plasma')\n", - " ax.set_title(f\"Units: {num_units}, N_Chars: {len(characters)}\")\n", - " ax.axis('off')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": {}, - "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", - "Training RBM with 100 hidden units on characters ['A', 'B']\n" + "Training RBM with 400 hidden units on characters 2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 8/8 [00:00<00:00, 870.59it/s]\n" + "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 325.34it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.22267.\n" + "Reconstruction error: 0.24916.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 8/8 [00:00<00:00, 982.70it/s]\n", - "Epoch 2: 100%|██████████| 8/8 [00:00<00:00, 632.95it/s]\n", - "Epoch 3: 100%|██████████| 8/8 [00:00<00:00, 629.82it/s]\n", - "Epoch 4: 100%|██████████| 8/8 [00:00<00:00, 693.75it/s]\n", - "Epoch 5: 100%|██████████| 8/8 [00:00<00:00, 922.94it/s]\n", - "Epoch 6: 100%|██████████| 8/8 [00:00<00:00, 831.54it/s]\n", - "Epoch 7: 100%|██████████| 8/8 [00:00<00:00, 715.60it/s]\n", - "Epoch 8: 100%|██████████| 8/8 [00:00<00:00, 728.45it/s]\n", - "Epoch 9: 100%|██████████| 8/8 [00:00<00:00, 703.17it/s]\n", - "Epoch 10: 100%|██████████| 8/8 [00:00<00:00, 898.07it/s]\n", - "Epoch 11: 100%|██████████| 8/8 [00:00<00:00, 709.85it/s]\n", - 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"Training RBM with 500 hidden units on characters ['A', 'B']\n" + "Training RBM with 100 hidden units on characters Y\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 8/8 [00:00<00:00, 197.27it/s]\n" + "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 999.36it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.23119.\n" + "Reconstruction error: 0.1556.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 8/8 [00:00<00:00, 273.32it/s]\n", - "Epoch 2: 100%|██████████| 8/8 [00:00<00:00, 236.29it/s]\n", - "Epoch 3: 100%|██████████| 8/8 [00:00<00:00, 228.67it/s]\n", - "Epoch 4: 100%|██████████| 8/8 [00:00<00:00, 249.96it/s]\n", - "Epoch 5: 100%|██████████| 8/8 [00:00<00:00, 239.27it/s]\n", - "Epoch 6: 100%|██████████| 8/8 [00:00<00:00, 250.59it/s]\n", - "Epoch 7: 100%|██████████| 8/8 [00:00<00:00, 246.73it/s]\n", - "Epoch 8: 100%|██████████| 8/8 [00:00<00:00, 240.08it/s]\n", - 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DBM launch function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def run_experiment(hidden_layers_sizes, n_epochs=100, characters=['A', 'B', '1', '2']):\n", + " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", + "\n", + " # Déterminer le nombre maximum de couches et le nombre unique d'unités\n", + " max_layers = max(len(sizes) for sizes in hidden_layers_sizes)\n", + " unique_units = sorted({sizes[0] for sizes in hidden_layers_sizes})\n", + "\n", + " # Préparer une grille de subplots\n", + " fig, axes = plt.subplots(len(unique_units), max_layers, figsize=(max_layers * 3, len(unique_units) * 3), squeeze=False)\n", + "\n", + " # Initialiser tous les axes comme invisibles; ils seront activés lorsqu'utilisés\n", + " for ax_row in axes:\n", + " for ax in ax_row:\n", + " ax.set_visible(False)\n", + "\n", + " for layer_sizes in hidden_layers_sizes:\n", + " print(f\"\\nTraining DBN with hidden layers: {layer_sizes}\")\n", + " dbn = DBN(n_visible=data.shape[1], hidden_layer_sizes=layer_sizes, random_state=42)\n", + " dbn.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=16, print_each=1000000)\n", + "\n", + " # Génération et affichage d'une image\n", + " generated_image = dbn.generate_image(n_samples=1)\n", + " unit_idx = unique_units.index(layer_sizes[0])\n", + " layer_idx = len(layer_sizes) - 2 # Index 0 pour 2 couches, index 1 pour 3 couches, etc.\n", + "\n", + " ax = axes[unit_idx][layer_idx]\n", + " ax.set_visible(True)\n", + " ax.imshow(generated_image[0].reshape(20, 16), cmap='plasma')\n", + " ax.set_title(f\"N_Layers: {len(layer_sizes)}, N_Units: {layer_sizes[0]}\")\n", + " ax.axis('off')\n", + "\n", + " # Enregistrement de l'image générée\n", + " directory = f\"../resultat/dbn/{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers\"\n", + " os.makedirs(directory, exist_ok=True)\n", + " np.save(f\"{directory}/Units_{layer_sizes[0]}_Chars_{''.join(characters)}.npy\", generated_image[0])\n", + "\n", + " #save figure\n", + " plt.tight_layout()\n", + " directory_image = \"../resultat/images/dbn\"\n", + " os.makedirs(directory_image, exist_ok=True)\n", + " plt.savefig(f\"{directory_image}/dbn_{len(characters)}_chars_{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers.png\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exemple d'utilisation avec des configurations générées\n", + "configurations = generate_symmetric_configurations(min_layers = 2, max_layers = 5, min_neurons = 100, max_neurons = 700, step_neurons = 100)\n", + "run_experiment(configurations, n_epochs=1000, characters=['Y'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Effect of the number of characters on reconstruction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# configuration = 2 layer with 200 units each\n", + "configurations_fixe = generate_symmetric_configurations(min_layers = 2, max_layers = 2, min_neurons = 200, max_neurons = 200, step_neurons = 100)\n", + "\n", + "run_experiment(configurations_fixe, n_epochs=2, characters = ['Y'])\n", + "run_experiment(configurations_fixe, n_epochs=2, characters = ['E', 'Y'])\n", + "run_experiment(configurations_fixe, n_epochs=2, characters = ['E', 'Y', '2'])\n", + "run_experiment(configurations_fixe, n_epochs=2, characters = ['E', 'Y', 'G', '2'])\n", + "run_experiment(configurations_fixe, n_epochs=2, characters=['E', 'Y', 'G', '2', '7'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Exemple d'utilisation\n", "hidden_units_sizes = [100, 200, 300, 400, 500, 600, 700]\n", - "run_rbm_experiment(hidden_units_sizes, n_epochs=500, character_sets=[['A', 'B'], ['A', 'B', 'C'], ['A', 'B', 'C', 'D']])\n" + "run_rbm_experiment(hidden_units_sizes, n_epochs=2, character_sets=[['A', 'B'], ['A', 'B', 'C'], ['A', 'B', 'C', 'D']])\n" ] }, { @@ -22170,20 +15481,9 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -22208,7 +15508,10 @@ "\n", "# Exemple d'utilisation\n", "file_path = \"../resultat/dbn/100_Units_2_Layers/Units_100_Chars_A.npy\"\n", - "load_and_display_image(file_path)\n" + "load_and_display_image(file_path)\n", + "\n", + "file_path2 = \"../resultat/rbn/100_Units_2_Chars.npy\"\n", + "load_and_display_image(file_path2)" ] } ], diff --git a/notebook/gan and other.ipynb b/notebook/gan and other.ipynb new file mode 100644 index 0000000..ee2b028 --- /dev/null +++ b/notebook/gan and other.ipynb @@ -0,0 +1,15267 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Restricted Botlzman Machines (RBM)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#FIXME: Review the generation process (theoretically) and fix the implementation " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from typing import List, Dict, Tuple, Literal, Optional, Union, Iterable\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import scipy.io\n", + "from tqdm import tqdm\n", + "from numpy._typing import ArrayLike\n", + "\n", + "ArrayLike = Union[List, Tuple, np.ndarray]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "DATA_FOLDER = \"../data/\"\n", + "ALPHA_DIGIT_PATH = os.path.join(DATA_FOLDER, \"binaryalphadigs.mat\")\n", + "MNIST_PATH = os.path.join(DATA_FOLDER, \"mnist_all.mat\")\n", + "\n", + "if not os.path.exists(ALPHA_DIGIT_PATH):\n", + " raise FileNotFoundError(f\"The file {ALPHA_DIGIT_PATH} does not exist.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Implementing a RBM and testing on Binary AlphaDigits" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Class LabelsClass Count
0039
1139
2239
3339
4439
5539
6639
7739
8839
9939
10A39
11B39
12C39
13D39
14E39
15F39
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22M39
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29T39
30U39
31V39
32W39
33X39
34Y39
35Z39
\n", + "
" + ], + "text/plain": [ + " Class Labels Class Count\n", + "0 0 39\n", + "1 1 39\n", + "2 2 39\n", + "3 3 39\n", + "4 4 39\n", + "5 5 39\n", + "6 6 39\n", + "7 7 39\n", + "8 8 39\n", + "9 9 39\n", + "10 A 39\n", + "11 B 39\n", + "12 C 39\n", + "13 D 39\n", + "14 E 39\n", + "15 F 39\n", + "16 G 39\n", + "17 H 39\n", + "18 I 39\n", + "19 J 39\n", + "20 K 39\n", + "21 L 39\n", + "22 M 39\n", + "23 N 39\n", + "24 O 39\n", + "25 P 39\n", + "26 Q 39\n", + "27 R 39\n", + "28 S 39\n", + "29 T 39\n", + "30 U 39\n", + "31 V 39\n", + "32 W 39\n", + "33 X 39\n", + "34 Y 39\n", + "35 Z 39" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def _load_data(file_path: str) -> Dict[str, np.ndarray]:\n", + " \"\"\"\n", + " Load Binary AlphaDigits data from a .mat file.\n", + "\n", + " Parameters:\n", + " - file_path (str): Path to the .mat file containing the data.\n", + "\n", + " Returns:\n", + " - data (dict): Loaded data dictionary.\n", + " \"\"\"\n", + " if file_path is None:\n", + " raise ValueError(\"File path must be provided.\")\n", + "\n", + " return scipy.io.loadmat(file_path)\n", + "\n", + "\n", + "data = _load_data(ALPHA_DIGIT_PATH)\n", + "class_labels = data[\"classlabels\"].flatten() \n", + "class_count = data[\"classcounts\"].flatten()\n", + "df = pd.DataFrame(\n", + " {\n", + " \"Class Labels\": class_labels,\n", + " \"Class Count\": class_count\n", + " }\n", + ")\n", + "df[\"Class Labels\"] = df[\"Class Labels\"].apply(lambda x: x[0])\n", + "df[\"Class Count\"] = df[\"Class Count\"].apply(lambda x: x[0][0])\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(36, 39)\n", + "(20, 16)\n" + ] + } + ], + "source": [ + "def _load_data(file_path: str, which: Literal[\"alphadigit\", \"mnist\"]=\"alphadigit\") -> Dict[str, np.ndarray]:\n", + " \"\"\"\n", + " Load Binary AlphaDigits data from a .mat file.\n", + "\n", + " Parameters:\n", + " - file_path (str): Path to the .mat file containing the data.\n", + " - which (Literal[\"alphadigit\", \"mnist\"], optional): Specifies \n", + " which data to load. The default value is \"alphadigit\".\n", + "\n", + " Returns:\n", + " - data (dict): A dictionary containing the loaded data.\n", + "\n", + " Raises:\n", + " - ValueError: If the file_path parameter is None.\n", + " - ValueError: If the which parameter is not \"alphadigit\".\n", + "\n", + " Example Usage:\n", + " ```python\n", + " data = _load_data(\"data.mat\", \"alphadigit\")\n", + " ```\n", + " \"\"\"\n", + " if file_path is None:\n", + " raise ValueError(\"File path must be provided.\")\n", + " \n", + " if which == \"alphadigit\":\n", + " return scipy.io.loadmat(file_path)[\"dat\"]\n", + " \n", + " raise ValueError(\"MNIST NOT YET AVAILABLE.\")\n", + "\n", + "alphadigit_data = _load_data(ALPHA_DIGIT_PATH) \n", + "print(alphadigit_data.shape)\n", + "print(alphadigit_data[0][0].shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 > map to > [0]\n", + "10 > map to > [10]\n", + "A > map to > [10]\n", + "[1, 'C'] > map to > [[1], [12]]\n", + "36 > no mapping available, out of range\n" + ] + } + ], + "source": [ + "def _map_characters_to_indices(characters: Union[str, int, List[Union[str, int]]]) -> List[int]:\n", + " \"\"\"\n", + " Map alphanumeric character to its corresponding index.\n", + "\n", + " Parameters:\n", + " - character (str, int, list of str or int): Alphanumeric character or its index.\n", + "\n", + " Returns:\n", + " - char_index (int): Corresponding index for the character.\n", + " \"\"\"\n", + " if isinstance(characters, list):\n", + " return [_map_characters_to_indices(char) for char in characters]\n", + " if isinstance(characters, int) and 0 <= characters <= 35:\n", + " return [characters]\n", + " if (isinstance(characters, str) and characters.isdigit()\n", + " and 0 <= int(characters) <= 9):\n", + " return [int(characters)]\n", + " if (isinstance(characters, str) and characters.isalpha()\n", + " and 'A' <= characters.upper() <= 'Z'):\n", + " return [ord(characters.upper()) - ord('A') + 10]\n", + " \n", + " raise ValueError(\n", + " \"Invalid character input. It should be an alphanumeric\" \n", + " \"character '[0-9|A-Z]' or its index representing '[0-35]'.\"\n", + " )\n", + "\n", + "for char in [0, 10, \"A\", [1, \"C\"], 36]:\n", + " try:\n", + " map = _map_characters_to_indices(char)\n", + " print(f\"{char} > map to > {map}\")\n", + " except:\n", + " print(f\"{char} > no mapping available, out of range\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def read_alpha_digit(characters: Optional[Union[str, int, List[Union[str, int]]]] = None,\n", + " file_path: Optional[str] = ALPHA_DIGIT_PATH,\n", + " data: Optional[Dict[str, np.ndarray]] = None,\n", + " use_data: bool = False,\n", + " ) -> np.ndarray:\n", + " \"\"\"\n", + " Reads binary AlphaDigits data from a .mat file or uses already loaded data. \n", + " It extracts the data for a specified alphanumeric character or its index, and \n", + " flattens the images into one-dimensional vectors.\n", + "\n", + " Parameters:\n", + " - characters (Union[str, int, List[Union[str, int]]], optional): Alphanumeric character \n", + " or its index whose data needs to be extracted. It can be a single character or \n", + " a list of characters. Default is None.\n", + " - file_path (str, optional): Path to the .mat file containing the data. \n", + " Default is None.\n", + " - data (dict, optional): Already loaded data dictionary. \n", + " Default is None.\n", + " - use_data (bool): Flag to indicate whether to use already loaded data.\n", + " Default is False.\n", + "\n", + " Returns:\n", + " - flattened_images (numpy.ndarray): Flattened images for the specified character(s).\n", + " \"\"\"\n", + " if not use_data:\n", + " data = _load_data(file_path, which=\"alphadigit\")\n", + "\n", + " char_indices = _map_characters_to_indices(characters)\n", + "\n", + " # Select the rows corresponding to the characters indices.\n", + " char_data: np.ndarray = data[char_indices]\n", + " \n", + " # Flatten each image into a one-dimensional vector.\n", + " flattened_images = np.array([image.flatten() for image in char_data.flatten()])\n", + " return flattened_images\n", + "\n", + "def plot_characters(chars, data):\n", + " num_chars = len(chars)\n", + " num_images_per_char = data.shape[0] // num_chars\n", + " fig, ax = plt.subplots(1, num_chars, figsize=(num_chars * 2, 2))\n", + "\n", + " for i, char in enumerate(chars):\n", + " # Find the index of the first image corresponding to the current char\n", + " start_index = i * num_images_per_char\n", + " image = data[start_index].reshape(20, 16)\n", + " ax[i].imshow(image, cmap='gray')\n", + " ax[i].set_title(f'Char: {char}')\n", + " ax[i].axis('off')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# Example\n", + "chars = [0, \"K\", 7, \"Z\"]\n", + "data = read_alpha_digit(chars, data=alphadigit_data, use_data=True)\n", + "plot_characters(chars, data)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data shape: (156, 320)\n" + ] + } + ], + "source": [ + "print(\"data shape:\", data.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "class RBM:\n", + " def __init__(self, n_visible: int, n_hidden: int=100, random_state=None) -> None:\n", + " \"\"\"\n", + " Initialize the Restricted Boltzmann Machine.\n", + "\n", + " Parameters:\n", + " - n_visible (int): Number of visible units.\n", + " - n_hidden (int): Number of hidden units. Default 100.\n", + " - random_state: Random seed for reproducibility.\n", + " \"\"\"\n", + " self.n_visible = n_visible\n", + " self.n_hidden = n_hidden\n", + " \n", + " self.a = np.zeros((1, n_visible)) # visible_bias\n", + " self.b = np.zeros((1, n_hidden)) # hidden_bias\n", + " self.rng = np.random.default_rng(random_state)\n", + " self.W = 1e-4 * self.rng.standard_normal(size=(n_visible, n_hidden)) # weights\n", + "\n", + " def __repr__(self) -> str:\n", + " return f\"RBM(n_visible={self.n_visible}, n_hidden={self.n_hidden})\"\n", + "\n", + " def _sigmoid(self, x: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Sigmoid activation function.\n", + "\n", + " Parameters:\n", + " - x (numpy.ndarray): Input array.\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Result of applying the sigmoid function to the input.\n", + " \"\"\"\n", + " return 1 / (1 + np.exp(-x))\n", + " \n", + " def _reconstruction_error(self, input: np.ndarray, image: np.ndarray) -> float:\n", + " \"\"\"\n", + " Compute reconstruction error.\n", + "\n", + " Parameters:\n", + " - input (numpy.ndarray): Original input data.\n", + " - image (numpy.ndarray): Reconstructed image.\n", + "\n", + " Returns:\n", + " - float: Reconstruction error.\n", + " \"\"\"\n", + " return np.round(np.power(image - input, 2).mean(), 5)\n", + "\n", + " def input_output(self, data: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Compute hidden units given visible units.\n", + "\n", + " Parameters:\n", + " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Hidden unit activations, shape (n_samples, n_hidden).\n", + " \"\"\"\n", + " return self._sigmoid(data @ self.W + self.b)\n", + "\n", + " def output_input(self, data_h: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Compute visible units given hidden units.\n", + "\n", + " Parameters:\n", + " - data_h (numpy.ndarray): Hidden unit activations, shape (n_samples, n_hidden).\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Reconstructed visible units, shape (n_samples, n_visible).\n", + " \"\"\"\n", + " return self._sigmoid(data_h @ self.W.T + self.a)\n", + " \n", + " def calcul_softmax(self, data: np.ndarray) -> np.ndarray:\n", + " \"\"\"\n", + " Calculate softmax probabilities for the output units.\n", + "\n", + " Parameters:\n", + " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Softmax probabilities, shape (n_samples, n_hidden).\n", + " \"\"\"\n", + " # Compute activations for the hidden layer\n", + " hidden_activations = self.input_output(data)\n", + " \n", + " # Compute softmax probabilities for the output layer\n", + " exp_hidden_activations = np.exp(hidden_activations)\n", + " softmax_probs = exp_hidden_activations / np.sum(exp_hidden_activations, axis=1, keepdims=True)\n", + " \n", + " return softmax_probs\n", + "\n", + " def update(\n", + " self, \n", + " batch: np.ndarray,\n", + " learning_rate: float=0.1,\n", + " batch_size: Optional[int]=None,\n", + " return_output: bool=False\n", + " ):\n", + " \"\"\"_summary_\n", + "\n", + " Args:\n", + " batch (np.ndarray): _description_\n", + " learning_rate (float, optional): _description_. Defaults to 0.1.\n", + " batch_size (Optional[int], optional): _description_. Defaults to None.\n", + " return_output (bool, optional): _description_. Defaults to False.\n", + " \"\"\"\n", + " if not batch_size:\n", + " batch_size = batch.shape[0]\n", + " pos_h_probs = self.input_output(batch)\n", + " pos_v_probs = self.output_input(pos_h_probs)\n", + " neg_h_probs = self.input_output(pos_v_probs)\n", + " \n", + " # Update weights and biases\n", + " self.W += learning_rate * (batch.T @ pos_h_probs - pos_v_probs.T @ neg_h_probs) / batch_size\n", + " self.b += learning_rate * (pos_h_probs - neg_h_probs).mean(axis=0)\n", + " self.a += learning_rate * (batch - pos_v_probs).mean(axis=0)\n", + "\n", + " if return_output:\n", + " return self, pos_v_probs\n", + " \n", + " return self \n", + "\n", + " def train(self, \n", + " data: np.ndarray,\n", + " learning_rate: float=0.1,\n", + " n_epochs: int=10,\n", + " batch_size: int=10,\n", + " print_each=10\n", + " ) -> 'RBM':\n", + " \"\"\"\n", + " Train the RBM using Contrastive Divergence.\n", + "\n", + " Parameters:\n", + " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", + " - n_epochs (int): Number of training epochs. Default is 10.\n", + " - batch_size (int): Size of mini-batches. Default is 10.\n", + "\n", + " Returns:\n", + " - RBM: Trained RBM instance.\n", + " \"\"\"\n", + " n_samples = data.shape[0]\n", + " for epoch in range(n_epochs):\n", + " self.rng.shuffle(data)\n", + " for i in tqdm(range(0, n_samples, batch_size), desc=f\"Epoch {epoch}\"):\n", + " batch = data[i:i+batch_size]\n", + " _, pos_v_probs = self.update(\n", + " batch=batch,\n", + " learning_rate=learning_rate,\n", + " batch_size=batch_size,\n", + " return_output=True\n", + " )\n", + " \n", + " if epoch % print_each == 0:\n", + " tqdm.write(\n", + " f\"Reconstruction error: {self._reconstruction_error(batch, pos_v_probs)}.\")\n", + "\n", + " return self\n", + "\n", + " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", + " \"\"\"\n", + " Generate samples from the RBM using Gibbs sampling.\n", + "\n", + " Parameters:\n", + " - n_samples (int): Number of samples to generate. Default is 10.\n", + " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 1.\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", + " \"\"\"\n", + " samples = np.zeros((n_samples, self.n_visible))\n", + " \n", + " # Matrix of initlization value of Gibbs samples for each sample. \n", + " V = self.rng.binomial(1, self.rng.random(), size=n_samples*self.n_visible).reshape((n_samples, self.n_visible))\n", + " for i in range(n_samples):\n", + " for _ in range(n_gibbs_steps):\n", + " h_probs = self._sigmoid(V[i] @ self.W + self.b) # vector\n", + " h = self.rng.binomial(1, h_probs)\n", + " v_probs = self._sigmoid(h @ self.W.T + self.a)\n", + " v = self.rng.binomial(1, v_probs)\n", + " samples[i] = v\n", + " return samples" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the alpha_digit data\n", + "data = read_alpha_digit(file_path=ALPHA_DIGIT_PATH, characters=['Z'])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RBM(n_visible=320, n_hidden=200)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 571.10it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.16569.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 504.18it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 531.21it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 481.33it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 589.29it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 472.40it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 416.17it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 480.92it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 593.11it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 438.05it/s]\n", + "Epoch 10: 100%|██████████| 4/4 [00:00<00:00, 561.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.13308.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 11: 100%|██████████| 4/4 [00:00<00:00, 558.12it/s]\n", + "Epoch 12: 100%|██████████| 4/4 [00:00<00:00, 400.77it/s]\n", + "Epoch 13: 100%|██████████| 4/4 [00:00<00:00, 501.37it/s]\n", + "Epoch 14: 100%|██████████| 4/4 [00:00<00:00, 528.42it/s]\n", + "Epoch 15: 100%|██████████| 4/4 [00:00<00:00, 429.48it/s]\n", + "Epoch 16: 100%|██████████| 4/4 [00:00<00:00, 603.24it/s]\n", + "Epoch 17: 100%|██████████| 4/4 [00:00<00:00, 562.71it/s]\n", + "Epoch 18: 100%|██████████| 4/4 [00:00<00:00, 466.09it/s]\n", + "Epoch 19: 100%|██████████| 4/4 [00:00<00:00, 568.97it/s]\n", + "Epoch 20: 0%| | 0/4 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate samples\n", + "generated_samples = rbm.generate_image(n_samples=10, n_gibbs_steps=1)\n", + "\n", + "# Plot original and generated samples\n", + "plt.figure(figsize=(12, 6))\n", + "for i in range(10):\n", + " plt.subplot(2, 10, i + 1)\n", + " plt.imshow(data[i].reshape(20, 16), cmap='gray')\n", + " plt.title('Original')\n", + " plt.axis('off')\n", + " \n", + " plt.subplot(2, 10, i + 11)\n", + " plt.imshow(generated_samples[i].reshape(20, 16), cmap='gray')\n", + " plt.title('Generated')\n", + " plt.axis('off')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RBM(n_visible=320, n_hidden=200)\n" + ] + } + ], + "source": [ + "print(rbm)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Implementing a Deep Belief Network (DBN) and test on Binary AlphaDigits" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "class DBN:\n", + " def __init__(self, n_visible: int, hidden_layer_sizes: list[int], random_state=None):\n", + " \"\"\"\n", + " Initialize the Deep Belief Network.\n", + "\n", + " Parameters:\n", + " - n_visible (int): Number of visible units.\n", + " - hidden_layer_sizes (list[int]): List of sizes for each hidden layer.\n", + " - random_state: Random seed for reproducibility.\n", + " \"\"\"\n", + " self.n_visible = n_visible\n", + " self.hidden_layer_sizes = hidden_layer_sizes\n", + " self.rbms: List[RBM] = []\n", + " self.rng = np.random.default_rng(random_state)\n", + "\n", + " # Initialize the first RBM\n", + " first_rbm = RBM(\n", + " n_visible=n_visible,\n", + " n_hidden=hidden_layer_sizes[0],\n", + " random_state=random_state,\n", + " )\n", + " self.rbms.append(first_rbm)\n", + "\n", + " # Initialize RBMs for subsequent hidden layers\n", + " for i, size in enumerate(hidden_layer_sizes[1:], start=1):\n", + " rbm = RBM(\n", + " n_visible=hidden_layer_sizes[i - 1],\n", + " n_hidden=size,\n", + " random_state=random_state,\n", + " )\n", + " self.rbms.append(rbm)\n", + "\n", + "\n", + " def __getitem__(self, key):\n", + " return self.rbms[key]\n", + " \n", + "\n", + " def __repr__(self):\n", + " \"\"\"\n", + " Return a string representation of the DBN object.\n", + " \"\"\"\n", + " rbm_reprs = [f\"{'':4}{repr(rbm)}\" for rbm in self.rbms]\n", + " join_rbm_reprs = ',\\n'.join(rbm_reprs)\n", + " return f\"DBN([\\n{join_rbm_reprs}\\n])\"\n", + "\n", + "\n", + " def train(self,\n", + " data: np.ndarray,\n", + " learning_rate: float=0.1,\n", + " n_epochs: int=10,\n", + " batch_size: int=10,\n", + " print_each: int=10,\n", + " ) -> \"DBN\":\n", + " \"\"\"\n", + " Train the DBN using Greedy layer-wise procedure.\n", + "\n", + " Parameters:\n", + " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", + " - n_epochs (int): Number of training epochs. Default is 10.\n", + " - batch_size (int): Size of mini-batches. Default is 10.\n", + " - print_each: Print reconstruction error each `print_each` epochs.\n", + " - verbose\n", + "\n", + " Returns:\n", + " - DBN: Trained DBN instance.\n", + " \"\"\"\n", + " input_data = data\n", + " for rbm in self.rbms:\n", + " rbm.train(\n", + " input_data,\n", + " learning_rate=learning_rate,\n", + " n_epochs=n_epochs,\n", + " batch_size=batch_size,\n", + " print_each=print_each,\n", + " )\n", + " # Update input data for the next RBM\n", + " input_data = rbm.input_output(input_data)\n", + "\n", + " return self\n", + "\n", + " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", + " \"\"\"\n", + " Generate samples from the DBN using Gibbs sampling.\n", + "\n", + " Parameters:\n", + " - n_samples (int): Number of samples to generate. Default is 1.\n", + " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100.\n", + "\n", + " Returns:\n", + " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", + " \"\"\"\n", + " # samples = np.zeros((n_samples, self.n_visible))\n", + "\n", + " # Generate samples using the first RBM in the DBN\n", + " samples = self.rbms[-1].generate_image(n_samples, n_gibbs_steps)\n", + " for rbm in reversed(self.rbms[:-1]):\n", + " # Sample from the conditional probability of layer l-1 given layer l: p(h_{s-1}|h_{s}).\n", + " h_probs = rbm.output_input(samples)\n", + " h = self.rng.binomial(1, p=h_probs) \n", + " samples = h\n", + " return samples" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# from principal_dbn_alpha import DBN" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 1330.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.15919.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 1757.70it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 981.93it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 1333.43it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1231.90it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 2267.80it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 1332.48it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 958.04it/s]\n", + "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 1333.64it/s]\n", + "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1331.42it/s]\n", + "Epoch 0: 100%|██████████| 4/4 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# # Generate images\n", + "generated_images = dbn.generate_image(n_samples=5, n_gibbs_steps=1)\n", + "\n", + "# Display generated images\n", + "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(12, 4))\n", + "for i in range(5):\n", + " axes[i].imshow(generated_images[i].reshape(20, 16), cmap='gray')\n", + " axes[i].set_title(f\"Image {i+1}\")\n", + " axes[i].axis('off')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "class DNN(DBN):\n", + " def __init__(\n", + " self,\n", + " input_dim: int,\n", + " output_dim: int,\n", + " hidden_layer_sizes: List[int],\n", + " random_state=None\n", + " ):\n", + " \"\"\"\n", + " Initialize the Deep Neural Network (DNN).\n", + "\n", + " Parameters:\n", + " - input_dim (int): Dimension of the input.\n", + " - output_dim (int): Dimension of the output.\n", + " - hidden_layer_sizes (List[int]): List of sizes for each hidden layer.\n", + " - random_state: Random seed for reproducibility.\n", + " \"\"\"\n", + " super().__init__(\n", + " n_visible=input_dim,\n", + " hidden_layer_sizes=hidden_layer_sizes,\n", + " random_state=random_state\n", + " )\n", + " #--> self.rbms contains only the pre-trainable RBMs \n", + " self.clf = RBM(self.rbms[-1].n_hidden, output_dim)\n", + " self.network = self.rbms + [self.clf] # DNN = [DBN + Classifier] ~ [RBM_0,...,RBM_N, RBM_Clf]\n", + "\n", + " def __getitem__(self, key):\n", + " return self.network[key]\n", + " \n", + " def __repr__(self):\n", + " join_repr = \"\\n\".join([f\"{'':4}{repr(rbm)},\" for rbm in self.network])\n", + " return f\"DNN([\\n{join_repr} \\n])\"\n", + " \n", + " \n", + " def pretrain(self, n_epochs: int, learning_rate: float, batch_size: int, data: np.ndarray) -> \"DNN\":\n", + " \"\"\"\n", + " Pretrain the hidden layers of the DNN using the DBN training method.\n", + "\n", + " Parameters:\n", + " - n_epochs (int): Number of training epochs.\n", + " - learning_rate (float): Learning rate for gradient descent.\n", + " - batch_size (int): Size of mini-batches.\n", + " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + "\n", + " Returns:\n", + " - DNN: Pretrained DNN instance.\n", + " \"\"\"\n", + " # NOTE: Use the inherited `train` method to perform pre-training since `self.rbms`\n", + " # only contains the pre-trainable RBMs.\n", + " return self.train(data, n_epochs=n_epochs, learning_rate=learning_rate, batch_size=batch_size)\n", + " \n", + " def input_output(self, input_data: np.ndarray) -> Tuple[List[np.ndarray], np.ndarray]:\n", + " \"\"\"\n", + " Get the outputs on each layer of the DNN and the softmax probabilities on the output layer.\n", + "\n", + " Parameters:\n", + " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + "\n", + " Returns:\n", + " - Tuple[List[np.ndarray], np.ndarray]: Outputs on each layer & softmax probabilities.\n", + " \"\"\"\n", + " layer_outputs = []\n", + " \n", + " # Input layer output\n", + " layer_outputs.append(input_data)\n", + " \n", + " # Hidden layers output\n", + " for rbm in self.rbms:\n", + " layer_outputs.append(rbm.input_output(layer_outputs[-1]))\n", + " \n", + " # Softmax probabilities on the output layer\n", + " output_probs = self.network[-1].calcul_softmax(layer_outputs[-1])\n", + " \n", + " return layer_outputs, output_probs\n", + " \n", + "\n", + " def _cross_entropy(batch_labels: np.ndarray, output_probs: np.ndarray, eps: float = 1e-15) -> float:\n", + " \"\"\"\n", + " Calculate the cross entropy between the batch labels and output probabilities.\n", + "\n", + " Parameters:\n", + " - batch_labels (numpy.ndarray): True labels for the batch, shape (batch_size, n_classes).\n", + " - output_probs (numpy.ndarray): Predicted probabilities for the batch, shape (batch_size, n_classes).\n", + " - eps (float): Small value to avoid numerical instability in logarithm calculation. Default is 1e-15.\n", + "\n", + " Returns:\n", + " - float: Cross entropy value.\n", + " \"\"\"\n", + " return -np.mean(np.sum(batch_labels * np.log(output_probs + eps), axis=1))\n", + "\n", + "\n", + " def backpropagation(\n", + " self,\n", + " input_data: np.ndarray,\n", + " labels: np.ndarray,\n", + " n_epochs: int,\n", + " learning_rate: float,\n", + " batch_size: int,\n", + " eps: float = 1e-15\n", + " ) -> \"DNN\":\n", + " \"\"\"\n", + " Estimate the weights/biases of the network using backpropagation algorithm.\n", + "\n", + " Parameters:\n", + " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", + " - labels (numpy.ndarray): Labels for the input data, shape (n_samples, n_classes).\n", + " - n_epochs (int): Number of training epochs.\n", + " - learning_rate (float): Learning rate for gradient descent.\n", + " - batch_size (int): Size of mini-batches.\n", + " - eps (float): Small value to avoid numerical instability in logarithm calculation. Default is 1e-15.\n", + "\n", + " Returns:\n", + " - DNN: Updated DNN instance.\n", + " \"\"\"\n", + " n_samples = input_data.shape[0]\n", + " \n", + " for epoch in tqdm(range(n_epochs), desc=\"Training\", unit=\"epoch\"):\n", + " for batch_start in range(0, n_samples, batch_size):\n", + " batch_end = min(batch_start + batch_size, n_samples)\n", + " batch_input = input_data[batch_start:batch_end]\n", + " batch_labels = labels[batch_start:batch_end]\n", + "\n", + " # Forward pass\n", + " layer_outputs, output_probs = self.input_output(batch_input)\n", + "\n", + " # Backward pass (update weights and biases)\n", + " self.network[-1].update(batch_labels, layer_outputs[-1], learning_rate)\n", + " for i in range(len(self.network) - 2, -1, -1):\n", + " self.network[i].update(layer_outputs[i], layer_outputs[i + 1], self.network[i + 1].weights, learning_rate)\n", + "\n", + " # Calculate cross entropy after each epoch\n", + " loss = self._cross_entropy(batch_labels, output_probs, eps)\n", + " tqdm.write(f\"Epoch {epoch + 1}/{n_epochs}, Cross Entropy: {loss}\")\n", + "\n", + " return self\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 446.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.18317.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 3997.43it/s]\n", + "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 2144.88it/s]\n", + "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 2020.62it/s]\n", + "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 2675.79it/s]\n", + "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 2564.15it/s]\n", + "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 5282.50it/s]\n", + "Epoch 7: 100%|██████████| 4/4 [00:00=4.8.0 (from torch)\n", + " Using cached 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"source": [ + "#!pip install torch torchvision\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import numpy as np\n", + "from torch.autograd import Variable\n", + "import torchvision.transforms as transforms\n", + "import torchvision.datasets as datasets\n", + "from torchvision.utils import save_image\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the Generator network\n", + "class Generator(nn.Module):\n", + " def __init__(self, z_dim, img_shape):\n", + " super(Generator, self).__init__()\n", + " self.img_shape = img_shape\n", + " self.model = nn.Sequential(\n", + " nn.Linear(z_dim, 256),\n", + " nn.LeakyReLU(0.2),\n", + " nn.Linear(256, 512),\n", + " nn.LeakyReLU(0.2),\n", + " nn.Linear(512, int(np.prod(img_shape))),\n", + " nn.Tanh()\n", + " )\n", + "\n", + " def forward(self, z):\n", + " img = self.model(z)\n", + " img = img.view(img.size(0), *self.img_shape)\n", + " return img\n", + "\n", + "# Define the Discriminator network\n", + "class Discriminator(nn.Module):\n", + " def __init__(self, img_shape):\n", + " super(Discriminator, self).__init__()\n", + " self.model = nn.Sequential(\n", + " nn.Linear(int(np.prod(img_shape)), 512),\n", + " nn.LeakyReLU(0.2),\n", + " nn.Linear(512, 256),\n", + " nn.LeakyReLU(0.2),\n", + " nn.Linear(256, 1),\n", + " nn.Sigmoid()\n", + " )\n", + "\n", + " def forward(self, img):\n", + " flattened = img.view(img.size(0), -1)\n", + " validity = self.model(flattened)\n", + " return validity\n", + "\n", + "class GAN():\n", + " def __init__(self, dataset_name='mnist'):\n", + " # Load data\n", + " self.img_shape = (1, 64, 64) # for MNIST\n", + " self.z_dim = 100\n", + " self.dataset_name = dataset_name\n", + " self.model_file = f'models/{self.dataset_name}_gan_model.pickle'\n", + "\n", + " # Define networks\n", + " self.generator = Generator(self.z_dim, self.img_shape)\n", + " self.discriminator = Discriminator(self.img_shape)\n", + "\n", + " # Optimizers\n", + " self.optimizer_G = optim.Adam(self.generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n", + " self.optimizer_D = optim.Adam(self.discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n", + "\n", + " # Loss function\n", + " self.adversarial_loss = nn.BCELoss()\n", + "\n", + " def load_gan_data(self):\n", + " # MNIST Dataset\n", + " transform = transforms.Compose([\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.5], [0.5])\n", + " ])\n", + "\n", + " dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)\n", + " dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True)\n", + " return dataloader\n", + "\n", + " def train(self, epochs, train_loader, sample_interval=1000):\n", + " for epoch in range(epochs):\n", + " for i, (imgs, _) in enumerate(train_loader):\n", + " # Adversarial ground truths\n", + " valid = Variable(torch.FloatTensor(imgs.size(0), 1).fill_(1.0), requires_grad=False)\n", + " fake = Variable(torch.FloatTensor(imgs.size(0), 1).fill_(0.0), requires_grad=False)\n", + "\n", + " # Configure input\n", + " real_imgs = Variable(imgs.type(torch.FloatTensor))\n", + "\n", + " # -----------------\n", + " # Train Generator\n", + " # -----------------\n", + " self.optimizer_G.zero_grad()\n", + "\n", + " # Sample noise as generator input\n", + " z = Variable(torch.FloatTensor(np.random.normal(0, 1, (imgs.size(0), self.z_dim))))\n", + "\n", + " # Generate a batch of images\n", + " gen_imgs = self.generator(z)\n", + "\n", + " # Loss measures generator's ability to fool the discriminator\n", + " g_loss = self.adversarial_loss(self.discriminator(gen_imgs), valid)\n", + "\n", + " g_loss.backward()\n", + " self.optimizer_G.step()\n", + "\n", + " # ---------------------\n", + " # Train Discriminator\n", + " # ---------------------\n", + " self.optimizer_D.zero_grad()\n", + "\n", + " # Measure discriminator's ability to classify real from generated samples\n", + " real_loss = self.adversarial_loss(self.discriminator(real_imgs), valid)\n", + " fake_loss = self.adversarial_loss(self.discriminator(gen_imgs.detach()), fake)\n", + " d_loss = (real_loss + fake_loss) / 2\n", + "\n", + " d_loss.backward()\n", + " self.optimizer_D.step()\n", + "\n", + " print(f\"[Epoch {epoch}/{epochs}] [Batch {i}/{len(train_loader)}] [D loss: {d_loss.item()}] [G loss: {g_loss.item()}]\")\n", + "\n", + " # If at save interval => save generated image samples and model checkpoints\n", + " if i % sample_interval == 0:\n", + " # Save image samples\n", + " # Save model checkpoints \n", + " self.save_sample_images(epoch, i)\n", + " torch.save(self.generator.state_dict(), f'results/generator_epoch{epoch}_batch{i}.pth')\n", + " torch.save(self.discriminator.state_dict(), f'results/discriminator_epoch{epoch}_batch{i}.pth') \n", + " \n", + " def save_sample_images(self, epoch, batch):\n", + " # Generate noise\n", + " z = Variable(torch.FloatTensor(np.random.normal(0, 1, (25, self.z_dim))))\n", + "\n", + " # Generate images from noise\n", + " gen_imgs = self.generator(z).detach()\n", + "\n", + " # Rescale images from [-1, 1] to [0, 1] range\n", + " gen_imgs = (gen_imgs + 1) / 2\n", + "\n", + " # Save image grid\n", + " save_image(gen_imgs.data, f'results/epoch{epoch}_batch{batch}.png', nrow=5, normalize=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_set_data(set_set):\n", + " \"\"\"\n", + " Prepare the image tensors and labels from a given dataset.\n", + "\n", + " Parameters:\n", + " set_set (list): The dataset containing images and labels.\n", + "\n", + " Returns:\n", + " set_img_tensors (torch.Tensor): The image tensors of the dataset.\n", + " set_labels (torch.Tensor): The labels of the dataset.\n", + " \"\"\"\n", + " set_images = []\n", + " set_labels = []\n", + "\n", + " # Separate the labels and images from the dataset\n", + " for i in range(len(set_set)):\n", + " set_images.append(set_set[i][0])\n", + " set_labels.append(int(set_set[i][1][-2:]) - 1) # Subtract 1 here (so 0 corresponds to 01, 1 to 02, etc.) because the loss function expects 0-starting labels\n", + "\n", + " set_img_tensors = torch.tensor(set_images, dtype=torch.float32).permute(0, 3, 1, 2)\n", + " set_labels = torch.tensor(set_labels, dtype=torch.long)\n", + "\n", + " return set_img_tensors, set_labels\n", + "\n", + "\n", + "# Put into dataloader because we will use mini-batch gradient descent\n", + "\n", + "# TRAINING SET\n", + "train_dataset = TensorDataset(train_img_tensors, train_labels)\n", + "train_loader = DataLoader(dataset=train_dataset, batch_size=100, shuffle=True)\n", + "\n", + "# TEST SET\n", + "test_dataset = TensorDataset(test_img_tensors, test_labels)\n", + "test_loader = DataLoader(dataset=test_dataset, batch_size=100, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Epoch 0/1] [Batch 0/12000] [D loss: 1.0838191509246826] [G loss: 0.6863428950309753]\n", + "[Epoch 0/1] [Batch 1/12000] [D loss: 0.5735496878623962] [G loss: 0.7067567706108093]\n", + "[Epoch 0/1] [Batch 2/12000] [D loss: 0.43297380208969116] [G loss: 0.7209208607673645]\n", + "[Epoch 0/1] [Batch 3/12000] [D loss: 0.4233285188674927] [G loss: 0.7485953569412231]\n", + "[Epoch 0/1] [Batch 4/12000] [D loss: 0.39475032687187195] [G loss: 0.7606310248374939]\n", + "[Epoch 0/1] [Batch 5/12000] [D loss: 0.3162686824798584] [G loss: 0.7600753903388977]\n", + "[Epoch 0/1] [Batch 6/12000] [D loss: 0.30194148421287537] [G loss: 0.791181743144989]\n", + "[Epoch 0/1] [Batch 7/12000] [D loss: 0.3068079948425293] [G loss: 0.7795546650886536]\n", + "[Epoch 0/1] [Batch 8/12000] [D loss: 0.31288668513298035] [G loss: 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0/1] [Batch 1994/12000] [D loss: 0.236822709441185] [G loss: 0.9846187829971313]\n", + "[Epoch 0/1] [Batch 1995/12000] [D loss: 0.19088466465473175] [G loss: 1.1478955745697021]\n", + "[Epoch 0/1] [Batch 1996/12000] [D loss: 0.2450101226568222] [G loss: 0.9505489468574524]\n", + "[Epoch 0/1] [Batch 1997/12000] [D loss: 0.19359757006168365] [G loss: 1.1374849081039429]\n", + "[Epoch 0/1] [Batch 1998/12000] [D loss: 0.1740988940000534] [G loss: 1.2241334915161133]\n", + "[Epoch 0/1] [Batch 1999/12000] [D loss: 0.15129292011260986] [G loss: 1.3428772687911987]\n", + "[Epoch 0/1] [Batch 2000/12000] [D loss: 0.18704311549663544] [G loss: 1.1644879579544067]\n", + "[Epoch 0/1] [Batch 2001/12000] [D loss: 0.2483702450990677] [G loss: 1.0178323984146118]\n", + "[Epoch 0/1] [Batch 2002/12000] [D loss: 0.18075963854789734] [G loss: 1.2050660848617554]\n", + "[Epoch 0/1] [Batch 2003/12000] [D loss: 0.8363043069839478] [G loss: 1.206109642982483]\n", + "[Epoch 0/1] [Batch 2004/12000] [D loss: 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[Batch 11912/12000] [D loss: 0.09403976052999496] [G loss: 1.7634567022323608]\n", + "[Epoch 0/1] [Batch 11913/12000] [D loss: 0.026941601186990738] [G loss: 2.9477572441101074]\n", + "[Epoch 0/1] [Batch 11914/12000] [D loss: 0.13723985850811005] [G loss: 1.4269806146621704]\n", + "[Epoch 0/1] [Batch 11915/12000] [D loss: 0.005127379670739174] [G loss: 4.585136413574219]\n", + "[Epoch 0/1] [Batch 11916/12000] [D loss: 0.03411146625876427] [G loss: 2.7188920974731445]\n", + "[Epoch 0/1] [Batch 11917/12000] [D loss: 0.07310452312231064] [G loss: 1.9949318170547485]\n", + "[Epoch 0/1] [Batch 11918/12000] [D loss: 0.006317567080259323] [G loss: 4.377584934234619]\n", + "[Epoch 0/1] [Batch 11919/12000] [D loss: 0.028776172548532486] [G loss: 2.8836984634399414]\n", + "[Epoch 0/1] [Batch 11920/12000] [D loss: 0.13653430342674255] [G loss: 1.4314614534378052]\n", + "[Epoch 0/1] [Batch 11921/12000] [D loss: 0.01767760142683983] [G loss: 3.3599352836608887]\n", + "[Epoch 0/1] [Batch 11922/12000] [D loss: 0.07434841245412827] [G loss: 1.9792730808258057]\n", + "[Epoch 0/1] [Batch 11923/12000] [D loss: 0.17949135601520538] [G loss: 1.198608636856079]\n", + "[Epoch 0/1] [Batch 11924/12000] [D loss: 6.940688133239746] [G loss: 3.150968074798584]\n", + "[Epoch 0/1] [Batch 11925/12000] [D loss: 0.042234670370817184] [G loss: 2.5133039951324463]\n", + "[Epoch 0/1] [Batch 11926/12000] [D loss: 0.03558404743671417] [G loss: 2.678083658218384]\n", + "[Epoch 0/1] [Batch 11927/12000] [D loss: 0.08128765225410461] [G loss: 1.8968005180358887]\n", + "[Epoch 0/1] [Batch 11928/12000] [D loss: 0.8082725405693054] [G loss: 0.22137461602687836]\n", + "[Epoch 0/1] [Batch 11929/12000] [D loss: 0.030659835785627365] [G loss: 2.822157859802246]\n", + "[Epoch 0/1] [Batch 11930/12000] [D loss: 0.00305578182451427] [G loss: 5.1006269454956055]\n", + "[Epoch 0/1] [Batch 11931/12000] [D loss: 0.0110671641305089] [G loss: 3.821672201156616]\n", + "[Epoch 0/1] [Batch 11932/12000] [D loss: 0.047533921897411346] [G loss: 2.4003219604492188]\n", + "[Epoch 0/1] [Batch 11933/12000] [D loss: 0.015518931671977043] [G loss: 3.4880261421203613]\n", + "[Epoch 0/1] [Batch 11934/12000] [D loss: 0.02474316582083702] [G loss: 3.0306999683380127]\n", + "[Epoch 0/1] [Batch 11935/12000] [D loss: 0.008490198291838169] [G loss: 4.084174156188965]\n", + "[Epoch 0/1] [Batch 11936/12000] [D loss: 0.045862480998039246] [G loss: 2.4344727993011475]\n", + "[Epoch 0/1] [Batch 11937/12000] [D loss: 0.09184908866882324] [G loss: 1.7849045991897583]\n", + "[Epoch 0/1] [Batch 11938/12000] [D loss: 0.0246274434030056] [G loss: 3.0352730751037598]\n", + "[Epoch 0/1] [Batch 11939/12000] [D loss: 0.31941962242126465] [G loss: 0.7505747079849243]\n", + "[Epoch 0/1] [Batch 11940/12000] [D loss: 0.021298222243785858] [G loss: 3.1772072315216064]\n", + "[Epoch 0/1] [Batch 11941/12000] [D loss: 0.0045468769967556] [G loss: 4.704710960388184]\n", + "[Epoch 0/1] [Batch 11942/12000] [D loss: 0.0069402121007442474] [G loss: 4.284207820892334]\n", + "[Epoch 0/1] [Batch 11943/12000] [D loss: 0.04212784394621849] [G loss: 2.5157313346862793]\n", + "[Epoch 0/1] [Batch 11944/12000] [D loss: 0.0921362116932869] [G loss: 1.7820618152618408]\n", + "[Epoch 0/1] [Batch 11945/12000] [D loss: 0.03434452787041664] [G loss: 2.712313413619995]\n", + "[Epoch 0/1] [Batch 11946/12000] [D loss: 0.04112602770328522] [G loss: 2.538810968399048]\n", + "[Epoch 0/1] [Batch 11947/12000] [D loss: 0.24235749244689941] [G loss: 0.9567813277244568]\n", + "[Epoch 0/1] [Batch 11948/12000] [D loss: 0.054317522794008255] [G loss: 2.2735869884490967]\n", + "[Epoch 0/1] [Batch 11949/12000] [D loss: 0.0786018893122673] [G loss: 1.9277846813201904]\n", + "[Epoch 0/1] [Batch 11950/12000] [D loss: 0.09216111153364182] [G loss: 1.781815767288208]\n", + "[Epoch 0/1] [Batch 11951/12000] [D loss: 0.059039629995822906] [G loss: 2.1948578357696533]\n", + "[Epoch 0/1] [Batch 11952/12000] [D loss: 0.24018551409244537] [G loss: 0.9637855887413025]\n", + "[Epoch 0/1] [Batch 11953/12000] [D loss: 0.018776241689920425] [G loss: 3.3007333278656006]\n", + "[Epoch 0/1] [Batch 11954/12000] [D loss: 0.006896386854350567] [G loss: 4.290498733520508]\n", + "[Epoch 0/1] [Batch 11955/12000] [D loss: 0.019644128158688545] [G loss: 3.2564094066619873]\n", + "[Epoch 0/1] [Batch 11956/12000] [D loss: 0.005023753736168146] [G loss: 4.60545015335083]\n", + "[Epoch 0/1] [Batch 11957/12000] [D loss: 0.011152512393891811] [G loss: 3.814074993133545]\n", + "[Epoch 0/1] [Batch 11958/12000] [D loss: 0.15633301436901093] [G loss: 1.3148826360702515]\n", + "[Epoch 0/1] [Batch 11959/12000] [D loss: 0.06785688549280167] [G loss: 2.0642967224121094]\n", + "[Epoch 0/1] [Batch 11960/12000] [D loss: 0.040480516850948334] [G loss: 2.553994655609131]\n", + "[Epoch 0/1] [Batch 11961/12000] [D loss: 0.027522847056388855] [G loss: 2.926988124847412]\n", + "[Epoch 0/1] [Batch 11962/12000] [D loss: 0.026952676475048065] [G loss: 2.947356939315796]\n", + "[Epoch 0/1] [Batch 11963/12000] [D loss: 0.6660107970237732] [G loss: 0.3064478933811188]\n", + "[Epoch 0/1] [Batch 11964/12000] [D loss: 0.07071729749441147] [G loss: 2.02580189704895]\n", + "[Epoch 0/1] [Batch 11965/12000] [D loss: 0.0042016045190393925] [G loss: 4.7833404541015625]\n", + "[Epoch 0/1] [Batch 11966/12000] [D loss: 0.003020638134330511] [G loss: 5.112159252166748]\n", + "[Epoch 0/1] [Batch 11967/12000] [D loss: 0.0030624596402049065] [G loss: 5.098450660705566]\n", + "[Epoch 0/1] [Batch 11968/12000] [D loss: 0.007869784720242023] [G loss: 4.1594367027282715]\n", + "[Epoch 0/1] [Batch 11969/12000] [D loss: 0.021592942997813225] [G loss: 3.163756847381592]\n", + "[Epoch 0/1] [Batch 11970/12000] [D loss: 0.01934470236301422] [G loss: 3.2714717388153076]\n", + "[Epoch 0/1] [Batch 11971/12000] [D loss: 0.03200102970004082] [G loss: 2.780670404434204]\n", + "[Epoch 0/1] [Batch 11972/12000] [D loss: 0.19769874215126038] [G loss: 1.1190568208694458]\n", + "[Epoch 0/1] [Batch 11973/12000] [D loss: 0.09800433367490768] [G loss: 1.726000428199768]\n", + "[Epoch 0/1] [Batch 11974/12000] [D loss: 0.013352134265005589] [G loss: 3.63625431060791]\n", + "[Epoch 0/1] [Batch 11975/12000] [D loss: 1.0781641006469727] [G loss: 0.12301472574472427]\n", + "[Epoch 0/1] [Batch 11976/12000] [D loss: 0.011224819347262383] [G loss: 3.8076846599578857]\n", + "[Epoch 0/1] [Batch 11977/12000] [D loss: 0.0029278842266649008] [G loss: 5.143254280090332]\n", + "[Epoch 0/1] [Batch 11978/12000] [D loss: 0.0021782247349619865] [G loss: 5.438275337219238]\n", + "[Epoch 0/1] [Batch 11979/12000] [D loss: 0.002689403248950839] [G loss: 5.2279767990112305]\n", + "[Epoch 0/1] [Batch 11980/12000] [D loss: 0.015559832565486431] [G loss: 3.4854347705841064]\n", + "[Epoch 0/1] [Batch 11981/12000] [D loss: 0.022725479677319527] [G loss: 3.1137607097625732]\n", + "[Epoch 0/1] [Batch 11982/12000] [D loss: 0.017826678231358528] [G loss: 3.3516857624053955]\n", + "[Epoch 0/1] [Batch 11983/12000] [D loss: 0.019173303619027138] [G loss: 3.2802011966705322]\n", + "[Epoch 0/1] [Batch 11984/12000] [D loss: 0.0782238095998764] [G loss: 1.932238221168518]\n", + "[Epoch 0/1] [Batch 11985/12000] [D loss: 0.16387997567653656] [G loss: 1.2748817205429077]\n", + "[Epoch 0/1] [Batch 11986/12000] [D loss: 0.008408796973526478] [G loss: 4.093726634979248]\n", + "[Epoch 0/1] [Batch 11987/12000] [D loss: 0.044820938259363174] [G loss: 2.456418991088867]\n", + "[Epoch 0/1] [Batch 11988/12000] [D loss: 0.12329575419425964] [G loss: 1.5207854509353638]\n", + "[Epoch 0/1] [Batch 11989/12000] [D loss: 0.07336963713169098] [G loss: 1.9915704727172852]\n", + "[Epoch 0/1] [Batch 11990/12000] [D loss: 0.4363557994365692] [G loss: 0.5409705638885498]\n", + "[Epoch 0/1] [Batch 11991/12000] [D loss: 0.03823548182845116] [G loss: 2.6088359355926514]\n", + "[Epoch 0/1] [Batch 11992/12000] [D loss: 0.05510196089744568] [G loss: 2.260018825531006]\n", + "[Epoch 0/1] [Batch 11993/12000] [D loss: 0.013046055100858212] [G loss: 3.65913987159729]\n", + "[Epoch 0/1] [Batch 11994/12000] [D loss: 0.07849512249231339] [G loss: 1.9290400743484497]\n", + "[Epoch 0/1] [Batch 11995/12000] [D loss: 0.02171831950545311] [G loss: 3.1580917835235596]\n", + "[Epoch 0/1] [Batch 11996/12000] [D loss: 0.16114424169063568] [G loss: 1.2920138835906982]\n", + "[Epoch 0/1] [Batch 11997/12000] [D loss: 0.07179585844278336] [G loss: 2.011718273162842]\n", + "[Epoch 0/1] [Batch 11998/12000] [D loss: 0.06098384037613869] [G loss: 2.164363384246826]\n", + "[Epoch 0/1] [Batch 11999/12000] [D loss: 0.09850254654884338] [G loss: 1.7214127779006958]\n" + ] + } + ], + "source": [ + "# Assume test_data is already defined and processed by your prepared_data function\n", + "# from standard_GAN import GAN\n", + "\n", + "gan = GAN()\n", + "gan.train(epochs=1, train_loader=train_dataset)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + 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.../dbn/dbn_2_chars_400_Units_4_Layers.png | Bin 0 -> 8359 bytes .../dbn/dbn_3_chars_400_Units_4_Layers.png | Bin 0 -> 8419 bytes .../dbn/dbn_4_chars_400_Units_4_Layers.png | Bin 0 -> 8241 bytes .../dbn/dbn_5_chars_400_Units_4_Layers.png | Bin 0 -> 8408 bytes 11 files changed, 20307 insertions(+), 14184 deletions(-) create mode 100644 resultat/dbn/400_Units_4_Layers/Units_400_Chars_YA.npy create mode 100644 resultat/dbn/400_Units_4_Layers/Units_400_Chars_YAB.npy create mode 100644 resultat/dbn/400_Units_4_Layers/Units_400_Chars_YAB1.npy create mode 100644 resultat/dbn/400_Units_4_Layers/Units_400_Chars_YAB12.npy create mode 100644 resultat/images/dbn/dbn_1_chars_400_Units_4_Layers.png create mode 100644 resultat/images/dbn/dbn_2_chars_400_Units_4_Layers.png create mode 100644 resultat/images/dbn/dbn_3_chars_400_Units_4_Layers.png create mode 100644 resultat/images/dbn/dbn_4_chars_400_Units_4_Layers.png create mode 100644 resultat/images/dbn/dbn_5_chars_400_Units_4_Layers.png diff --git a/notebook/experiments_ALPHA_DIGITS.ipynb b/notebook/experiments_ALPHA_DIGITS.ipynb index 037be4e..24d504b 100644 --- a/notebook/experiments_ALPHA_DIGITS.ipynb +++ b/notebook/experiments_ALPHA_DIGITS.ipynb @@ -174,9 +174,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def read_alpha_digit(characters: Optional[Union[str, int, List[Union[str, int]]]] = None,\n", " file_path: Optional[str] = ALPHA_DIGIT_PATH,\n", @@ -231,7 +242,7 @@ " plt.show()\n", "\n", "# Example\n", - "chars = [0, \"K\", 7, \"Z\"]\n", + "chars = [2, \"Y\", 7, \"Z\"]\n", "data = read_alpha_digit(chars, data=alphadigit_data, use_data=True)\n", "plot_characters(chars, data)" ] @@ -890,6 +901,13 @@ " return configurations" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## I- Effect of Layers and units" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -899,7 +917,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -942,7 +960,200 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hidden_units_sizes = [100, 200, 300, 400, 500, 600, 700]\n", + "run_rbm_experiment(hidden_units_sizes, n_epochs=1000, character_sets=['Y'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_rbm_experiment(hidden_units_sizes, n_epochs=1000, character_sets=['Y'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. DBM launch function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def run_experiment(hidden_layers_sizes, n_epochs=100, characters=['A', 'B', '1', '2']):\n", + " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", + "\n", + " # Déterminer le nombre maximum de couches et le nombre unique d'unités\n", + " max_layers = max(len(sizes) for sizes in hidden_layers_sizes)\n", + " unique_units = sorted({sizes[0] for sizes in hidden_layers_sizes})\n", + "\n", + " # Préparer une grille de subplots\n", + " fig, axes = plt.subplots(len(unique_units), max_layers, figsize=(max_layers * 3, len(unique_units) * 3), squeeze=False)\n", + "\n", + " # Initialiser tous les axes comme invisibles; ils seront activés lorsqu'utilisés\n", + " for ax_row in axes:\n", + " for ax in ax_row:\n", + " ax.set_visible(False)\n", + "\n", + " for layer_sizes in hidden_layers_sizes:\n", + " print(f\"\\nTraining DBN with hidden layers: {layer_sizes}\")\n", + " dbn = DBN(n_visible=data.shape[1], hidden_layer_sizes=layer_sizes, random_state=42)\n", + " dbn.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=16, print_each=1000000)\n", + "\n", + " # Génération et affichage d'une image\n", + " generated_image = dbn.generate_image(n_samples=1)\n", + " unit_idx = unique_units.index(layer_sizes[0])\n", + " layer_idx = len(layer_sizes) - 2 # Index 0 pour 2 couches, index 1 pour 3 couches, etc.\n", + "\n", + " ax = axes[unit_idx][layer_idx]\n", + " ax.set_visible(True)\n", + " ax.imshow(generated_image[0].reshape(20, 16), cmap='plasma')\n", + " ax.set_title(f\"N_Layers: {len(layer_sizes)}, N_Units: {layer_sizes[0]}\")\n", + " ax.axis('off')\n", + "\n", + " # Enregistrement de l'image générée\n", + " directory = f\"../resultat/dbn/{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers\"\n", + " os.makedirs(directory, exist_ok=True)\n", + " np.save(f\"{directory}/Units_{layer_sizes[0]}_Chars_{''.join(characters)}.npy\", generated_image[0])\n", + "\n", + " #save figure\n", + " plt.tight_layout()\n", + " directory_image = \"../resultat/images/dbn\"\n", + " os.makedirs(directory_image, exist_ok=True)\n", + " plt.savefig(f\"{directory_image}/dbn_{len(characters)}_chars_{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers.png\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exemple d'utilisation avec des configurations générées\n", + "configurations = generate_symmetric_configurations(min_layers = 2, max_layers = 5, min_neurons = 100, max_neurons = 700, step_neurons = 100)\n", + "run_experiment(configurations, n_epochs=1000, characters=['Y'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## I- Effect of the number of characters on reconstruction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. RBM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# configuration = 2 layer with 200 units each\n", + "configurations_fixe = generate_symmetric_configurations(min_layers = 2, max_layers = 2, min_neurons = 200, max_neurons = 200, step_neurons = 100)\n", + "\n", + "run_experiment(configurations_fixe, n_epochs=2, characters = ['Y'])\n", + "run_experiment(configurations_fixe, n_epochs=2, characters = ['E', 'Y'])\n", + "run_experiment(configurations_fixe, n_epochs=2, characters = ['E', 'Y', '2'])\n", + "run_experiment(configurations_fixe, n_epochs=2, characters = ['E', 'Y', 'G', '2'])\n", + "run_experiment(configurations_fixe, n_epochs=2, characters=['E', 'Y', 'G', '2', '7'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exemple d'utilisation\n", + "hidden_units_sizes = [100, 200, 300, 400, 500, 600, 700]\n", + "run_rbm_experiment(hidden_units_sizes, n_epochs=2, character_sets=[['A', 'B'], ['A', 'B', 'C'], ['A', 'B', 'C', 'D']])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. DBM" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def run_experiment(hidden_layers_sizes, n_epochs=100, characters=['A', 'B', '1', '2']):\n", + " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", + "\n", + " # Déterminer le nombre maximum de couches et le nombre unique d'unités\n", + " max_layers = max(len(sizes) for sizes in hidden_layers_sizes)\n", + " unique_units = sorted({sizes[0] for sizes in hidden_layers_sizes})\n", + "\n", + " # Préparer une grille de subplots\n", + " fig, axes = plt.subplots(len(unique_units), max_layers, figsize=(max_layers * 3, len(unique_units) * 3), squeeze=False)\n", + "\n", + " # Initialiser tous les axes comme invisibles; ils seront activés lorsqu'utilisés\n", + " for ax_row in axes:\n", + " for ax in ax_row:\n", + " ax.set_visible(False)\n", + "\n", + " for layer_sizes in hidden_layers_sizes:\n", + " print(f\"\\nTraining DBN with hidden layers: {layer_sizes}\")\n", + " dbn = DBN(n_visible=data.shape[1], hidden_layer_sizes=layer_sizes, random_state=42)\n", + " dbn.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=16, print_each=1000000)\n", + "\n", + " # Génération et affichage d'une image\n", + " generated_image = dbn.generate_image(n_samples=1)\n", + " unit_idx = unique_units.index(layer_sizes[0])\n", + " layer_idx = len(layer_sizes) - 2 # Index 0 pour 2 couches, index 1 pour 3 couches, etc.\n", + "\n", + " ax = axes[unit_idx][layer_idx]\n", + " ax.set_visible(True)\n", + " ax.imshow(generated_image[0].reshape(20, 16), cmap='plasma')\n", + " ax.set_title(f\"N_Layers: {len(layer_sizes)}, N_Units: {layer_sizes[0]}, N_Chars: {len(characters)}\")\n", + " ax.axis('off')\n", + "\n", + " # Enregistrement de l'image générée\n", + " directory = f\"../resultat/dbn/{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers\"\n", + " os.makedirs(directory, exist_ok=True)\n", + " np.save(f\"{directory}/Units_{layer_sizes[0]}_Chars_{''.join(characters)}.npy\", generated_image[0])\n", + "\n", + " #save figure\n", + " plt.tight_layout()\n", + " directory_image = \"../resultat/images/dbn\"\n", + " os.makedirs(directory_image, exist_ok=True)\n", + " plt.savefig(f\"{directory_image}/dbn_{len(characters)}_chars_{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers.png\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -950,7202 +1161,8160 @@ "output_type": "stream", "text": [ "\n", - "Training RBM with 100 hidden units on characters 2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 0: 0%| | 0/3 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Exemple d'utilisation avec des configurations générées\n", + "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", + "run_experiment(configurations, n_epochs=1000, characters=['Y'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", - "Training RBM with 500 hidden units on characters 2\n" + "Training DBN with hidden layers: [400, 400, 400, 400]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 1939.11it/s]\n" + "Epoch 0: 100%|██████████| 5/5 [00:00<00:00, 395.25it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.29085.\n" + "Reconstruction error: 0.22324.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 204.30it/s]\n", - "Epoch 2: 100%|██████████| 3/3 [00:00<00:00, 270.88it/s]\n", - "Epoch 3: 100%|██████████| 3/3 [00:00<00:00, 320.02it/s]\n", - "Epoch 4: 100%|██████████| 3/3 [00:00<00:00, 294.23it/s]\n", - "Epoch 5: 100%|██████████| 3/3 [00:00<00:00, 347.35it/s]\n", - "Epoch 6: 100%|██████████| 3/3 [00:00<00:00, 279.48it/s]\n", - "Epoch 7: 100%|██████████| 3/3 [00:00<00:00, 324.92it/s]\n", - "Epoch 8: 100%|██████████| 3/3 [00:00<00:00, 310.03it/s]\n", - "Epoch 9: 100%|██████████| 3/3 [00:00<00:00, 568.72it/s]\n", - "Epoch 10: 100%|██████████| 3/3 [00:00<00:00, 493.85it/s]\n", - "Epoch 11: 100%|██████████| 3/3 [00:00<00:00, 361.79it/s]\n", - "Epoch 12: 100%|██████████| 3/3 [00:00<00:00, 357.34it/s]\n", - "Epoch 13: 100%|██████████| 3/3 [00:00<00:00, 339.85it/s]\n", - "Epoch 14: 100%|██████████| 3/3 [00:00<00:00, 364.76it/s]\n", - "Epoch 15: 100%|██████████| 3/3 [00:00<00:00, 383.31it/s]\n", - "Epoch 16: 100%|██████████| 3/3 [00:00<00:00, 388.90it/s]\n", - "Epoch 17: 100%|██████████| 3/3 [00:00<00:00, 295.27it/s]\n", - "Epoch 18: 100%|██████████| 3/3 [00:00<00:00, 333.83it/s]\n", - "Epoch 19: 100%|██████████| 3/3 [00:00<00:00, 358.63it/s]\n", - "Epoch 20: 100%|██████████| 3/3 [00:00<00:00, 315.90it/s]\n", - "Epoch 21: 100%|██████████| 3/3 [00:00<00:00, 256.98it/s]\n", - "Epoch 22: 100%|██████████| 3/3 [00:00<00:00, 364.32it/s]\n", - "Epoch 23: 100%|██████████| 3/3 [00:00<00:00, 407.36it/s]\n", - "Epoch 24: 100%|██████████| 3/3 [00:00<00:00, 409.09it/s]\n", - "Epoch 25: 100%|██████████| 3/3 [00:00<00:00, 898.52it/s]\n", - "Epoch 26: 100%|██████████| 3/3 [00:00<00:00, 365.65it/s]\n", - "Epoch 27: 100%|██████████| 3/3 [00:00<00:00, 361.99it/s]\n", - "Epoch 28: 100%|██████████| 3/3 [00:00<00:00, 344.71it/s]\n", - "Epoch 29: 100%|██████████| 3/3 [00:00<00:00, 298.27it/s]\n", - "Epoch 30: 100%|██████████| 3/3 [00:00<00:00, 835.69it/s]\n", - "Epoch 31: 100%|██████████| 3/3 [00:00<00:00, 386.69it/s]\n", - "Epoch 32: 100%|██████████| 3/3 [00:00<00:00, 329.40it/s]\n", - "Epoch 33: 100%|██████████| 3/3 [00:00<00:00, 340.09it/s]\n", - "Epoch 34: 100%|██████████| 3/3 [00:00<00:00, 402.81it/s]\n", - "Epoch 35: 100%|██████████| 3/3 [00:00<00:00, 360.39it/s]\n", - "Epoch 36: 100%|██████████| 3/3 [00:00<00:00, 401.64it/s]\n", - "Epoch 37: 100%|██████████| 3/3 [00:00<00:00, 377.65it/s]\n", - "Epoch 38: 100%|██████████| 3/3 [00:00<00:00, 354.46it/s]\n", - "Epoch 39: 100%|██████████| 3/3 [00:00<00:00, 401.93it/s]\n", - "Epoch 40: 100%|██████████| 3/3 [00:00<00:00, 464.86it/s]\n", - "Epoch 41: 100%|██████████| 3/3 [00:00<00:00, 409.85it/s]\n", - "Epoch 42: 100%|██████████| 3/3 [00:00<00:00, 372.19it/s]\n", - "Epoch 43: 100%|██████████| 3/3 [00:00<00:00, 359.70it/s]\n", - "Epoch 44: 100%|██████████| 3/3 [00:00<00:00, 353.43it/s]\n", - "Epoch 45: 100%|██████████| 3/3 [00:00<00:00, 414.13it/s]\n", - "Epoch 46: 100%|██████████| 3/3 [00:00<00:00, 410.00it/s]\n", - "Epoch 47: 100%|██████████| 3/3 [00:00<00:00, 407.24it/s]\n", - "Epoch 48: 100%|██████████| 3/3 [00:00<00:00, 380.71it/s]\n", - "Epoch 49: 100%|██████████| 3/3 [00:00<00:00, 440.25it/s]\n", - "Epoch 50: 100%|██████████| 3/3 [00:00<00:00, 374.76it/s]\n", - "Epoch 51: 100%|██████████| 3/3 [00:00<00:00, 392.27it/s]\n", - "Epoch 52: 100%|██████████| 3/3 [00:00<00:00, 441.58it/s]\n", - "Epoch 53: 100%|██████████| 3/3 [00:00<00:00, 366.24it/s]\n", - "Epoch 54: 100%|██████████| 3/3 [00:00<00:00, 341.67it/s]\n", - "Epoch 55: 100%|██████████| 3/3 [00:00<00:00, 407.16it/s]\n", - "Epoch 56: 100%|██████████| 3/3 [00:00<00:00, 489.34it/s]\n", - "Epoch 57: 100%|██████████| 3/3 [00:00<00:00, 427.79it/s]\n", - "Epoch 58: 100%|██████████| 3/3 [00:00" + "
" ] }, "metadata": {}, @@ -8153,13 +9322,15 @@ } ], "source": [ - "hidden_units_sizes = [100, 200, 300, 400, 500, 600, 700]\n", - "run_rbm_experiment(hidden_units_sizes, n_epochs=1000, character_sets=['Y'])\n" + "# Exemple d'utilisation avec des configurations générées\n", + "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", + "run_experiment(configurations, n_epochs=1000, characters=['Y', 'A'])\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -8167,7309 +9338,12261 @@ "output_type": "stream", "text": [ "\n", - "Training RBM with 100 hidden units on characters Y\n" + "Training DBN with hidden layers: [400, 400, 400, 400]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 999.36it/s]\n" + "Epoch 0: 100%|██████████| 8/8 [00:00<00:00, 304.66it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.1556.\n" + "Reconstruction error: 0.234.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 1227.84it/s]\n", - "Epoch 2: 100%|██████████| 3/3 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Exemple d'utilisation avec des configurations générées\n", + "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", + "run_experiment(configurations, n_epochs=1000, characters=['Y', 'A', 'B'])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.18141.\n" + "\n", + "Training DBN with hidden layers: [400, 400, 400, 400]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 178.52it/s]\n", - "Epoch 2: 100%|██████████| 3/3 [00:00<00:00, 66.62it/s]\n", - "Epoch 3: 100%|██████████| 3/3 [00:00<00:00, 373.55it/s]\n", - "Epoch 4: 100%|██████████| 3/3 [00:00<00:00, 396.34it/s]\n", - "Epoch 5: 100%|██████████| 3/3 [00:00<00:00, 1000.31it/s]\n", - "Epoch 6: 100%|██████████| 3/3 [00:00<00:00, 998.09it/s]\n", - "Epoch 7: 100%|██████████| 3/3 [00:00<00:00, 742.57it/s]\n", - "Epoch 8: 100%|██████████| 3/3 [00:00<00:00, 1000.07it/s]\n", - "Epoch 9: 100%|██████████| 3/3 [00:00<00:00, 695.57it/s]\n", - "Epoch 10: 100%|██████████| 3/3 [00:00<00:00, 893.10it/s]\n", - "Epoch 11: 100%|██████████| 3/3 [00:00<00:00, 1126.90it/s]\n", - "Epoch 12: 100%|██████████| 3/3 [00:00<00:00, 1080.73it/s]\n", - "Epoch 13: 100%|██████████| 3/3 [00:00<00:00, 870.49it/s]\n", - "Epoch 14: 100%|██████████| 3/3 [00:00<00:00, 834.91it/s]\n", - "Epoch 15: 100%|██████████| 3/3 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Exemple d'utilisation avec des configurations générées\n", + "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", + "run_experiment(configurations, n_epochs=1000, characters=['Y', 'A', 'B', '1'])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", - "Training RBM with 600 hidden units on characters Y\n" + "Training DBN with hidden layers: [400, 400, 400, 400]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 62.96it/s]\n" + "Epoch 0: 100%|██████████| 13/13 [00:00<00:00, 324.55it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.22526.\n" + "Reconstruction error: 0.21863.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 245.41it/s]\n", - "Epoch 2: 100%|██████████| 3/3 [00:00<00:00, 264.69it/s]\n", - "Epoch 3: 100%|██████████| 3/3 [00:00<00:00, 285.75it/s]\n", - "Epoch 4: 100%|██████████| 3/3 [00:00<00:00, 329.86it/s]\n", - "Epoch 5: 100%|██████████| 3/3 [00:00<00:00, 258.67it/s]\n", - "Epoch 6: 100%|██████████| 3/3 [00:00<00:00, 416.54it/s]\n", - "Epoch 7: 100%|██████████| 3/3 [00:00<00:00, 324.75it/s]\n", - "Epoch 8: 100%|██████████| 3/3 [00:00<00:00, 521.10it/s]\n", - "Epoch 9: 100%|██████████| 3/3 [00:00<00:00, 433.15it/s]\n", - "Epoch 10: 100%|██████████| 3/3 [00:00<00:00, 191.68it/s]\n", - "Epoch 11: 100%|██████████| 3/3 [00:00" + "
" ] }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "run_rbm_experiment(hidden_units_sizes, n_epochs=1000, character_sets=['Y'])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. DBM launch function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "def run_experiment(hidden_layers_sizes, n_epochs=100, characters=['A', 'B', '1', '2']):\n", - " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", - "\n", - " # Déterminer le nombre maximum de couches et le nombre unique d'unités\n", - " max_layers = max(len(sizes) for sizes in hidden_layers_sizes)\n", - " unique_units = sorted({sizes[0] for sizes in hidden_layers_sizes})\n", - "\n", - " # Préparer une grille de subplots\n", - " fig, axes = plt.subplots(len(unique_units), max_layers, figsize=(max_layers * 3, len(unique_units) * 3), squeeze=False)\n", - "\n", - " # Initialiser tous les axes comme invisibles; ils seront activés lorsqu'utilisés\n", - " for ax_row in axes:\n", - " for ax in ax_row:\n", - " ax.set_visible(False)\n", - "\n", - " for layer_sizes in hidden_layers_sizes:\n", - " print(f\"\\nTraining DBN with hidden layers: {layer_sizes}\")\n", - " dbn = DBN(n_visible=data.shape[1], hidden_layer_sizes=layer_sizes, random_state=42)\n", - " dbn.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=16, print_each=1000000)\n", - "\n", - " # Génération et affichage d'une image\n", - " generated_image = dbn.generate_image(n_samples=1)\n", - " unit_idx = unique_units.index(layer_sizes[0])\n", - " layer_idx = len(layer_sizes) - 2 # Index 0 pour 2 couches, index 1 pour 3 couches, etc.\n", - "\n", - " ax = axes[unit_idx][layer_idx]\n", - " ax.set_visible(True)\n", - " ax.imshow(generated_image[0].reshape(20, 16), cmap='plasma')\n", - " ax.set_title(f\"N_Layers: {len(layer_sizes)}, N_Units: {layer_sizes[0]}\")\n", - " ax.axis('off')\n", - "\n", - " # Enregistrement de l'image générée\n", - " directory = f\"../resultat/dbn/{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers\"\n", - " os.makedirs(directory, exist_ok=True)\n", - " np.save(f\"{directory}/Units_{layer_sizes[0]}_Chars_{''.join(characters)}.npy\", generated_image[0])\n", - "\n", - " #save figure\n", - " plt.tight_layout()\n", - " directory_image = \"../resultat/images/dbn\"\n", - " os.makedirs(directory_image, exist_ok=True)\n", - " plt.savefig(f\"{directory_image}/dbn_{len(characters)}_chars_{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers.png\")\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "# Exemple d'utilisation avec des configurations générées\n", - "configurations = generate_symmetric_configurations(min_layers = 2, max_layers = 5, min_neurons = 100, max_neurons = 700, step_neurons = 100)\n", - "run_experiment(configurations, n_epochs=1000, characters=['Y'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Effect of the number of characters on reconstruction" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# configuration = 2 layer with 200 units each\n", - "configurations_fixe = generate_symmetric_configurations(min_layers = 2, max_layers = 2, min_neurons = 200, max_neurons = 200, step_neurons = 100)\n", - "\n", - "run_experiment(configurations_fixe, n_epochs=2, characters = ['Y'])\n", - "run_experiment(configurations_fixe, n_epochs=2, characters = ['E', 'Y'])\n", - "run_experiment(configurations_fixe, n_epochs=2, characters = ['E', 'Y', '2'])\n", - "run_experiment(configurations_fixe, n_epochs=2, characters = ['E', 'Y', 'G', '2'])\n", - "run_experiment(configurations_fixe, n_epochs=2, characters=['E', 'Y', 'G', '2', '7'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Exemple d'utilisation\n", - "hidden_units_sizes = [100, 200, 300, 400, 500, 600, 700]\n", - "run_rbm_experiment(hidden_units_sizes, n_epochs=2, character_sets=[['A', 'B'], ['A', 'B', 'C'], ['A', 'B', 'C', 'D']])\n" + "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", + "run_experiment(configurations, n_epochs=1000, characters=['Y', 'A', 'B', '1', '2'])\n", + "\n" ] }, { diff --git a/resultat/dbn/400_Units_4_Layers/Units_400_Chars_Y.npy b/resultat/dbn/400_Units_4_Layers/Units_400_Chars_Y.npy index 7a4cc967fcc096f7edcfd908173fa0a5505cc51b..046c7c42ec5a65ed62bb1c9fc842170f79bd9123 100644 GIT binary patch delta 84 zcmZn=Z4lkS!8F-{MPsr7Qv#S?z-Tb}0kg$q0VW3^9Wl88NH1XY0LnE?E@0%Ce1W-O gvH(zg17pJG53B*qlOF&j8W;s87qCQ3zQ7s*0D^5Co&W#< delta 61 zcmZn=Z4lkS!8G{-i^Ajr<_;jXn4G}u0LB+s1Aw%~6ihSB?9R+> z9?SJ&wTYu^bhDW&civ8oogF7OF?OH(i%-wn+-L5k&(l-sD!n|Ug{ydfJQ^m0#N18m zuNVE<7r*b7Fx2mm`Xv2R?~wALqX%3=?AJvpkNwgf>+I7w`@z`nAgQ8q}?0j literal 0 HcmV?d00001 diff --git a/resultat/dbn/400_Units_4_Layers/Units_400_Chars_YAB.npy b/resultat/dbn/400_Units_4_Layers/Units_400_Chars_YAB.npy new 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CHOHO Date: Sat, 30 Mar 2024 17:24:11 +0100 Subject: [PATCH 12/16] chore : add fig on number of different chars --- notebook/experiments_ALPHA_DIGITS.ipynb | 13133 ++++++---------- .../Units_400_Chars_YAZ.npy | Bin 0 -> 2688 bytes .../Units_400_Chars_YUZ.npy | Bin 0 -> 2688 bytes .../dbn/dbn_3_chars_400_Units_4_Layers.png | Bin 8419 -> 8480 bytes 4 files changed, 5101 insertions(+), 8032 deletions(-) create mode 100644 resultat/dbn/400_Units_4_Layers/Units_400_Chars_YAZ.npy create mode 100644 resultat/dbn/400_Units_4_Layers/Units_400_Chars_YUZ.npy diff --git a/notebook/experiments_ALPHA_DIGITS.ipynb b/notebook/experiments_ALPHA_DIGITS.ipynb index 24d504b..6354b46 100644 --- a/notebook/experiments_ALPHA_DIGITS.ipynb +++ b/notebook/experiments_ALPHA_DIGITS.ipynb @@ -9330,7 +9330,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -9345,4063 +9345,4063 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 8/8 [00:00<00:00, 304.66it/s]\n" + "Epoch 0: 100%|██████████| 8/8 [00:00<00:00, 219.02it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.234.\n" + "Reconstruction error: 0.23016.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 8/8 [00:00<00:00, 454.87it/s]\n", - "Epoch 2: 100%|██████████| 8/8 [00:00<00:00, 217.44it/s]\n", - "Epoch 3: 100%|██████████| 8/8 [00:00<00:00, 181.93it/s]\n", - "Epoch 4: 100%|██████████| 8/8 [00:00<00:00, 347.51it/s]\n", - "Epoch 5: 100%|██████████| 8/8 [00:00<00:00, 6203.44it/s]\n", - "Epoch 6: 100%|██████████| 8/8 [00:00<00:00, 511.80it/s]\n", - "Epoch 7: 100%|██████████| 8/8 [00:00<00:00, 429.95it/s]\n", - "Epoch 8: 100%|██████████| 8/8 [00:00<00:00, 1300.56it/s]\n", - "Epoch 9: 100%|██████████| 8/8 [00:00<00:00, 533.13it/s]\n", - "Epoch 10: 100%|██████████| 8/8 [00:00" ] @@ -13413,13 +13413,13 @@ "source": [ "# Exemple d'utilisation avec des configurations générées\n", "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", - "run_experiment(configurations, n_epochs=1000, characters=['Y', 'A', 'B'])\n", + "run_experiment(configurations, n_epochs=1000, characters=['Y', 'U', 'Z'])\n", "\n" ] }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -13434,7 +13434,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 10/10 [00:00<00:00, 304.15it/s]\n" + "Epoch 0: 100%|██████████| 10/10 [00:00<00:00, 501.11it/s]\n" ] }, { @@ -13448,1006 +13448,1006 @@ "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 10/10 [00:00<00:00, 861.24it/s]\n", - "Epoch 2: 100%|██████████| 10/10 [00:00<00:00, 560.87it/s]\n", - "Epoch 3: 100%|██████████| 10/10 [00:00<00:00, 686.47it/s]\n", - "Epoch 4: 100%|██████████| 10/10 [00:00<00:00, 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388.61it/s]\n", - "Epoch 95: 100%|██████████| 10/10 [00:00 3\u001b[0m \u001b[43mrun_experiment\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfigurations\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcharacters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mY\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mA\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mB\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m1\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[1;32mIn[74], line 21\u001b[0m, in \u001b[0;36mrun_experiment\u001b[1;34m(hidden_layers_sizes, n_epochs, characters)\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mTraining DBN with hidden layers: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlayer_sizes\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 20\u001b[0m dbn \u001b[38;5;241m=\u001b[39m DBN(n_visible\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m], hidden_layer_sizes\u001b[38;5;241m=\u001b[39mlayer_sizes, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m---> 21\u001b[0m \u001b[43mdbn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m16\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_each\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 23\u001b[0m \u001b[38;5;66;03m# Génération et affichage d'une image\u001b[39;00m\n\u001b[0;32m 24\u001b[0m generated_image \u001b[38;5;241m=\u001b[39m dbn\u001b[38;5;241m.\u001b[39mgenerate_image(n_samples\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "Cell \u001b[1;32mIn[18], line 70\u001b[0m, in \u001b[0;36mDBN.train\u001b[1;34m(self, data, learning_rate, n_epochs, batch_size, print_each)\u001b[0m\n\u001b[0;32m 68\u001b[0m input_data \u001b[38;5;241m=\u001b[39m data\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m rbm \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrbms:\n\u001b[1;32m---> 70\u001b[0m \u001b[43mrbm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 72\u001b[0m \u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlearning_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 73\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 74\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 75\u001b[0m \u001b[43m \u001b[49m\u001b[43mprint_each\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprint_each\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 76\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 77\u001b[0m \u001b[38;5;66;03m# Update input data for the next RBM\u001b[39;00m\n\u001b[0;32m 78\u001b[0m input_data \u001b[38;5;241m=\u001b[39m rbm\u001b[38;5;241m.\u001b[39minput_output(input_data)\n", + "Cell \u001b[1;32mIn[12], line 145\u001b[0m, in \u001b[0;36mRBM.train\u001b[1;34m(self, data, learning_rate, n_epochs, batch_size, print_each)\u001b[0m\n\u001b[0;32m 143\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m0\u001b[39m, n_samples, batch_size), desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEpoch \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepoch\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 144\u001b[0m batch \u001b[38;5;241m=\u001b[39m data[i:i\u001b[38;5;241m+\u001b[39mbatch_size]\n\u001b[1;32m--> 145\u001b[0m _, pos_v_probs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlearning_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[0;32m 150\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 152\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m epoch \u001b[38;5;241m%\u001b[39m print_each \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 153\u001b[0m tqdm\u001b[38;5;241m.\u001b[39mwrite(\n\u001b[0;32m 154\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReconstruction error: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reconstruction_error(batch,\u001b[38;5;250m \u001b[39mpos_v_probs)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "Cell \u001b[1;32mIn[12], line 113\u001b[0m, in \u001b[0;36mRBM.update\u001b[1;34m(self, batch, learning_rate, batch_size, return_output)\u001b[0m\n\u001b[0;32m 111\u001b[0m \u001b[38;5;66;03m# Update weights and biases\u001b[39;00m\n\u001b[0;32m 112\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mW \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m (batch\u001b[38;5;241m.\u001b[39mT \u001b[38;5;241m@\u001b[39m pos_h_probs \u001b[38;5;241m-\u001b[39m pos_v_probs\u001b[38;5;241m.\u001b[39mT \u001b[38;5;241m@\u001b[39m neg_h_probs) \u001b[38;5;241m/\u001b[39m batch_size\n\u001b[1;32m--> 113\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mb \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m \u001b[43m(\u001b[49m\u001b[43mpos_h_probs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mneg_h_probs\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 114\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m (batch \u001b[38;5;241m-\u001b[39m pos_v_probs)\u001b[38;5;241m.\u001b[39mmean(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 116\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_output:\n", + "File \u001b[1;32mc:\\Users\\choho\\Desktop\\Master DS\\Deep learning II\\github\\.venv\\lib\\site-packages\\numpy\\core\\_methods.py:101\u001b[0m, in \u001b[0;36m_mean\u001b[1;34m(a, axis, dtype, out, keepdims, where)\u001b[0m\n\u001b[0;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 99\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m um\u001b[38;5;241m.\u001b[39mclip(a, \u001b[38;5;28mmin\u001b[39m, \u001b[38;5;28mmax\u001b[39m, out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 101\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_mean\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m 102\u001b[0m arr \u001b[38;5;241m=\u001b[39m asanyarray(a)\n\u001b[0;32m 104\u001b[0m is_float16_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] }, { "data": { - "image/png": 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16)\n" - ] - } - ], + "outputs": [], "source": [ "def _load_data(file_path: str, which: Literal[\"alphadigit\", \"mnist\"]=\"alphadigit\") -> Dict[str, np.ndarray]:\n", " \"\"\"\n", @@ -398,21 +134,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 > map to > [0]\n", - "10 > map to > [10]\n", - "A > map to > [10]\n", - "[1, 'C'] > map to > [[1], [12]]\n", - "36 > no mapping available, out of range\n" - ] - } - ], + "outputs": [], "source": [ "def _map_characters_to_indices(characters: Union[str, int, List[Union[str, int]]]) -> List[int]:\n", " \"\"\"\n", @@ -450,20 +174,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def read_alpha_digit(characters: Optional[Union[str, int, List[Union[str, int]]]] = None,\n", " file_path: Optional[str] = ALPHA_DIGIT_PATH,\n", @@ -525,24 +238,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "data shape: (156, 320)\n" - ] - } - ], + "outputs": [], "source": [ "print(\"data shape:\", data.shape)" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -730,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -740,1183 +445,9 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RBM(n_visible=320, n_hidden=200)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 666.56it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.16569.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 222.19it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 76.35it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 666.69it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 499.96it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 571.39it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 444.29it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 444.39it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 666.87it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 665.95it/s]\n", - "Epoch 10: 100%|██████████| 4/4 [00:00<00:00, 799.83it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.13308.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 11: 100%|██████████| 4/4 [00:00<00:00, 500.07it/s]\n", - "Epoch 12: 100%|██████████| 4/4 [00:00<00:00, 667.11it/s]\n", - "Epoch 13: 100%|██████████| 4/4 [00:00<00:00, 499.93it/s]\n", - "Epoch 14: 100%|██████████| 4/4 [00:00<00:00, 666.87it/s]\n", - "Epoch 15: 100%|██████████| 4/4 [00:00<00:00, 478.53it/s]\n", - "Epoch 16: 100%|██████████| 4/4 [00:00<00:00, 571.59it/s]\n", - "Epoch 17: 100%|██████████| 4/4 [00:00<00:00, 571.18it/s]\n", - "Epoch 18: 100%|██████████| 4/4 [00:00<00:00, 452.50it/s]\n", - "Epoch 19: 100%|██████████| 4/4 [00:00<00:00, 534.92it/s]\n", - "Epoch 20: 100%|██████████| 4/4 [00:00<00:00, 489.13it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.11447.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 21: 100%|██████████| 4/4 [00:00<00:00, 473.20it/s]\n", - "Epoch 22: 100%|██████████| 4/4 [00:00<00:00, 250.00it/s]\n", - "Epoch 23: 100%|██████████| 4/4 [00:00<00:00, 571.35it/s]\n", - "Epoch 24: 100%|██████████| 4/4 [00:00<00:00, 430.93it/s]\n", - "Epoch 25: 100%|██████████| 4/4 [00:00<00:00, 678.72it/s]\n", - "Epoch 26: 100%|██████████| 4/4 [00:00<00:00, 316.71it/s]\n", - "Epoch 27: 100%|██████████| 4/4 [00:00<00:00, 444.42it/s]\n", - "Epoch 28: 100%|██████████| 4/4 [00:00<00:00, 500.16it/s]\n", - "Epoch 29: 100%|██████████| 4/4 [00:00<00:00, 666.66it/s]\n", - "Epoch 30: 100%|██████████| 4/4 [00:00<00:00, 666.82it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.0752.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 31: 100%|██████████| 4/4 [00:00<00:00, 537.96it/s]\n", - "Epoch 32: 100%|██████████| 4/4 [00:00<00:00, 465.62it/s]\n", - "Epoch 33: 100%|██████████| 4/4 [00:00<00:00, 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error: 8e-05.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 491: 100%|██████████| 4/4 [00:00<00:00, 578.19it/s]\n", - "Epoch 492: 100%|██████████| 4/4 [00:00<00:00, 500.29it/s]\n", - "Epoch 493: 100%|██████████| 4/4 [00:00<00:00, 568.43it/s]\n", - "Epoch 494: 100%|██████████| 4/4 [00:00<00:00, 499.87it/s]\n", - "Epoch 495: 100%|██████████| 4/4 [00:00<00:00, 666.48it/s]\n", - "Epoch 496: 100%|██████████| 4/4 [00:00<00:00, 444.65it/s]\n", - "Epoch 497: 100%|██████████| 4/4 [00:00<00:00, 666.69it/s]\n", - "Epoch 498: 100%|██████████| 4/4 [00:00<00:00, 571.49it/s]\n", - "Epoch 499: 100%|██████████| 4/4 [00:00<00:00, 499.81it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "RBM(n_visible=320, n_hidden=200)" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Parameters\n", "n_visible = data.shape[1] # Number of visible units (size of each image)\n", @@ -1932,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1941,20 +472,9 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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zcuUXAACA9BS/AAAApKf4BQAAID3FLwAAAOmNFHfIAwAAkJwrvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4BAABIT/ELAABAeopfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACA9xS8AAADpKX4rWr58eWzcuLH2MIaO3OuRfT2yr0Pu9ci+DrnXI/s65F7PVLKfcvG7devW+L3f+734xV/8xZgzZ07MmTMnTjrppHj3u98dDzzwwFQ32zp33nlnXHXVVbWHMUru9ci+HtnXIfd6ZF+H3OuRfR1yr0f2dcyYyjf9zd/8Tfzmb/5mzJgxIy644IJ45StfGdOmTYv/+q//ittvvz0+85nPxNatW2PZsmVNj7fv7rzzzrjxxhtbMWlyr0f29ci+DrnXI/s65F6P7OuQez2yr2fSxe/DDz8c5513Xixbtiz+/u//PpYsWTLm69dee218+tOfjmnT2vkb1c8880zMnTu39jAmTe71yL4e2dch93pkX4fc65F9HXKvR/aVlUm6+OKLS0SUf/mXf+n4e/7zP/+zvOUtbykLFy4ss2bNKmvWrCl/+Zd/OeY5N910U4mIcu+995b3vve9ZdGiRWXOnDnlzW9+c9mxY8e4bd55553l1FNPLXPmzCnz5s0rZ511VnnwwQfHPGfDhg1l7ty5ZcuWLeUNb3hDmTdvXnnTm95USinl29/+djnnnHPKy172snLEEUeUl770peXyyy8vP/3pT8d8f0SM+2+/PXv2lE9+8pPlpJNOKrNmzSpHH310ufjii8uuXbvGjGPv3r3l4x//eFm6dGmZPXt2Of3008uDDz5Yli1bVjZs2NBRhnKvk3spspf98GUvd2t+2LKXuzU/bNnL3ZofxuxLKWXSxe+xxx5bTjjhhI6f/+CDD5YFCxaUk046qVx77bXlhhtuKKeddloZGRkpt99+++jz9k/YL//yL5fXv/715frrry/ve9/7yvTp08u55547Zps333xzGRkZKb/xG79Rrr/++nLttdeW5cuXl5e85CVl69ato8/bsGFDmTVrVlmxYkXZsGFD+exnP1tuvvnmUkopl112WTnrrLPK1VdfXTZt2lQuvPDCMn369HLOOeeMfv8//dM/lTPPPLNERPnyl788+t9+v/M7v1NmzJhRLrroovLZz362fPCDHyxz584tv/Irv1J+/vOfjz7vwx/+cImIctZZZ5UbbrihvPOd7yzHHntsWbRoUccTJvc6uZcie9kPX/Zyt+aHLXu5W/PDlr3crflhzL6USRa/Tz75ZImI8uY3v3nc15544ony2GOPjf63v+r/tV/7tbJ69erys5/9bPS5e/fuLSeffHJZuXLl6GP7J2zt2rVl7969o4+/973vLdOnTy+7d+8upZTy9NNPl5e85CXloosuGvP6//d//1cWLFgw5vH9P2340Ic+NG68B/9UYr9rrrmmjIyMlEceeWT0sXe/+91jfkKx33e+850SEeWWW24Z8/g3v/nNMY/v2LGjHHHEEeXss88es19XXnlliYiOJkzuB/Qz91JkfzDZH5A5e7kfYM0fkDl7uR9gzR+QOXu5H2DNH5A9+/0m9cvkTz31VEREzJs3b9zXTj/99Fi8ePHofzfeeGPs2rUr/uEf/iHOPffcePrpp2Pnzp2xc+fOePzxx2PdunWxefPm2L59+5jtXHzxxTEyMjL679e+9rWxZ8+eeOSRRyIi4u67747du3fH+eefP7q9nTt3xvTp0+M1r3lN3HPPPePG9q53vWvcY7Nnzx79/2eeeSZ27twZJ598cpRS4r777nvRLG677bZYsGBBnHnmmWPGsWbNmpg3b97oOL71rW/Fz3/+87jsssvG7Nfll1/+oq+xn9wP6GfuEbI/mOyHI3u5H2DND0f2cj/Amh+O7OV+gDU/PNnvN6mGV0cddVRERPzkJz8Z97VNmzbF008/HY8++mi87W1vi4iILVu2RCklPvKRj8RHPvKRCbe5Y8eOWLp06ei/X/7yl4/5+sKFCyMi4oknnoiIiM2bN0dExOtf//oJtzd//vwx/54xY0a89KUvHfe8H//4x/GHf/iH8Vd/9Vej297vySefnHDbB9u8eXM8+eSTcfTRR0/49R07dkREjC60lStXjvn64sWLR/ftxcj9gH7mHiH7g8l+vIzZy/0Aa368jNnL/QBrfryM2cv9AGt+vKzZ7zep4nfBggWxZMmSePDBB8d97TWveU1ERGzbtm30sb1790ZExBVXXBHr1q2bcJsnnHDCmH9Pnz59wueVUsZs88tf/nIcc8wx4543Y8bYXZo1a9a4bml79uyJM888M3bt2hUf/OAHY9WqVTF37tzYvn17bNy4cfQ1XsjevXvj6KOPjltuuWXCry9evPhFt9EpuR/Qz9wjZH8w2Q9H9nI/wJofjuzlfoA1PxzZy/0Aa354st9v0n/q6Oyzz47Pf/7z8a//+q/x6le/+gWfe/zxx0dExMyZM2Pt2rVTG+EhVqxYERERRx999JS3+f3vfz9++MMfxpe+9KV4xzveMfr43XffPe65B19eP3Qc3/rWt+KUU04Zc9n/UPv/PtfmzZtH84iIeOyxx8b9lOSFyP3AOPqZe4TsDx6H7CdvELOX+4FxWPOTN4jZy/3AOKz5yRvE7OV+YBzW/OQNavYREZP+A1If+MAHYs6cOfHOd74zHn300XFf3/9ThYh9oZ5++umxadOm+N///d9xz33ssccm+/Kxbt26mD9/flx99dXx3HPPTWmb+38icvBYSynxqU99atxz9/8dq927d495/Nxzz409e/bExz/+8XHf8/zzz48+f+3atTFz5sy4/vrrx7zedddd96LjPJjc9+l37hGy30/2w5O93Pex5ocne7nvY80PT/Zy38eaH67sI6Zw5XflypVx6623xvnnnx8nnnhiXHDBBfHKV74ySimxdevWuPXWW2PatGmjvxt+4403xqmnnhqrV6+Oiy66KI4//vh49NFH45//+Z/jv//7v+P++++f1OvPnz8/PvOZz8Tb3/72eNWrXhXnnXdeLF68OH784x/HHXfcEaecckrccMMNL7iNVatWxYoVK+KKK66I7du3x/z58+Mb3/jGhD89WLNmTUREvOc974l169bF9OnT47zzzovXve51cckll8Q111wT3/ve9+LXf/3XY+bMmbF58+a47bbb4lOf+lScc845sXjx4rjiiivimmuuifXr18dZZ50V9913X9x1112xaNGijvdb7nVyj5C97Icve7lb88OWvdyt+WHLXu7W/DBmHxET9J3u0JYtW8q73vWucsIJJ5QjjzyyzJ49u6xatapceuml5Xvf+96Y5z788MPlHe94RznmmGPKzJkzy9KlS8v69evL17/+9dHn7G/P/W//9m9jvveee+4pEVHuueeecY+vW7euLFiwoBx55JFlxYoVZePGjeXf//3fR5+z/w8zT+Shhx4qa9euLfPmzSuLFi0qF110Ubn//vtLRJSbbrpp9HnPP/98ueyyy8rixYvLyMjIuFbdn/vc58qaNWvK7Nmzy1FHHVVWr15dPvCBD5T/+Z//GX3Onj17yh/90R+VJUuWdP2HmeW+T79zL0X2+8l+eLKX+z7W/PBkL/d9rPnhyV7u+1jzw5P9SCkHXT8GAACAhCZ9zy8AAAAMGsUvAAAA6Sl+AQAASE/xCwAAQHqKXwAAANJT/AIAAJDejG6+eWRkpKPndfrXlCbaXpN/ianT8fZDN/vVdO69liX3iN6v0cO9xkQOfd1+5DzRvvZjPbZ5DWXOvZ+vMxVtWhcTyZz9RNo0H02fb2qdvzp53aZzb9v5ps2fl5qe216vlcPpx2ebqep0bLXOP207zzc5l206p09kqvvlyi8AAADpKX4BAABIT/ELAABAeopfAAAA0uuq4dVEurnxu9c319e6eb9WQ5y26MfY2n5T/mRMNa+szS4Opy3jqzWOmg0/hj37Ycu5U1nO9U3vR5s/23TTbKcXc9Hm7Lt5n5zoezN9bumlTjNuuiFUzflpSy2V9T3WlV8AAADSU/wCAACQnuIXAACA9BS/AAAApNdVw6tBa8aRRdO5D2Kzo0O1rVnBoGm6uULT2Q/aesxE9uM533Snm/cc2TfL8d25fmTVTdOmfrxuBm071wxT9p2e+3udvSu/AAAApKf4BQAAID3FLwAAAOkpfgEAAEivq4ZX5FDjZvtOb3pvWzOuYWpMABHtOwYZfNYPh5P1fNP2fciaO+3SljXlyi8AAADpKX4BAABIT/ELAABAeopfAAAA0uuq4ZUb5MebKJN+vMag5d7peNu2X9006ppI2/avrbpZ800fk22es5pj68d56dDXaPNcMLiaPt9Yp80b1KaZcKim13KT5682HStNjsWVXwAAANJT/AIAAJCe4hcAAID0FL8AAACk11XDq36Y6g3etW7S7qb5US1NNoSo1VyibRm3pUlAlsZbTTebqZHLoDZeqdWcblDzarMmjw9zMbG2nG+ya3NWzl0TG7TP823Tj/fYGmqcH135BQAAID3FLwAAAOkpfgEAAEhP8QsAAEB61RpedXOjdZubCbTlBvLD6XR8U70BvS3zUFs/1sEwZd3pMd90w7mpnmv6Mf/9ahLRzfm27efDTtTch27muNfn+m4M4pqqdUw3eb7pZg4H4Vhu85rvRpvWfFveY9knw5rvdE01uVZc+QUAACA9xS8AAADpKX4BAABIT/ELAABAeo03vBqEpghNadO+tmksvda2fW3LePrROKIt+xrRrrF0IlMTj0HLPrtBm4+2N7fKwPmmP5oeW5v3NaL945uqtu1X28ZzsKabZ9XYV1d+AQAASE/xCwAAQHqKXwAAANJT/AIAAJBe4w2v+qFGI4c233zeL3KvZ6rZd9MEq9PvzTxHnWTVj/3PnPHhON/UI/s6mszd+XtyapzrZd/7c42M92ky5zY3suqUK78AAACkp/gFAAAgPcUvAAAA6Sl+AQAASG8gG171Wptv0u4XDU+aV6PZSNPNrSCL7OebNpN95zrJShPDemRPZlk/G7ryCwAAQHqKXwAAANJT/AIAAJDe0N/z6/6KifX69/yHMfdu9rnJ+Wh6HFn0+o/Ad5Nd9nvDen0PUaasmib7OtqS+zDOTzf36Tb5mu4D7oycJqfXn2UmMmjZu/ILAABAeopfAAAA0lP8AgAAkJ7iFwAAgPSqNbyq8UeSB+2G7H5pS+MN9plqg4GmG0Bknrdu1nwnuXSaXadzlmkunG/qkf3g6WTOmj7fZNLrhj1NN2MaxPlo+v20yTVP8zJk78ovAAAA6Sl+AQAASE/xCwAAQHqKXwAAANLrS8OrTm/+r9EEq2lt2odhaio2CI08aszHIOQy6GR8eG06H8Ig6qSxIYfX5GfNbppnZX6f6GZ/O3le5uy61fT5oC259vo858ovAAAA6Sl+AQAASE/xCwAAQHqKXwAAANJrvOFVP26+bvPN752Oo1bTiqYbjbUl90HQdPadfK/5ad6huXeTcab5qdHYMFN+g0b2/dHk+WZQdfOZr9PzTY330zZ/lu2FJudiGPXjPbbX55u2rHlXfgEAAEhP8QsAAEB6il8AAADSU/wCAACQXuMNrzq9+brpG9+nepN2P26sb1MDg6abW7U597bpdJ+n2sCgm+Zw/WhU1w/9aN421dcYxsZyNY7zNjUTrEn2dXRznm/D9tuo6XNzk9tres3Xmt9+NBXr9WebfujXcdV0jZR1zU91LK78AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACC9rhpe1WoWVeOG7G70Yyz9aFg0aLnX1OZmM4O4HvvxGm1pZOUY6lybsqo5lhrvxbJvzzmom2110+iopm6yqnGub7t+vBdlyL1NYzmcbpqfdvJ9tTQ5Fld+AQAASE/xCwAAQHqKXwAAANJT/AIAAJBeVw2v2nyDfGaD2Eyohn7tw6Ct+aaP20Fs2lRjzJ02kemmWcWgrnkm1qZmRFOd80E8P3Sj0/1ty/mmTY0S2/jaU9GPc/igZUL/WEMvzpVfAAAA0lP8AgAAkJ7iFwAAgPQUvwAAAKTXVcMrYPBovFFnzJlyH7YmRrXUam7VpCzroh/HZZPHVTfjyDJnh9Pr81fbGhTSbsO+DmocG678AgAAkJ7iFwAAgPQUvwAAAKSn+AUAACC9kdLFXcWdNuMYtJu5m775uu3bGzTDvv/sYx30R5vP821aA/0aS5v2GaDXap3znGvbpcn5cOUXAACA9BS/AAAApKf4BQAAID3FLwAAAOnNaHqDbWk20uk4Om3m0g03yDerZp4aINTRj+OUzrVlzU80jqYbdNVce9Y9eN/thzZnXGsc/XrdNmffD53uf5OZuPILAABAeopfAAAA0lP8AgAAkJ7iFwAAgPRGyjDdVQ0AAMBQcuUXAACA9BS/AAAApKf4BQAAID3FLwAAAOkpfgEAAEhP8QsAAEB6il8AAADSU/wCAACQnuIXAACA9P4f9CuIDfmGtCsAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Generate samples\n", "generated_samples = rbm.generate_image(n_samples=10, n_gibbs_steps=1)\n", @@ -1977,17 +497,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RBM(n_visible=320, n_hidden=200)\n" - ] - } - ], + "outputs": [], "source": [ "print(rbm)" ] @@ -2001,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2111,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2120,99 +632,9 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 800.29it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.15315.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 666.26it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 666.93it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 801.09it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 800.82it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 666.26it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 667.17it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 571.72it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 800.33it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 666.85it/s]\n", - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 3987.93it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.03169.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 3956.89it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 3990.77it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 4016.57it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1996.81it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 4004.11it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 4004.11it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 2007.32it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 1998.48it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 3995.53it/s]\n", - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 4002.20it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.00677.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 2003.73it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 2000.14it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 4005.06it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 3993.62it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 3994.58it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 4012.73it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 4003.15it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 2003.01it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 3998.38it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "DBN([\n", - " RBM(n_visible=320, n_hidden=100),\n", - " RBM(n_visible=100, n_hidden=50),\n", - " RBM(n_visible=50, n_hidden=25)\n", - "])" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "n_visible=data.shape[1]\n", "hidden_layer_sizes = [100, 50, 25]\n", @@ -2223,17 +645,9 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[RBM(n_visible=100, n_hidden=50), RBM(n_visible=50, n_hidden=25)]\n" - ] - } - ], + "outputs": [], "source": [ "# Check if the RBM are accessibles via a slicing \n", "print(dbn[1:3])" @@ -2241,20 +655,9 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# # Generate images\n", "generated_images = dbn.generate_image(n_samples=5, n_gibbs_steps=1)\n", @@ -2271,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2414,85 +817,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 498.09it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.15315.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 499.89it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 571.16it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 499.74it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 499.96it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 500.10it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 499.99it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 571.29it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 444.55it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 499.90it/s]\n", - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 1335.02it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.03169.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 2003.25it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 1999.91it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 3995.53it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1999.43it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 1995.86it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 1331.95it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 666.42it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 1333.96it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1333.75it/s]\n", - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 1333.75it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.00677.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 1335.13it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 2001.82it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 2001.10it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1333.43it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 2004.93it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 1999.19it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 1997.76it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 2003.49it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1998.00it/s]\n" - ] - } - ], + "outputs": [], "source": [ "n_visible=data.shape[1]\n", "hidden_layer_sizes = [100, 50, 25]\n", @@ -2512,22 +839,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No. network output = 5\n", - "Input data (0): (39, 320)\n", - "Hidden layer input (1): (39, 100)\n", - "Hidden layer input (2): (39, 50)\n", - "Hidden layer input (3): (39, 25)\n", - "Softmax output (4): (39, 20)\n" - ] - } - ], + "outputs": [], "source": [ "layer_outputs, softmax_output = dnn.input_output(data)\n", "n_layers_net = len(layer_outputs) + 1\n", @@ -2539,15 +853,6 @@ "\n", "print(f\"Softmax output ({n_layers_net - 1}):\", softmax_output.shape)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dnn." - ] } ], "metadata": { diff --git a/notebook/experiments_ALPHA_DIGITS.ipynb b/notebook/experiments_ALPHA_DIGITS.ipynb index 6354b46..861b420 100644 --- a/notebook/experiments_ALPHA_DIGITS.ipynb +++ b/notebook/experiments_ALPHA_DIGITS.ipynb @@ -4,481 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Restricted Botlzman Machines (RBM)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#FIXME: Review the generation process (theoretically) and fix the implementation " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from typing import List, Dict, Tuple, Literal, Optional, Union, Iterable\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import scipy.io\n", - "from tqdm import tqdm\n", - "from numpy._typing import ArrayLike\n", - "\n", - "ArrayLike = Union[List, Tuple, np.ndarray]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "DATA_FOLDER = \"../data/\"\n", - "ALPHA_DIGIT_PATH = os.path.join(DATA_FOLDER, \"binaryalphadigs.mat\")\n", - "MNIST_PATH = os.path.join(DATA_FOLDER, \"mnist_all.mat\")\n", - "\n", - "if not os.path.exists(ALPHA_DIGIT_PATH):\n", - " raise FileNotFoundError(f\"The file {ALPHA_DIGIT_PATH} does not exist.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.1 Implementing a RBM and testing on Binary AlphaDigits" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def _load_data(file_path: str) -> Dict[str, np.ndarray]:\n", - " \"\"\"\n", - " Load Binary AlphaDigits data from a .mat file.\n", - "\n", - " Parameters:\n", - " - file_path (str): Path to the .mat file containing the data.\n", - "\n", - " Returns:\n", - " - data (dict): Loaded data dictionary.\n", - " \"\"\"\n", - " if file_path is None:\n", - " raise ValueError(\"File path must be provided.\")\n", - "\n", - " return scipy.io.loadmat(file_path)\n", - "\n", - "\n", - "data = _load_data(ALPHA_DIGIT_PATH)\n", - "class_labels = data[\"classlabels\"].flatten() \n", - "class_count = data[\"classcounts\"].flatten()\n", - "df = pd.DataFrame(\n", - " {\n", - " \"Class Labels\": class_labels,\n", - " \"Class Count\": class_count\n", - " }\n", - ")\n", - "df[\"Class Labels\"] = df[\"Class Labels\"].apply(lambda x: x[0])\n", - "df[\"Class Count\"] = df[\"Class Count\"].apply(lambda x: x[0][0])\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def _load_data(file_path: str, which: Literal[\"alphadigit\", \"mnist\"]=\"alphadigit\") -> Dict[str, np.ndarray]:\n", - " \"\"\"\n", - " Load Binary AlphaDigits data from a .mat file.\n", - "\n", - " Parameters:\n", - " - file_path (str): Path to the .mat file containing the data.\n", - " - which (Literal[\"alphadigit\", \"mnist\"], optional): Specifies \n", - " which data to load. The default value is \"alphadigit\".\n", - "\n", - " Returns:\n", - " - data (dict): A dictionary containing the loaded data.\n", - "\n", - " Raises:\n", - " - ValueError: If the file_path parameter is None.\n", - " - ValueError: If the which parameter is not \"alphadigit\".\n", - "\n", - " Example Usage:\n", - " ```python\n", - " data = _load_data(\"data.mat\", \"alphadigit\")\n", - " ```\n", - " \"\"\"\n", - " if file_path is None:\n", - " raise ValueError(\"File path must be provided.\")\n", - " \n", - " if which == \"alphadigit\":\n", - " return scipy.io.loadmat(file_path)[\"dat\"]\n", - " \n", - " raise ValueError(\"MNIST NOT YET AVAILABLE.\")\n", - "\n", - "alphadigit_data = _load_data(ALPHA_DIGIT_PATH) \n", - "print(alphadigit_data.shape)\n", - "print(alphadigit_data[0][0].shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def _map_characters_to_indices(characters: Union[str, int, List[Union[str, int]]]) -> List[int]:\n", - " \"\"\"\n", - " Map alphanumeric character to its corresponding index.\n", - "\n", - " Parameters:\n", - " - character (str, int, list of str or int): Alphanumeric character or its index.\n", - "\n", - " Returns:\n", - " - char_index (int): Corresponding index for the character.\n", - " \"\"\"\n", - " if isinstance(characters, list):\n", - " return [_map_characters_to_indices(char) for char in characters]\n", - " if isinstance(characters, int) and 0 <= characters <= 35:\n", - " return [characters]\n", - " if (isinstance(characters, str) and characters.isdigit()\n", - " and 0 <= int(characters) <= 9):\n", - " return [int(characters)]\n", - " if (isinstance(characters, str) and characters.isalpha()\n", - " and 'A' <= characters.upper() <= 'Z'):\n", - " return [ord(characters.upper()) - ord('A') + 10]\n", - " \n", - " raise ValueError(\n", - " \"Invalid character input. It should be an alphanumeric\" \n", - " \"character '[0-9|A-Z]' or its index representing '[0-35]'.\"\n", - " )\n", - "\n", - "for char in [0, 10, \"A\", [1, \"C\"], 36]:\n", - " try:\n", - " map = _map_characters_to_indices(char)\n", - " print(f\"{char} > map to > {map}\")\n", - " except:\n", - " print(f\"{char} > no mapping available, out of range\")" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def read_alpha_digit(characters: Optional[Union[str, int, List[Union[str, int]]]] = None,\n", - " file_path: Optional[str] = ALPHA_DIGIT_PATH,\n", - " data: Optional[Dict[str, np.ndarray]] = None,\n", - " use_data: bool = False,\n", - " ) -> np.ndarray:\n", - " \"\"\"\n", - " Reads binary AlphaDigits data from a .mat file or uses already loaded data. \n", - " It extracts the data for a specified alphanumeric character or its index, and \n", - " flattens the images into one-dimensional vectors.\n", - "\n", - " Parameters:\n", - " - characters (Union[str, int, List[Union[str, int]]], optional): Alphanumeric character \n", - " or its index whose data needs to be extracted. It can be a single character or \n", - " a list of characters. Default is None.\n", - " - file_path (str, optional): Path to the .mat file containing the data. \n", - " Default is None.\n", - " - data (dict, optional): Already loaded data dictionary. \n", - " Default is None.\n", - " - use_data (bool): Flag to indicate whether to use already loaded data.\n", - " Default is False.\n", - "\n", - " Returns:\n", - " - flattened_images (numpy.ndarray): Flattened images for the specified character(s).\n", - " \"\"\"\n", - " if not use_data:\n", - " data = _load_data(file_path, which=\"alphadigit\")\n", - "\n", - " char_indices = _map_characters_to_indices(characters)\n", - "\n", - " # Select the rows corresponding to the characters indices.\n", - " char_data: np.ndarray = data[char_indices]\n", - " \n", - " # Flatten each image into a one-dimensional vector.\n", - " flattened_images = np.array([image.flatten() for image in char_data.flatten()])\n", - " return flattened_images\n", - "\n", - "def plot_characters(chars, data):\n", - " num_chars = len(chars)\n", - " num_images_per_char = data.shape[0] // num_chars\n", - " fig, ax = plt.subplots(1, num_chars, figsize=(num_chars * 2, 2))\n", - "\n", - " for i, char in enumerate(chars):\n", - " # Find the index of the first image corresponding to the current char\n", - " start_index = i * num_images_per_char\n", - " image = data[start_index].reshape(20, 16)\n", - " ax[i].imshow(image, cmap='gray')\n", - " ax[i].set_title(f'Char: {char}')\n", - " ax[i].axis('off')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "# Example\n", - "chars = [2, \"Y\", 7, \"Z\"]\n", - "data = read_alpha_digit(chars, data=alphadigit_data, use_data=True)\n", - "plot_characters(chars, data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"data shape:\", data.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class RBM:\n", - " def __init__(self, n_visible: int, n_hidden: int=100, random_state=None) -> None:\n", - " \"\"\"\n", - " Initialize the Restricted Boltzmann Machine.\n", - "\n", - " Parameters:\n", - " - n_visible (int): Number of visible units.\n", - " - n_hidden (int): Number of hidden units. Default 100.\n", - " - random_state: Random seed for reproducibility.\n", - " \"\"\"\n", - " self.n_visible = n_visible\n", - " self.n_hidden = n_hidden\n", - " \n", - " self.a = np.zeros((1, n_visible)) # visible_bias\n", - " self.b = np.zeros((1, n_hidden)) # hidden_bias\n", - " self.rng = np.random.default_rng(random_state)\n", - " self.W = 1e-4 * self.rng.standard_normal(size=(n_visible, n_hidden)) # weights\n", - "\n", - " def __repr__(self) -> str:\n", - " return f\"RBM(n_visible={self.n_visible}, n_hidden={self.n_hidden})\"\n", - "\n", - " def _sigmoid(self, x: np.ndarray) -> np.ndarray:\n", - " \"\"\"\n", - " Sigmoid activation function.\n", - "\n", - " Parameters:\n", - " - x (numpy.ndarray): Input array.\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Result of applying the sigmoid function to the input.\n", - " \"\"\"\n", - " return 1 / (1 + np.exp(-x))\n", - " \n", - " def _reconstruction_error(self, input: np.ndarray, image: np.ndarray) -> float:\n", - " \"\"\"\n", - " Compute reconstruction error.\n", - "\n", - " Parameters:\n", - " - input (numpy.ndarray): Original input data.\n", - " - image (numpy.ndarray): Reconstructed image.\n", - "\n", - " Returns:\n", - " - float: Reconstruction error.\n", - " \"\"\"\n", - " return np.round(np.power(image - input, 2).mean(), 5)\n", - "\n", - " def input_output(self, data: np.ndarray) -> np.ndarray:\n", - " \"\"\"\n", - " Compute hidden units given visible units.\n", - "\n", - " Parameters:\n", - " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Hidden unit activations, shape (n_samples, n_hidden).\n", - " \"\"\"\n", - " return self._sigmoid(data @ self.W + self.b)\n", - "\n", - " def output_input(self, data_h: np.ndarray) -> np.ndarray:\n", - " \"\"\"\n", - " Compute visible units given hidden units.\n", - "\n", - " Parameters:\n", - " - data_h (numpy.ndarray): Hidden unit activations, shape (n_samples, n_hidden).\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Reconstructed visible units, shape (n_samples, n_visible).\n", - " \"\"\"\n", - " return self._sigmoid(data_h @ self.W.T + self.a)\n", - " \n", - " def calcul_softmax(self, data: np.ndarray) -> np.ndarray:\n", - " \"\"\"\n", - " Calculate softmax probabilities for the output units.\n", - "\n", - " Parameters:\n", - " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Softmax probabilities, shape (n_samples, n_hidden).\n", - " \"\"\"\n", - " # Compute activations for the hidden layer\n", - " hidden_activations = self.input_output(data)\n", - " \n", - " # Compute softmax probabilities for the output layer\n", - " exp_hidden_activations = np.exp(hidden_activations)\n", - " softmax_probs = exp_hidden_activations / np.sum(exp_hidden_activations, axis=1, keepdims=True)\n", - " \n", - " return softmax_probs\n", - "\n", - " def update(\n", - " self, \n", - " batch: np.ndarray,\n", - " learning_rate: float=0.1,\n", - " batch_size: Optional[int]=None,\n", - " return_output: bool=False\n", - " ):\n", - " \"\"\"_summary_\n", - "\n", - " Args:\n", - " batch (np.ndarray): _description_\n", - " learning_rate (float, optional): _description_. Defaults to 0.1.\n", - " batch_size (Optional[int], optional): _description_. Defaults to None.\n", - " return_output (bool, optional): _description_. Defaults to False.\n", - " \"\"\"\n", - " if not batch_size:\n", - " batch_size = batch.shape[0]\n", - " pos_h_probs = self.input_output(batch)\n", - " pos_v_probs = self.output_input(pos_h_probs)\n", - " neg_h_probs = self.input_output(pos_v_probs)\n", - " \n", - " # Update weights and biases\n", - " self.W += learning_rate * (batch.T @ pos_h_probs - pos_v_probs.T @ neg_h_probs) / batch_size\n", - " self.b += learning_rate * (pos_h_probs - neg_h_probs).mean(axis=0)\n", - " self.a += learning_rate * (batch - pos_v_probs).mean(axis=0)\n", - "\n", - " if return_output:\n", - " return self, pos_v_probs\n", - " \n", - " return self \n", - "\n", - " def train(self, \n", - " data: np.ndarray,\n", - " learning_rate: float=0.1,\n", - " n_epochs: int=10,\n", - " batch_size: int=10,\n", - " print_each=10\n", - " ) -> 'RBM':\n", - " \"\"\"\n", - " Train the RBM using Contrastive Divergence.\n", - "\n", - " Parameters:\n", - " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", - " - n_epochs (int): Number of training epochs. Default is 10.\n", - " - batch_size (int): Size of mini-batches. Default is 10.\n", - "\n", - " Returns:\n", - " - RBM: Trained RBM instance.\n", - " \"\"\"\n", - " n_samples = data.shape[0]\n", - " for epoch in range(n_epochs):\n", - " self.rng.shuffle(data)\n", - " for i in tqdm(range(0, n_samples, batch_size), desc=f\"Epoch {epoch}\"):\n", - " batch = data[i:i+batch_size]\n", - " _, pos_v_probs = self.update(\n", - " batch=batch,\n", - " learning_rate=learning_rate,\n", - " batch_size=batch_size,\n", - " return_output=True\n", - " )\n", - " \n", - " if epoch % print_each == 0:\n", - " tqdm.write(\n", - " f\"Reconstruction error: {self._reconstruction_error(batch, pos_v_probs)}.\")\n", - "\n", - " return self\n", - "\n", - " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", - " \"\"\"\n", - " Generate samples from the RBM using Gibbs sampling.\n", - "\n", - " Parameters:\n", - " - n_samples (int): Number of samples to generate. Default is 10.\n", - " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 1.\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", - " \"\"\"\n", - " samples = np.zeros((n_samples, self.n_visible))\n", - " \n", - " # Matrix of initlization value of Gibbs samples for each sample. \n", - " V = self.rng.binomial(1, self.rng.random(), size=n_samples*self.n_visible).reshape((n_samples, self.n_visible))\n", - " for i in range(n_samples):\n", - " for _ in range(n_gibbs_steps):\n", - " h_probs = self._sigmoid(V[i] @ self.W + self.b) # vector\n", - " h = self.rng.binomial(1, h_probs)\n", - " v_probs = self._sigmoid(h @ self.W.T + self.a)\n", - " v = self.rng.binomial(1, v_probs)\n", - " samples[i] = v\n", - " return samples" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the alpha_digit data\n", - "data = read_alpha_digit(file_path=ALPHA_DIGIT_PATH, characters=['Z'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Parameters\n", - "n_visible = data.shape[1] # Number of visible units (size of each image)\n", - "n_hidden = 200 # Number of hidden units (hyperparameter)\n", - "\n", - "# Initialize RBM\n", - "rbm = RBM(n_visible=n_visible, n_hidden=n_hidden, random_state=42)\n", - "print(rbm)\n", - "\n", - "# Train RBM\n", - "rbm.train(data, learning_rate=0.1, n_epochs=500, batch_size=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.testing.assert_allclose(rbm.calcul_softmax(data).sum(1), 1)" + "# Analysis on ALPHA DIGITS " ] }, { @@ -487,23 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Generate samples\n", - "generated_samples = rbm.generate_image(n_samples=10, n_gibbs_steps=1)\n", - "\n", - "# Plot original and generated samples\n", - "plt.figure(figsize=(12, 6))\n", - "for i in range(10):\n", - " plt.subplot(2, 10, i + 1)\n", - " plt.imshow(data[i].reshape(20, 16), cmap='gray')\n", - " plt.title('Original')\n", - " plt.axis('off')\n", - " \n", - " plt.subplot(2, 10, i + 11)\n", - " plt.imshow(generated_samples[i].reshape(20, 16), cmap='gray')\n", - " plt.title('Generated')\n", - " plt.axis('off')\n", - "\n", - "plt.show()" + "!pip install nbformat" ] }, { @@ -512,369 +22,26 @@ "metadata": {}, "outputs": [], "source": [ - "print(rbm)" + "%run experiments.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 3.2 Implementing a Deep Belief Network (DBN) and test on Binary AlphaDigits" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class DBN:\n", - " def __init__(self, n_visible: int, hidden_layer_sizes: list[int], random_state=None):\n", - " \"\"\"\n", - " Initialize the Deep Belief Network.\n", - "\n", - " Parameters:\n", - " - n_visible (int): Number of visible units.\n", - " - hidden_layer_sizes (list[int]): List of sizes for each hidden layer.\n", - " - random_state: Random seed for reproducibility.\n", - " \"\"\"\n", - " self.n_visible = n_visible\n", - " self.hidden_layer_sizes = hidden_layer_sizes\n", - " self.rbms: List[RBM] = []\n", - " self.rng = np.random.default_rng(random_state)\n", - "\n", - " # Initialize the first RBM\n", - " first_rbm = RBM(\n", - " n_visible=n_visible,\n", - " n_hidden=hidden_layer_sizes[0],\n", - " random_state=random_state,\n", - " )\n", - " self.rbms.append(first_rbm)\n", - "\n", - " # Initialize RBMs for subsequent hidden layers\n", - " for i, size in enumerate(hidden_layer_sizes[1:], start=1):\n", - " rbm = RBM(\n", - " n_visible=hidden_layer_sizes[i - 1],\n", - " n_hidden=size,\n", - " random_state=random_state,\n", - " )\n", - " self.rbms.append(rbm)\n", - "\n", - "\n", - " def __getitem__(self, key):\n", - " return self.rbms[key]\n", - " \n", - "\n", - " def __repr__(self):\n", - " \"\"\"\n", - " Return a string representation of the DBN object.\n", - " \"\"\"\n", - " rbm_reprs = [f\"{'':4}{repr(rbm)}\" for rbm in self.rbms]\n", - " join_rbm_reprs = ',\\n'.join(rbm_reprs)\n", - " return f\"DBN([\\n{join_rbm_reprs}\\n])\"\n", - "\n", - "\n", - " def train(self,\n", - " data: np.ndarray,\n", - " learning_rate: float=0.1,\n", - " n_epochs: int=10,\n", - " batch_size: int=10,\n", - " print_each: int=10,\n", - " ) -> \"DBN\":\n", - " \"\"\"\n", - " Train the DBN using Greedy layer-wise procedure.\n", - "\n", - " Parameters:\n", - " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", - " - n_epochs (int): Number of training epochs. Default is 10.\n", - " - batch_size (int): Size of mini-batches. Default is 10.\n", - " - print_each: Print reconstruction error each `print_each` epochs.\n", - " - verbose\n", - "\n", - " Returns:\n", - " - DBN: Trained DBN instance.\n", - " \"\"\"\n", - " input_data = data\n", - " for rbm in self.rbms:\n", - " rbm.train(\n", - " input_data,\n", - " learning_rate=learning_rate,\n", - " n_epochs=n_epochs,\n", - " batch_size=batch_size,\n", - " print_each=print_each,\n", - " )\n", - " # Update input data for the next RBM\n", - " input_data = rbm.input_output(input_data)\n", - "\n", - " return self\n", - "\n", - " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", - " \"\"\"\n", - " Generate samples from the DBN using Gibbs sampling.\n", - "\n", - " Parameters:\n", - " - n_samples (int): Number of samples to generate. Default is 1.\n", - " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100.\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", - " \"\"\"\n", - " # samples = np.zeros((n_samples, self.n_visible))\n", - "\n", - " # Generate samples using the first RBM in the DBN\n", - " samples = self.rbms[-1].generate_image(n_samples, n_gibbs_steps)\n", - " for rbm in reversed(self.rbms[:-1]):\n", - " # Sample from the conditional probability of layer l-1 given layer l: p(h_{s-1}|h_{s}).\n", - " h_probs = rbm.output_input(samples)\n", - " h = self.rng.binomial(1, p=h_probs) \n", - " samples = h\n", - " return samples" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from principal_dbn_alpha import DBN" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "n_visible=data.shape[1]\n", - "hidden_layer_sizes = [100, 50, 25]\n", - "\n", - "dbn = DBN(n_visible=n_visible, hidden_layer_sizes=hidden_layer_sizes, random_state=42)\n", - "dbn.train(data, learning_rate=0.1, n_epochs=10, batch_size=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Check if the RBM are accessibles via a slicing \n", - "print(dbn[1:3])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# # Generate images\n", - "generated_images = dbn.generate_image(n_samples=5, n_gibbs_steps=1)\n", - "\n", - "# Display generated images\n", - "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(12, 4))\n", - "for i in range(5):\n", - " axes[i].imshow(generated_images[i].reshape(20, 16), cmap='gray')\n", - " axes[i].set_title(f\"Image {i+1}\")\n", - " axes[i].axis('off')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class DNN(DBN):\n", - " def __init__(\n", - " self,\n", - " input_dim: int,\n", - " output_dim: int,\n", - " hidden_layer_sizes: List[int],\n", - " random_state=None\n", - " ):\n", - " \"\"\"\n", - " Initialize the Deep Neural Network (DNN).\n", - "\n", - " Parameters:\n", - " - input_dim (int): Dimension of the input.\n", - " - output_dim (int): Dimension of the output.\n", - " - hidden_layer_sizes (List[int]): List of sizes for each hidden layer.\n", - " - random_state: Random seed for reproducibility.\n", - " \"\"\"\n", - " super().__init__(\n", - " n_visible=input_dim,\n", - " hidden_layer_sizes=hidden_layer_sizes,\n", - " random_state=random_state\n", - " )\n", - " #--> self.rbms contains only the pre-trainable RBMs \n", - " self.clf = RBM(self.rbms[-1].n_hidden, output_dim)\n", - " self.network = self.rbms + [self.clf] # DNN = [DBN + Classifier] ~ [RBM_0,...,RBM_N, RBM_Clf]\n", - "\n", - " def __getitem__(self, key):\n", - " return self.network[key]\n", - " \n", - " def __repr__(self):\n", - " join_repr = \"\\n\".join([f\"{'':4}{repr(rbm)},\" for rbm in self.network])\n", - " return f\"DNN([\\n{join_repr} \\n])\"\n", - " \n", - " \n", - " def pretrain(self, n_epochs: int, learning_rate: float, batch_size: int, data: np.ndarray) -> \"DNN\":\n", - " \"\"\"\n", - " Pretrain the hidden layers of the DNN using the DBN training method.\n", - "\n", - " Parameters:\n", - " - n_epochs (int): Number of training epochs.\n", - " - learning_rate (float): Learning rate for gradient descent.\n", - " - batch_size (int): Size of mini-batches.\n", - " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - "\n", - " Returns:\n", - " - DNN: Pretrained DNN instance.\n", - " \"\"\"\n", - " # NOTE: Use the inherited `train` method to perform pre-training since `self.rbms`\n", - " # only contains the pre-trainable RBMs.\n", - " return self.train(data, n_epochs=n_epochs, learning_rate=learning_rate, batch_size=batch_size)\n", - " \n", - " def input_output(self, input_data: np.ndarray) -> Tuple[List[np.ndarray], np.ndarray]:\n", - " \"\"\"\n", - " Get the outputs on each layer of the DNN and the softmax probabilities on the output layer.\n", - "\n", - " Parameters:\n", - " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - "\n", - " Returns:\n", - " - Tuple[List[np.ndarray], np.ndarray]: Outputs on each layer & softmax probabilities.\n", - " \"\"\"\n", - " layer_outputs = []\n", - " \n", - " # Input layer output\n", - " layer_outputs.append(input_data)\n", - " \n", - " # Hidden layers output\n", - " for rbm in self.rbms:\n", - " layer_outputs.append(rbm.input_output(layer_outputs[-1]))\n", - " \n", - " # Softmax probabilities on the output layer\n", - " output_probs = self.network[-1].calcul_softmax(layer_outputs[-1])\n", - " \n", - " return layer_outputs, output_probs\n", - " \n", - "\n", - " def _cross_entropy(batch_labels: np.ndarray, output_probs: np.ndarray, eps: float = 1e-15) -> float:\n", - " \"\"\"\n", - " Calculate the cross entropy between the batch labels and output probabilities.\n", - "\n", - " Parameters:\n", - " - batch_labels (numpy.ndarray): True labels for the batch, shape (batch_size, n_classes).\n", - " - output_probs (numpy.ndarray): Predicted probabilities for the batch, shape (batch_size, n_classes).\n", - " - eps (float): Small value to avoid numerical instability in logarithm calculation. Default is 1e-15.\n", - "\n", - " Returns:\n", - " - float: Cross entropy value.\n", - " \"\"\"\n", - " return -np.mean(np.sum(batch_labels * np.log(output_probs + eps), axis=1))\n", - "\n", - "\n", - " def backpropagation(\n", - " self,\n", - " input_data: np.ndarray,\n", - " labels: np.ndarray,\n", - " n_epochs: int,\n", - " learning_rate: float,\n", - " batch_size: int,\n", - " eps: float = 1e-15\n", - " ) -> \"DNN\":\n", - " \"\"\"\n", - " Estimate the weights/biases of the network using backpropagation algorithm.\n", - "\n", - " Parameters:\n", - " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - " - labels (numpy.ndarray): Labels for the input data, shape (n_samples, n_classes).\n", - " - n_epochs (int): Number of training epochs.\n", - " - learning_rate (float): Learning rate for gradient descent.\n", - " - batch_size (int): Size of mini-batches.\n", - " - eps (float): Small value to avoid numerical instability in logarithm calculation. Default is 1e-15.\n", - "\n", - " Returns:\n", - " - DNN: Updated DNN instance.\n", - " \"\"\"\n", - " n_samples = input_data.shape[0]\n", - " \n", - " for epoch in tqdm(range(n_epochs), desc=\"Training\", unit=\"epoch\"):\n", - " for batch_start in range(0, n_samples, batch_size):\n", - " batch_end = min(batch_start + batch_size, n_samples)\n", - " batch_input = input_data[batch_start:batch_end]\n", - " batch_labels = labels[batch_start:batch_end]\n", - "\n", - " # Forward pass\n", - " layer_outputs, output_probs = self.input_output(batch_input)\n", - "\n", - " # Backward pass (update weights and biases)\n", - " self.network[-1].update(batch_labels, layer_outputs[-1], learning_rate)\n", - " for i in range(len(self.network) - 2, -1, -1):\n", - " self.network[i].update(layer_outputs[i], layer_outputs[i + 1], self.network[i + 1].weights, learning_rate)\n", - "\n", - " # Calculate cross entropy after each epoch\n", - " loss = self._cross_entropy(batch_labels, output_probs, eps)\n", - " tqdm.write(f\"Epoch {epoch + 1}/{n_epochs}, Cross Entropy: {loss}\")\n", - "\n", - " return self\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "n_visible=data.shape[1]\n", - "hidden_layer_sizes = [100, 50, 25]\n", - "output_dim = 20\n", - "\n", - "dnn = DNN(input_dim=n_visible, hidden_layer_sizes=hidden_layer_sizes, output_dim=output_dim, random_state=42)\n", - "# keep last RBM's weights for further test.\n", - "weights = dnn[-1].W \n", - "\n", - "dnn.train(data, learning_rate=0.1, n_epochs=10, batch_size=10)\n", - "\n", - "# Check that the last RBM has not been trained.\n", - "np.testing.assert_equal (dnn[-1].a, 0) # visible bias\n", - "np.testing.assert_equal (dnn[-1].b, 0) # hidden bias\n", - "np.testing.assert_equal (dnn[-1].W, weights) # weights" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "layer_outputs, softmax_output = dnn.input_output(data)\n", - "n_layers_net = len(layer_outputs) + 1\n", - "print(\"No. network output =\", n_layers_net)\n", - "\n", - "print(f\"Input data (0): {layer_outputs[0].shape}\")\n", - "for idx, layer_output in enumerate(layer_outputs[1:]):\n", - " print(f\"Hidden layer input ({idx+1}): {layer_output.shape}\")\n", - "\n", - "print(f\"Softmax output ({n_layers_net - 1}):\", softmax_output.shape)" + "## I- Effect of Layers and units" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Analysis on ALPHA DIGITS " + "we create a special function : generate_symmetric_configurations to create liste of hidden layer we want to use for the experiment" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -901,13 +68,6 @@ " return configurations" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## I- Effect of Layers and units" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -965,7 +125,7 @@ "outputs": [], "source": [ "hidden_units_sizes = [100, 200, 300, 400, 500, 600, 700]\n", - "run_rbm_experiment(hidden_units_sizes, n_epochs=1000, character_sets=['Y'])\n" + "run_rbm_experiment(hidden_units_sizes, n_epochs=2, character_sets=['Y'])\n" ] }, { @@ -992,7 +152,7 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "def run_experiment(hidden_layers_sizes, n_epochs=100, characters=['A', 'B', '1', '2']):\n", + "def run_dbm_experiment(hidden_layers_sizes, n_epochs=100, characters=['A', 'B', '1', '2']):\n", " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", "\n", " # Déterminer le nombre maximum de couches et le nombre unique d'unités\n", @@ -1046,7 +206,7 @@ "source": [ "# Exemple d'utilisation avec des configurations générées\n", "configurations = generate_symmetric_configurations(min_layers = 2, max_layers = 5, min_neurons = 100, max_neurons = 700, step_neurons = 100)\n", - "run_experiment(configurations, n_epochs=1000, characters=['Y'])" + "run_dbm_experiment(configurations, n_epochs=1000, characters=['Y'])" ] }, { @@ -1065,95 +225,53 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# configuration = 2 layer with 200 units each\n", - "configurations_fixe = generate_symmetric_configurations(min_layers = 2, max_layers = 2, min_neurons = 200, max_neurons = 200, step_neurons = 100)\n", - "\n", - "run_experiment(configurations_fixe, n_epochs=2, characters = ['Y'])\n", - "run_experiment(configurations_fixe, n_epochs=2, characters = ['E', 'Y'])\n", - "run_experiment(configurations_fixe, n_epochs=2, characters = ['E', 'Y', '2'])\n", - "run_experiment(configurations_fixe, n_epochs=2, characters = ['E', 'Y', 'G', '2'])\n", - "run_experiment(configurations_fixe, n_epochs=2, characters=['E', 'Y', 'G', '2', '7'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Exemple d'utilisation\n", - "hidden_units_sizes = [100, 200, 300, 400, 500, 600, 700]\n", - "run_rbm_experiment(hidden_units_sizes, n_epochs=2, character_sets=[['A', 'B'], ['A', 'B', 'C'], ['A', 'B', 'C', 'D']])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. DBM" - ] - }, - { - "cell_type": "code", - "execution_count": 74, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os\n", "\n", - "def run_experiment(hidden_layers_sizes, n_epochs=100, characters=['A', 'B', '1', '2']):\n", - " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", - "\n", - " # Déterminer le nombre maximum de couches et le nombre unique d'unités\n", - " max_layers = max(len(sizes) for sizes in hidden_layers_sizes)\n", - " unique_units = sorted({sizes[0] for sizes in hidden_layers_sizes})\n", + "def run_rbm_experiment(hidden_units_sizes, n_epochs=100, character_sets=[['A', 'B'], ['1', '2', '3', '4'], ['A', 'B', '1', '2']]):\n", + " unique_units = sorted(hidden_units_sizes)\n", "\n", " # Préparer une grille de subplots\n", - " fig, axes = plt.subplots(len(unique_units), max_layers, figsize=(max_layers * 3, len(unique_units) * 3), squeeze=False)\n", + " # Chaque configuration a maintenant 5 colonnes pour les 5 échantillons\n", + " fig, axes = plt.subplots(len(character_sets), len(unique_units) * 5, figsize=(len(unique_units) * 3 * 5, len(character_sets) * 3), squeeze=False)\n", "\n", - " # Initialiser tous les axes comme invisibles; ils seront activés lorsqu'utilisés\n", - " for ax_row in axes:\n", - " for ax in ax_row:\n", - " ax.set_visible(False)\n", + " for row_idx, characters in enumerate(character_sets):\n", + " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", "\n", - " for layer_sizes in hidden_layers_sizes:\n", - " print(f\"\\nTraining DBN with hidden layers: {layer_sizes}\")\n", - " dbn = DBN(n_visible=data.shape[1], hidden_layer_sizes=layer_sizes, random_state=42)\n", - " dbn.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=16, print_each=1000000)\n", + " for col_idx, num_units in enumerate(unique_units):\n", + " print(f\"\\nTraining RBM with {num_units} hidden units on characters {characters}\")\n", + " rbm = RBM(n_visible=data.shape[1], n_hidden=num_units, random_state=42)\n", + " rbm.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=15, print_each=5000)\n", "\n", - " # Génération et affichage d'une image\n", - " generated_image = dbn.generate_image(n_samples=1)\n", - " unit_idx = unique_units.index(layer_sizes[0])\n", - " layer_idx = len(layer_sizes) - 2 # Index 0 pour 2 couches, index 1 pour 3 couches, etc.\n", + " # Génération de 5 images\n", + " generated_images = rbm.generate_image(n_samples=5)\n", "\n", - " ax = axes[unit_idx][layer_idx]\n", - " ax.set_visible(True)\n", - " ax.imshow(generated_image[0].reshape(20, 16), cmap='plasma')\n", - " ax.set_title(f\"N_Layers: {len(layer_sizes)}, N_Units: {layer_sizes[0]}, N_Chars: {len(characters)}\")\n", - " ax.axis('off')\n", + " for sample_idx in range(5):\n", + " ax = axes[row_idx, col_idx * 5 + sample_idx]\n", + " ax.imshow(generated_images[sample_idx].reshape(20, 16), cmap='plasma')\n", + " ax.set_title(f\"N_char {len(characters)}, Generation 1: {sample_idx+1}\")\n", + " ax.axis('off')\n", "\n", - " # Enregistrement de l'image générée\n", - " directory = f\"../resultat/dbn/{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers\"\n", - " os.makedirs(directory, exist_ok=True)\n", - " np.save(f\"{directory}/Units_{layer_sizes[0]}_Chars_{''.join(characters)}.npy\", generated_image[0])\n", + " # Enregistrer chaque échantillon généré\n", + " save_path = f\"../resultat/rbm/{num_units}_Units_{len(characters)}_Chars_Sample_{sample_idx}.npy\"\n", + " os.makedirs(os.path.dirname(save_path), exist_ok=True)\n", + " np.save(save_path, generated_images[sample_idx])\n", "\n", - " #save figure\n", " plt.tight_layout()\n", - " directory_image = \"../resultat/images/dbn\"\n", + " directory_image = \"../resultat/images/rbm\"\n", " os.makedirs(directory_image, exist_ok=True)\n", - " plt.savefig(f\"{directory_image}/dbn_{len(characters)}_chars_{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers.png\")\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n" + " plt.savefig(f\"{directory_image}/rbm_samples.png\")\n", + " plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1161,17506 +279,8095 @@ "output_type": "stream", "text": [ "\n", - "Training DBN with hidden layers: [400, 400, 400, 400]\n" + "Training RBM with 200 hidden units on characters E\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 295.31it/s]\n" + "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 427.21it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.20307.\n" + "Reconstruction error: 0.20001.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 750.64it/s]\n", + "Epoch 2: 100%|██████████| 3/3 [00:00<00:00, 544.41it/s]\n", + "Epoch 3: 100%|██████████| 3/3 [00:00<00:00, 542.95it/s]\n", 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351.75it/s]\n", + "Epoch 496: 100%|██████████| 3/3 [00:00<00:00, 593.56it/s]\n", + "Epoch 497: 100%|██████████| 3/3 [00:00<00:00, 506.54it/s]\n", + "Epoch 498: 100%|██████████| 3/3 [00:00<00:00, 600.13it/s]\n", + "Epoch 499: 100%|██████████| 3/3 [00:00<00:00, 498.75it/s]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Training RBM with 200 hidden units on characters E\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 369.49it/s]\n", - "Epoch 2: 100%|██████████| 3/3 [00:00<00:00, 429.52it/s]\n", - "Epoch 3: 100%|██████████| 3/3 [00:00<00:00, 373.68it/s]\n", - "Epoch 4: 100%|██████████| 3/3 [00:00<00:00, 354.72it/s]\n", - "Epoch 5: 100%|██████████| 3/3 [00:00<00:00, 343.89it/s]\n", - "Epoch 6: 100%|██████████| 3/3 [00:00<00:00, 393.99it/s]\n", - "Epoch 7: 100%|██████████| 3/3 [00:00<00:00, 733.36it/s]\n", - "Epoch 8: 100%|██████████| 3/3 [00:00<00:00, 262.18it/s]\n", - "Epoch 9: 100%|██████████| 3/3 [00:00<00:00, 568.18it/s]\n", - "Epoch 10: 100%|██████████| 3/3 [00:00<00:00, 1949.03it/s]\n", - "Epoch 11: 100%|██████████| 3/3 [00:00<00:00, 1750.79it/s]\n", - "Epoch 12: 100%|██████████| 3/3 [00:00<00:00, 6442.86it/s]\n", - "Epoch 13: 100%|██████████| 3/3 [00:00<00:00, 499.56it/s]\n", - "Epoch 14: 100%|██████████| 3/3 [00:00<00:00, 403.87it/s]\n", - "Epoch 15: 100%|██████████| 3/3 [00:00<00:00, 363.43it/s]\n", - "Epoch 16: 100%|██████████| 3/3 [00:00<00:00, 363.46it/s]\n", - "Epoch 17: 100%|██████████| 3/3 [00:00<00:00, 334.16it/s]\n", - "Epoch 18: 100%|██████████| 3/3 [00:00<00:00, 391.78it/s]\n", - "Epoch 19: 100%|██████████| 3/3 [00:00<00:00, 1691.71it/s]\n", - "Epoch 20: 100%|██████████| 3/3 [00:00" + "
" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "# Exemple d'utilisation avec des configurations générées\n", - "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", - "run_experiment(configurations, n_epochs=1000, characters=['Y'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ + }, { "name": "stdout", "output_type": "stream", "text": [ "\n", - "Training DBN with hidden layers: [400, 400, 400, 400]\n" + "Training RBM with 200 hidden units on characters E\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 372.20it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.20001.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 5/5 [00:00<00:00, 395.25it/s]\n" 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" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "# Exemple d'utilisation avec des configurations générées\n", - "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", - "run_experiment(configurations, n_epochs=1000, characters=['Y', 'A'])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Training RBM with 200 hidden units on characters E\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 386.18it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.20001.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 498.87it/s]\n", + "Epoch 2: 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" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "# Exemple d'utilisation avec des configurations générées\n", - "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", - "run_experiment(configurations, n_epochs=1000, characters=['Y', 'U', 'Z'])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ + }, { "name": "stdout", "output_type": "stream", "text": [ "\n", - "Training DBN with hidden layers: [400, 400, 400, 400]\n" + "Training RBM with 200 hidden units on characters E\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 10/10 [00:00<00:00, 501.11it/s]\n" + "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 260.24it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.22848.\n" + "Reconstruction error: 0.20001.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - 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"Epoch 307: 100%|██████████| 10/10 [00:00<00:00, 2055.02it/s]\n", - "Epoch 308: 100%|██████████| 10/10 [00:00<00:00, 637.93it/s]\n", - "Epoch 309: 100%|██████████| 10/10 [00:00<00:00, 640.04it/s]\n", - "Epoch 310: 100%|██████████| 10/10 [00:00<00:00, 639.98it/s]\n", - "Epoch 311: 100%|██████████| 10/10 [00:00<00:00, 639.94it/s]\n", - "Epoch 312: 100%|██████████| 10/10 [00:00<00:00, 466.13it/s]\n", - "Epoch 313: 100%|██████████| 10/10 [00:00<00:00, 999.43it/s]\n", - "Epoch 314: 100%|██████████| 10/10 [00:00<00:00, 639.53it/s]\n", - "Epoch 315: 100%|██████████| 10/10 [00:00 3\u001b[0m \u001b[43mrun_experiment\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfigurations\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcharacters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mY\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mA\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mB\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m1\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[1;32mIn[74], line 21\u001b[0m, in \u001b[0;36mrun_experiment\u001b[1;34m(hidden_layers_sizes, n_epochs, characters)\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mTraining DBN with hidden layers: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlayer_sizes\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 20\u001b[0m dbn \u001b[38;5;241m=\u001b[39m DBN(n_visible\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m], hidden_layer_sizes\u001b[38;5;241m=\u001b[39mlayer_sizes, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m---> 21\u001b[0m \u001b[43mdbn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m16\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_each\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 23\u001b[0m \u001b[38;5;66;03m# Génération et affichage d'une image\u001b[39;00m\n\u001b[0;32m 24\u001b[0m generated_image \u001b[38;5;241m=\u001b[39m dbn\u001b[38;5;241m.\u001b[39mgenerate_image(n_samples\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", - "Cell \u001b[1;32mIn[18], line 70\u001b[0m, in \u001b[0;36mDBN.train\u001b[1;34m(self, data, learning_rate, n_epochs, batch_size, print_each)\u001b[0m\n\u001b[0;32m 68\u001b[0m input_data \u001b[38;5;241m=\u001b[39m data\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m rbm \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrbms:\n\u001b[1;32m---> 70\u001b[0m \u001b[43mrbm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 72\u001b[0m \u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlearning_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 73\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 74\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 75\u001b[0m \u001b[43m \u001b[49m\u001b[43mprint_each\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprint_each\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 76\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 77\u001b[0m \u001b[38;5;66;03m# Update input data for the next RBM\u001b[39;00m\n\u001b[0;32m 78\u001b[0m input_data \u001b[38;5;241m=\u001b[39m rbm\u001b[38;5;241m.\u001b[39minput_output(input_data)\n", - "Cell \u001b[1;32mIn[12], line 145\u001b[0m, in \u001b[0;36mRBM.train\u001b[1;34m(self, data, learning_rate, n_epochs, batch_size, print_each)\u001b[0m\n\u001b[0;32m 143\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m0\u001b[39m, n_samples, batch_size), desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEpoch \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepoch\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 144\u001b[0m batch \u001b[38;5;241m=\u001b[39m data[i:i\u001b[38;5;241m+\u001b[39mbatch_size]\n\u001b[1;32m--> 145\u001b[0m _, pos_v_probs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlearning_rate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[0;32m 150\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 152\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m epoch \u001b[38;5;241m%\u001b[39m print_each \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 153\u001b[0m tqdm\u001b[38;5;241m.\u001b[39mwrite(\n\u001b[0;32m 154\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReconstruction error: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reconstruction_error(batch,\u001b[38;5;250m \u001b[39mpos_v_probs)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "Cell \u001b[1;32mIn[12], line 113\u001b[0m, in \u001b[0;36mRBM.update\u001b[1;34m(self, batch, learning_rate, batch_size, return_output)\u001b[0m\n\u001b[0;32m 111\u001b[0m \u001b[38;5;66;03m# Update weights and biases\u001b[39;00m\n\u001b[0;32m 112\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mW \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m (batch\u001b[38;5;241m.\u001b[39mT \u001b[38;5;241m@\u001b[39m pos_h_probs \u001b[38;5;241m-\u001b[39m pos_v_probs\u001b[38;5;241m.\u001b[39mT \u001b[38;5;241m@\u001b[39m neg_h_probs) \u001b[38;5;241m/\u001b[39m batch_size\n\u001b[1;32m--> 113\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mb \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m \u001b[43m(\u001b[49m\u001b[43mpos_h_probs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mneg_h_probs\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 114\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m learning_rate \u001b[38;5;241m*\u001b[39m (batch \u001b[38;5;241m-\u001b[39m pos_v_probs)\u001b[38;5;241m.\u001b[39mmean(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 116\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_output:\n", - "File \u001b[1;32mc:\\Users\\choho\\Desktop\\Master DS\\Deep learning II\\github\\.venv\\lib\\site-packages\\numpy\\core\\_methods.py:101\u001b[0m, in \u001b[0;36m_mean\u001b[1;34m(a, axis, dtype, out, keepdims, where)\u001b[0m\n\u001b[0;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 99\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m um\u001b[38;5;241m.\u001b[39mclip(a, \u001b[38;5;28mmin\u001b[39m, \u001b[38;5;28mmax\u001b[39m, out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 101\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_mean\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m 102\u001b[0m arr \u001b[38;5;241m=\u001b[39m asanyarray(a)\n\u001b[0;32m 104\u001b[0m is_float16_result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error: 0.17934.\n" ] }, { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Exemple d'utilisation avec des configurations générées\n", - "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", - "run_experiment(configurations, n_epochs=1000, characters=['Y', 'A', 'B', '1'])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 543.21it/s]\n", + "Epoch 2: 100%|██████████| 3/3 [00:00<00:00, 398.67it/s]\n", + "Epoch 3: 100%|██████████| 3/3 [00:00<00:00, 331.78it/s]\n", + "Epoch 4: 100%|██████████| 3/3 [00:00<00:00, 355.55it/s]\n", + "Epoch 5: 100%|██████████| 3/3 [00:00<00:00, 284.19it/s]\n", + "Epoch 6: 100%|██████████| 3/3 [00:00<00:00, 663.06it/s]\n", + "Epoch 7: 100%|██████████| 3/3 [00:00<00:00, 397.78it/s]\n", + "Epoch 8: 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[00:00<00:00, 293.66it/s]\n", + "Epoch 499: 100%|██████████| 3/3 [00:00<00:00, 503.40it/s]\n" ] }, { "data": { - "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], + "source": [ + "# configuration = 2 layer with 200 units each\n", + "configurations_fixe = [200]\n", + "run_rbm_experiment(configurations_fixe, n_epochs=500, character_sets = ['E'])\n", + "run_rbm_experiment(configurations_fixe, n_epochs=500, character_sets = ['E', 'Y'])\n", + "run_rbm_experiment(configurations_fixe, n_epochs=500, character_sets = ['E', 'Y', '2'])\n", + "run_rbm_experiment(configurations_fixe, n_epochs=500, character_sets = ['E', 'Y', 'A', '2'])\n", + "run_rbm_experiment(configurations_fixe, n_epochs=500, character_sets=['E', 'Y', 'A', '2', '7'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exemple d'utilisation\n", + "hidden_units_sizes = [100, 200, 300, 400, 500, 600, 700]\n", + "run_rbm_experiment(hidden_units_sizes, n_epochs=2, character_sets=[['A', 'B'], ['A', 'B', 'C'], ['A', 'B', 'C', 'D']])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. DBM" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def run_dbm_experiment(hidden_layers_sizes, n_epochs=100, characters=['A', 'B', '1', '2']):\n", + " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", + "\n", + " # Déterminer le nombre maximum de couches et le nombre unique d'unités\n", + " max_layers = max(len(sizes) for sizes in hidden_layers_sizes)\n", + " unique_units = sorted({sizes[0] for sizes in hidden_layers_sizes})\n", + "\n", + " # Préparer une grille de subplots\n", + " fig, axes = plt.subplots(len(unique_units), max_layers, figsize=(max_layers * 3, len(unique_units) * 3), squeeze=False)\n", + "\n", + " # Initialiser tous les axes comme invisibles; ils seront activés lorsqu'utilisés\n", + " for ax_row in axes:\n", + " for ax in ax_row:\n", + " ax.set_visible(False)\n", + "\n", + " for layer_sizes in hidden_layers_sizes:\n", + " print(f\"\\nTraining DBN with hidden layers: {layer_sizes}\")\n", + " dbn = DBN(n_visible=data.shape[1], hidden_layer_sizes=layer_sizes, random_state=42)\n", + " dbn.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=16, print_each=1000000)\n", + "\n", + " # Génération et affichage d'une image\n", + " generated_image = dbn.generate_image(n_samples=5)\n", + " unit_idx = unique_units.index(layer_sizes[0])\n", + " layer_idx = len(layer_sizes) - 2 # Index 0 pour 2 couches, index 1 pour 3 couches, etc.\n", + "\n", + " ax = axes[unit_idx][layer_idx]\n", + " ax.set_visible(True)\n", + " ax.imshow(generated_image[0].reshape(20, 16), cmap='plasma')\n", + " ax.set_title(f\"N_Layers: {len(layer_sizes)}, N_Units: {layer_sizes[0]}, N_Chars: {len(characters)}\")\n", + " ax.axis('off')\n", + "\n", + " # Enregistrement de l'image générée\n", + " directory = f\"../resultat/dbn/{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers\"\n", + " os.makedirs(directory, exist_ok=True)\n", + " np.save(f\"{directory}/Units_{layer_sizes[0]}_Chars_{''.join(characters)}.npy\", generated_image[0])\n", + "\n", + " #save figure\n", + " plt.tight_layout()\n", + " directory_image = \"../resultat/images/dbn\"\n", + " os.makedirs(directory_image, exist_ok=True)\n", + " plt.savefig(f\"{directory_image}/dbn_{len(characters)}_chars_{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers.png\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exemple d'utilisation avec des configurations générées\n", + "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", + "run_dbm_experiment(configurations, n_epochs=2, characters=['Y'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exemple d'utilisation avec des configurations générées\n", + "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", + "run_dbm_experiment(configurations, n_epochs=1000, characters=['Y', 'A'])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exemple d'utilisation avec des configurations générées\n", + "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", + "run_dbm_experiment(configurations, n_epochs=1000, characters=['Y', 'U', 'Z'])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exemple d'utilisation avec des configurations générées\n", + "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", + "run_dbm_experiment(configurations, n_epochs=1000, characters=['Y', 'A', 'B', '1'])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Exemple d'utilisation avec des configurations générées\n", "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", - "run_experiment(configurations, n_epochs=1000, characters=['Y', 'A', 'B', '1', '2'])\n", + "run_dbm_experiment(configurations, n_epochs=1000, characters=['Y', 'A', 'B', '1', '2'])\n", "\n" ] }, diff --git a/notebook/gan and other.ipynb b/notebook/gan and other.ipynb deleted file mode 100644 index ee2b028..0000000 --- a/notebook/gan and other.ipynb +++ /dev/null @@ -1,15267 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Restricted Botlzman Machines (RBM)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "#FIXME: Review the generation process (theoretically) and fix the implementation " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from typing import List, Dict, Tuple, Literal, Optional, Union, Iterable\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import scipy.io\n", - "from tqdm import tqdm\n", - "from numpy._typing import ArrayLike\n", - "\n", - "ArrayLike = Union[List, Tuple, np.ndarray]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "DATA_FOLDER = \"../data/\"\n", - "ALPHA_DIGIT_PATH = os.path.join(DATA_FOLDER, \"binaryalphadigs.mat\")\n", - "MNIST_PATH = os.path.join(DATA_FOLDER, \"mnist_all.mat\")\n", - "\n", - "if not os.path.exists(ALPHA_DIGIT_PATH):\n", - " raise FileNotFoundError(f\"The file {ALPHA_DIGIT_PATH} does not exist.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.1 Implementing a RBM and testing on Binary AlphaDigits" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0039
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32W39
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" - ], - "text/plain": [ - " Class Labels Class Count\n", - "0 0 39\n", - "1 1 39\n", - "2 2 39\n", - "3 3 39\n", - "4 4 39\n", - "5 5 39\n", - "6 6 39\n", - "7 7 39\n", - "8 8 39\n", - "9 9 39\n", - "10 A 39\n", - "11 B 39\n", - "12 C 39\n", - "13 D 39\n", - "14 E 39\n", - "15 F 39\n", - "16 G 39\n", - "17 H 39\n", - "18 I 39\n", - "19 J 39\n", - "20 K 39\n", - "21 L 39\n", - "22 M 39\n", - "23 N 39\n", - "24 O 39\n", - "25 P 39\n", - "26 Q 39\n", - "27 R 39\n", - "28 S 39\n", - "29 T 39\n", - "30 U 39\n", - "31 V 39\n", - "32 W 39\n", - "33 X 39\n", - "34 Y 39\n", - "35 Z 39" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def _load_data(file_path: str) -> Dict[str, np.ndarray]:\n", - " \"\"\"\n", - " Load Binary AlphaDigits data from a .mat file.\n", - "\n", - " Parameters:\n", - " - file_path (str): Path to the .mat file containing the data.\n", - "\n", - " Returns:\n", - " - data (dict): Loaded data dictionary.\n", - " \"\"\"\n", - " if file_path is None:\n", - " raise ValueError(\"File path must be provided.\")\n", - "\n", - " return scipy.io.loadmat(file_path)\n", - "\n", - "\n", - "data = _load_data(ALPHA_DIGIT_PATH)\n", - "class_labels = data[\"classlabels\"].flatten() \n", - "class_count = data[\"classcounts\"].flatten()\n", - "df = pd.DataFrame(\n", - " {\n", - " \"Class Labels\": class_labels,\n", - " \"Class Count\": class_count\n", - " }\n", - ")\n", - "df[\"Class Labels\"] = df[\"Class Labels\"].apply(lambda x: x[0])\n", - "df[\"Class Count\"] = df[\"Class Count\"].apply(lambda x: x[0][0])\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(36, 39)\n", - "(20, 16)\n" - ] - } - ], - "source": [ - "def _load_data(file_path: str, which: Literal[\"alphadigit\", \"mnist\"]=\"alphadigit\") -> Dict[str, np.ndarray]:\n", - " \"\"\"\n", - " Load Binary AlphaDigits data from a .mat file.\n", - "\n", - " Parameters:\n", - " - file_path (str): Path to the .mat file containing the data.\n", - " - which (Literal[\"alphadigit\", \"mnist\"], optional): Specifies \n", - " which data to load. The default value is \"alphadigit\".\n", - "\n", - " Returns:\n", - " - data (dict): A dictionary containing the loaded data.\n", - "\n", - " Raises:\n", - " - ValueError: If the file_path parameter is None.\n", - " - ValueError: If the which parameter is not \"alphadigit\".\n", - "\n", - " Example Usage:\n", - " ```python\n", - " data = _load_data(\"data.mat\", \"alphadigit\")\n", - " ```\n", - " \"\"\"\n", - " if file_path is None:\n", - " raise ValueError(\"File path must be provided.\")\n", - " \n", - " if which == \"alphadigit\":\n", - " return scipy.io.loadmat(file_path)[\"dat\"]\n", - " \n", - " raise ValueError(\"MNIST NOT YET AVAILABLE.\")\n", - "\n", - "alphadigit_data = _load_data(ALPHA_DIGIT_PATH) \n", - "print(alphadigit_data.shape)\n", - "print(alphadigit_data[0][0].shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 > map to > [0]\n", - "10 > map to > [10]\n", - "A > map to > [10]\n", - "[1, 'C'] > map to > [[1], [12]]\n", - "36 > no mapping available, out of range\n" - ] - } - ], - "source": [ - "def _map_characters_to_indices(characters: Union[str, int, List[Union[str, int]]]) -> List[int]:\n", - " \"\"\"\n", - " Map alphanumeric character to its corresponding index.\n", - "\n", - " Parameters:\n", - " - character (str, int, list of str or int): Alphanumeric character or its index.\n", - "\n", - " Returns:\n", - " - char_index (int): Corresponding index for the character.\n", - " \"\"\"\n", - " if isinstance(characters, list):\n", - " return [_map_characters_to_indices(char) for char in characters]\n", - " if isinstance(characters, int) and 0 <= characters <= 35:\n", - " return [characters]\n", - " if (isinstance(characters, str) and characters.isdigit()\n", - " and 0 <= int(characters) <= 9):\n", - " return [int(characters)]\n", - " if (isinstance(characters, str) and characters.isalpha()\n", - " and 'A' <= characters.upper() <= 'Z'):\n", - " return [ord(characters.upper()) - ord('A') + 10]\n", - " \n", - " raise ValueError(\n", - " \"Invalid character input. It should be an alphanumeric\" \n", - " \"character '[0-9|A-Z]' or its index representing '[0-35]'.\"\n", - " )\n", - "\n", - "for char in [0, 10, \"A\", [1, \"C\"], 36]:\n", - " try:\n", - " map = _map_characters_to_indices(char)\n", - " print(f\"{char} > map to > {map}\")\n", - " except:\n", - " print(f\"{char} > no mapping available, out of range\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def read_alpha_digit(characters: Optional[Union[str, int, List[Union[str, int]]]] = None,\n", - " file_path: Optional[str] = ALPHA_DIGIT_PATH,\n", - " data: Optional[Dict[str, np.ndarray]] = None,\n", - " use_data: bool = False,\n", - " ) -> np.ndarray:\n", - " \"\"\"\n", - " Reads binary AlphaDigits data from a .mat file or uses already loaded data. \n", - " It extracts the data for a specified alphanumeric character or its index, and \n", - " flattens the images into one-dimensional vectors.\n", - "\n", - " Parameters:\n", - " - characters (Union[str, int, List[Union[str, int]]], optional): Alphanumeric character \n", - " or its index whose data needs to be extracted. It can be a single character or \n", - " a list of characters. Default is None.\n", - " - file_path (str, optional): Path to the .mat file containing the data. \n", - " Default is None.\n", - " - data (dict, optional): Already loaded data dictionary. \n", - " Default is None.\n", - " - use_data (bool): Flag to indicate whether to use already loaded data.\n", - " Default is False.\n", - "\n", - " Returns:\n", - " - flattened_images (numpy.ndarray): Flattened images for the specified character(s).\n", - " \"\"\"\n", - " if not use_data:\n", - " data = _load_data(file_path, which=\"alphadigit\")\n", - "\n", - " char_indices = _map_characters_to_indices(characters)\n", - "\n", - " # Select the rows corresponding to the characters indices.\n", - " char_data: np.ndarray = data[char_indices]\n", - " \n", - " # Flatten each image into a one-dimensional vector.\n", - " flattened_images = np.array([image.flatten() for image in char_data.flatten()])\n", - " return flattened_images\n", - "\n", - "def plot_characters(chars, data):\n", - " num_chars = len(chars)\n", - " num_images_per_char = data.shape[0] // num_chars\n", - " fig, ax = plt.subplots(1, num_chars, figsize=(num_chars * 2, 2))\n", - "\n", - " for i, char in enumerate(chars):\n", - " # Find the index of the first image corresponding to the current char\n", - " start_index = i * num_images_per_char\n", - " image = data[start_index].reshape(20, 16)\n", - " ax[i].imshow(image, cmap='gray')\n", - " ax[i].set_title(f'Char: {char}')\n", - " ax[i].axis('off')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "# Example\n", - "chars = [0, \"K\", 7, \"Z\"]\n", - "data = read_alpha_digit(chars, data=alphadigit_data, use_data=True)\n", - "plot_characters(chars, data)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "data shape: (156, 320)\n" - ] - } - ], - "source": [ - "print(\"data shape:\", data.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "class RBM:\n", - " def __init__(self, n_visible: int, n_hidden: int=100, random_state=None) -> None:\n", - " \"\"\"\n", - " Initialize the Restricted Boltzmann Machine.\n", - "\n", - " Parameters:\n", - " - n_visible (int): Number of visible units.\n", - " - n_hidden (int): Number of hidden units. Default 100.\n", - " - random_state: Random seed for reproducibility.\n", - " \"\"\"\n", - " self.n_visible = n_visible\n", - " self.n_hidden = n_hidden\n", - " \n", - " self.a = np.zeros((1, n_visible)) # visible_bias\n", - " self.b = np.zeros((1, n_hidden)) # hidden_bias\n", - " self.rng = np.random.default_rng(random_state)\n", - " self.W = 1e-4 * self.rng.standard_normal(size=(n_visible, n_hidden)) # weights\n", - "\n", - " def __repr__(self) -> str:\n", - " return f\"RBM(n_visible={self.n_visible}, n_hidden={self.n_hidden})\"\n", - "\n", - " def _sigmoid(self, x: np.ndarray) -> np.ndarray:\n", - " \"\"\"\n", - " Sigmoid activation function.\n", - "\n", - " Parameters:\n", - " - x (numpy.ndarray): Input array.\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Result of applying the sigmoid function to the input.\n", - " \"\"\"\n", - " return 1 / (1 + np.exp(-x))\n", - " \n", - " def _reconstruction_error(self, input: np.ndarray, image: np.ndarray) -> float:\n", - " \"\"\"\n", - " Compute reconstruction error.\n", - "\n", - " Parameters:\n", - " - input (numpy.ndarray): Original input data.\n", - " - image (numpy.ndarray): Reconstructed image.\n", - "\n", - " Returns:\n", - " - float: Reconstruction error.\n", - " \"\"\"\n", - " return np.round(np.power(image - input, 2).mean(), 5)\n", - "\n", - " def input_output(self, data: np.ndarray) -> np.ndarray:\n", - " \"\"\"\n", - " Compute hidden units given visible units.\n", - "\n", - " Parameters:\n", - " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Hidden unit activations, shape (n_samples, n_hidden).\n", - " \"\"\"\n", - " return self._sigmoid(data @ self.W + self.b)\n", - "\n", - " def output_input(self, data_h: np.ndarray) -> np.ndarray:\n", - " \"\"\"\n", - " Compute visible units given hidden units.\n", - "\n", - " Parameters:\n", - " - data_h (numpy.ndarray): Hidden unit activations, shape (n_samples, n_hidden).\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Reconstructed visible units, shape (n_samples, n_visible).\n", - " \"\"\"\n", - " return self._sigmoid(data_h @ self.W.T + self.a)\n", - " \n", - " def calcul_softmax(self, data: np.ndarray) -> np.ndarray:\n", - " \"\"\"\n", - " Calculate softmax probabilities for the output units.\n", - "\n", - " Parameters:\n", - " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Softmax probabilities, shape (n_samples, n_hidden).\n", - " \"\"\"\n", - " # Compute activations for the hidden layer\n", - " hidden_activations = self.input_output(data)\n", - " \n", - " # Compute softmax probabilities for the output layer\n", - " exp_hidden_activations = np.exp(hidden_activations)\n", - " softmax_probs = exp_hidden_activations / np.sum(exp_hidden_activations, axis=1, keepdims=True)\n", - " \n", - " return softmax_probs\n", - "\n", - " def update(\n", - " self, \n", - " batch: np.ndarray,\n", - " learning_rate: float=0.1,\n", - " batch_size: Optional[int]=None,\n", - " return_output: bool=False\n", - " ):\n", - " \"\"\"_summary_\n", - "\n", - " Args:\n", - " batch (np.ndarray): _description_\n", - " learning_rate (float, optional): _description_. Defaults to 0.1.\n", - " batch_size (Optional[int], optional): _description_. Defaults to None.\n", - " return_output (bool, optional): _description_. Defaults to False.\n", - " \"\"\"\n", - " if not batch_size:\n", - " batch_size = batch.shape[0]\n", - " pos_h_probs = self.input_output(batch)\n", - " pos_v_probs = self.output_input(pos_h_probs)\n", - " neg_h_probs = self.input_output(pos_v_probs)\n", - " \n", - " # Update weights and biases\n", - " self.W += learning_rate * (batch.T @ pos_h_probs - pos_v_probs.T @ neg_h_probs) / batch_size\n", - " self.b += learning_rate * (pos_h_probs - neg_h_probs).mean(axis=0)\n", - " self.a += learning_rate * (batch - pos_v_probs).mean(axis=0)\n", - "\n", - " if return_output:\n", - " return self, pos_v_probs\n", - " \n", - " return self \n", - "\n", - " def train(self, \n", - " data: np.ndarray,\n", - " learning_rate: float=0.1,\n", - " n_epochs: int=10,\n", - " batch_size: int=10,\n", - " print_each=10\n", - " ) -> 'RBM':\n", - " \"\"\"\n", - " Train the RBM using Contrastive Divergence.\n", - "\n", - " Parameters:\n", - " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", - " - n_epochs (int): Number of training epochs. Default is 10.\n", - " - batch_size (int): Size of mini-batches. Default is 10.\n", - "\n", - " Returns:\n", - " - RBM: Trained RBM instance.\n", - " \"\"\"\n", - " n_samples = data.shape[0]\n", - " for epoch in range(n_epochs):\n", - " self.rng.shuffle(data)\n", - " for i in tqdm(range(0, n_samples, batch_size), desc=f\"Epoch {epoch}\"):\n", - " batch = data[i:i+batch_size]\n", - " _, pos_v_probs = self.update(\n", - " batch=batch,\n", - " learning_rate=learning_rate,\n", - " batch_size=batch_size,\n", - " return_output=True\n", - " )\n", - " \n", - " if epoch % print_each == 0:\n", - " tqdm.write(\n", - " f\"Reconstruction error: {self._reconstruction_error(batch, pos_v_probs)}.\")\n", - "\n", - " return self\n", - "\n", - " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", - " \"\"\"\n", - " Generate samples from the RBM using Gibbs sampling.\n", - "\n", - " Parameters:\n", - " - n_samples (int): Number of samples to generate. Default is 10.\n", - " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 1.\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", - " \"\"\"\n", - " samples = np.zeros((n_samples, self.n_visible))\n", - " \n", - " # Matrix of initlization value of Gibbs samples for each sample. \n", - " V = self.rng.binomial(1, self.rng.random(), size=n_samples*self.n_visible).reshape((n_samples, self.n_visible))\n", - " for i in range(n_samples):\n", - " for _ in range(n_gibbs_steps):\n", - " h_probs = self._sigmoid(V[i] @ self.W + self.b) # vector\n", - " h = self.rng.binomial(1, h_probs)\n", - " v_probs = self._sigmoid(h @ self.W.T + self.a)\n", - " v = self.rng.binomial(1, v_probs)\n", - " samples[i] = v\n", - " return samples" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the alpha_digit data\n", - "data = read_alpha_digit(file_path=ALPHA_DIGIT_PATH, characters=['Z'])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RBM(n_visible=320, n_hidden=200)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 571.10it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.16569.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 504.18it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 531.21it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 481.33it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 589.29it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 472.40it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 416.17it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 480.92it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 593.11it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 438.05it/s]\n", - "Epoch 10: 100%|██████████| 4/4 [00:00<00:00, 561.28it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.13308.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 11: 100%|██████████| 4/4 [00:00<00:00, 558.12it/s]\n", - "Epoch 12: 100%|██████████| 4/4 [00:00<00:00, 400.77it/s]\n", - "Epoch 13: 100%|██████████| 4/4 [00:00<00:00, 501.37it/s]\n", - "Epoch 14: 100%|██████████| 4/4 [00:00<00:00, 528.42it/s]\n", - "Epoch 15: 100%|██████████| 4/4 [00:00<00:00, 429.48it/s]\n", - "Epoch 16: 100%|██████████| 4/4 [00:00<00:00, 603.24it/s]\n", - "Epoch 17: 100%|██████████| 4/4 [00:00<00:00, 562.71it/s]\n", - "Epoch 18: 100%|██████████| 4/4 [00:00<00:00, 466.09it/s]\n", - "Epoch 19: 100%|██████████| 4/4 [00:00<00:00, 568.97it/s]\n", - "Epoch 20: 0%| | 0/4 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Generate samples\n", - "generated_samples = rbm.generate_image(n_samples=10, n_gibbs_steps=1)\n", - "\n", - "# Plot original and generated samples\n", - "plt.figure(figsize=(12, 6))\n", - "for i in range(10):\n", - " plt.subplot(2, 10, i + 1)\n", - " plt.imshow(data[i].reshape(20, 16), cmap='gray')\n", - " plt.title('Original')\n", - " plt.axis('off')\n", - " \n", - " plt.subplot(2, 10, i + 11)\n", - " plt.imshow(generated_samples[i].reshape(20, 16), cmap='gray')\n", - " plt.title('Generated')\n", - " plt.axis('off')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RBM(n_visible=320, n_hidden=200)\n" - ] - } - ], - "source": [ - "print(rbm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2 Implementing a Deep Belief Network (DBN) and test on Binary AlphaDigits" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "class DBN:\n", - " def __init__(self, n_visible: int, hidden_layer_sizes: list[int], random_state=None):\n", - " \"\"\"\n", - " Initialize the Deep Belief Network.\n", - "\n", - " Parameters:\n", - " - n_visible (int): Number of visible units.\n", - " - hidden_layer_sizes (list[int]): List of sizes for each hidden layer.\n", - " - random_state: Random seed for reproducibility.\n", - " \"\"\"\n", - " self.n_visible = n_visible\n", - " self.hidden_layer_sizes = hidden_layer_sizes\n", - " self.rbms: List[RBM] = []\n", - " self.rng = np.random.default_rng(random_state)\n", - "\n", - " # Initialize the first RBM\n", - " first_rbm = RBM(\n", - " n_visible=n_visible,\n", - " n_hidden=hidden_layer_sizes[0],\n", - " random_state=random_state,\n", - " )\n", - " self.rbms.append(first_rbm)\n", - "\n", - " # Initialize RBMs for subsequent hidden layers\n", - " for i, size in enumerate(hidden_layer_sizes[1:], start=1):\n", - " rbm = RBM(\n", - " n_visible=hidden_layer_sizes[i - 1],\n", - " n_hidden=size,\n", - " random_state=random_state,\n", - " )\n", - " self.rbms.append(rbm)\n", - "\n", - "\n", - " def __getitem__(self, key):\n", - " return self.rbms[key]\n", - " \n", - "\n", - " def __repr__(self):\n", - " \"\"\"\n", - " Return a string representation of the DBN object.\n", - " \"\"\"\n", - " rbm_reprs = [f\"{'':4}{repr(rbm)}\" for rbm in self.rbms]\n", - " join_rbm_reprs = ',\\n'.join(rbm_reprs)\n", - " return f\"DBN([\\n{join_rbm_reprs}\\n])\"\n", - "\n", - "\n", - " def train(self,\n", - " data: np.ndarray,\n", - " learning_rate: float=0.1,\n", - " n_epochs: int=10,\n", - " batch_size: int=10,\n", - " print_each: int=10,\n", - " ) -> \"DBN\":\n", - " \"\"\"\n", - " Train the DBN using Greedy layer-wise procedure.\n", - "\n", - " Parameters:\n", - " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - " - learning_rate (float): Learning rate for gradient descent. Default is 0.1.\n", - " - n_epochs (int): Number of training epochs. Default is 10.\n", - " - batch_size (int): Size of mini-batches. Default is 10.\n", - " - print_each: Print reconstruction error each `print_each` epochs.\n", - " - verbose\n", - "\n", - " Returns:\n", - " - DBN: Trained DBN instance.\n", - " \"\"\"\n", - " input_data = data\n", - " for rbm in self.rbms:\n", - " rbm.train(\n", - " input_data,\n", - " learning_rate=learning_rate,\n", - " n_epochs=n_epochs,\n", - " batch_size=batch_size,\n", - " print_each=print_each,\n", - " )\n", - " # Update input data for the next RBM\n", - " input_data = rbm.input_output(input_data)\n", - "\n", - " return self\n", - "\n", - " def generate_image(self, n_samples: int=1, n_gibbs_steps: int=1) -> np.ndarray:\n", - " \"\"\"\n", - " Generate samples from the DBN using Gibbs sampling.\n", - "\n", - " Parameters:\n", - " - n_samples (int): Number of samples to generate. Default is 1.\n", - " - n_gibbs_steps (int): Number of Gibbs sampling steps. Default is 100.\n", - "\n", - " Returns:\n", - " - numpy.ndarray: Generated samples, shape (n_samples, n_visible).\n", - " \"\"\"\n", - " # samples = np.zeros((n_samples, self.n_visible))\n", - "\n", - " # Generate samples using the first RBM in the DBN\n", - " samples = self.rbms[-1].generate_image(n_samples, n_gibbs_steps)\n", - " for rbm in reversed(self.rbms[:-1]):\n", - " # Sample from the conditional probability of layer l-1 given layer l: p(h_{s-1}|h_{s}).\n", - " h_probs = rbm.output_input(samples)\n", - " h = self.rng.binomial(1, p=h_probs) \n", - " samples = h\n", - " return samples" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# from principal_dbn_alpha import DBN" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 1330.26it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.15919.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 1757.70it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 981.93it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 1333.43it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 1231.90it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 2267.80it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 1332.48it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00<00:00, 958.04it/s]\n", - "Epoch 8: 100%|██████████| 4/4 [00:00<00:00, 1333.64it/s]\n", - "Epoch 9: 100%|██████████| 4/4 [00:00<00:00, 1331.42it/s]\n", - "Epoch 0: 100%|██████████| 4/4 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# # Generate images\n", - "generated_images = dbn.generate_image(n_samples=5, n_gibbs_steps=1)\n", - "\n", - "# Display generated images\n", - "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(12, 4))\n", - "for i in range(5):\n", - " axes[i].imshow(generated_images[i].reshape(20, 16), cmap='gray')\n", - " axes[i].set_title(f\"Image {i+1}\")\n", - " axes[i].axis('off')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "class DNN(DBN):\n", - " def __init__(\n", - " self,\n", - " input_dim: int,\n", - " output_dim: int,\n", - " hidden_layer_sizes: List[int],\n", - " random_state=None\n", - " ):\n", - " \"\"\"\n", - " Initialize the Deep Neural Network (DNN).\n", - "\n", - " Parameters:\n", - " - input_dim (int): Dimension of the input.\n", - " - output_dim (int): Dimension of the output.\n", - " - hidden_layer_sizes (List[int]): List of sizes for each hidden layer.\n", - " - random_state: Random seed for reproducibility.\n", - " \"\"\"\n", - " super().__init__(\n", - " n_visible=input_dim,\n", - " hidden_layer_sizes=hidden_layer_sizes,\n", - " random_state=random_state\n", - " )\n", - " #--> self.rbms contains only the pre-trainable RBMs \n", - " self.clf = RBM(self.rbms[-1].n_hidden, output_dim)\n", - " self.network = self.rbms + [self.clf] # DNN = [DBN + Classifier] ~ [RBM_0,...,RBM_N, RBM_Clf]\n", - "\n", - " def __getitem__(self, key):\n", - " return self.network[key]\n", - " \n", - " def __repr__(self):\n", - " join_repr = \"\\n\".join([f\"{'':4}{repr(rbm)},\" for rbm in self.network])\n", - " return f\"DNN([\\n{join_repr} \\n])\"\n", - " \n", - " \n", - " def pretrain(self, n_epochs: int, learning_rate: float, batch_size: int, data: np.ndarray) -> \"DNN\":\n", - " \"\"\"\n", - " Pretrain the hidden layers of the DNN using the DBN training method.\n", - "\n", - " Parameters:\n", - " - n_epochs (int): Number of training epochs.\n", - " - learning_rate (float): Learning rate for gradient descent.\n", - " - batch_size (int): Size of mini-batches.\n", - " - data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - "\n", - " Returns:\n", - " - DNN: Pretrained DNN instance.\n", - " \"\"\"\n", - " # NOTE: Use the inherited `train` method to perform pre-training since `self.rbms`\n", - " # only contains the pre-trainable RBMs.\n", - " return self.train(data, n_epochs=n_epochs, learning_rate=learning_rate, batch_size=batch_size)\n", - " \n", - " def input_output(self, input_data: np.ndarray) -> Tuple[List[np.ndarray], np.ndarray]:\n", - " \"\"\"\n", - " Get the outputs on each layer of the DNN and the softmax probabilities on the output layer.\n", - "\n", - " Parameters:\n", - " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - "\n", - " Returns:\n", - " - Tuple[List[np.ndarray], np.ndarray]: Outputs on each layer & softmax probabilities.\n", - " \"\"\"\n", - " layer_outputs = []\n", - " \n", - " # Input layer output\n", - " layer_outputs.append(input_data)\n", - " \n", - " # Hidden layers output\n", - " for rbm in self.rbms:\n", - " layer_outputs.append(rbm.input_output(layer_outputs[-1]))\n", - " \n", - " # Softmax probabilities on the output layer\n", - " output_probs = self.network[-1].calcul_softmax(layer_outputs[-1])\n", - " \n", - " return layer_outputs, output_probs\n", - " \n", - "\n", - " def _cross_entropy(batch_labels: np.ndarray, output_probs: np.ndarray, eps: float = 1e-15) -> float:\n", - " \"\"\"\n", - " Calculate the cross entropy between the batch labels and output probabilities.\n", - "\n", - " Parameters:\n", - " - batch_labels (numpy.ndarray): True labels for the batch, shape (batch_size, n_classes).\n", - " - output_probs (numpy.ndarray): Predicted probabilities for the batch, shape (batch_size, n_classes).\n", - " - eps (float): Small value to avoid numerical instability in logarithm calculation. Default is 1e-15.\n", - "\n", - " Returns:\n", - " - float: Cross entropy value.\n", - " \"\"\"\n", - " return -np.mean(np.sum(batch_labels * np.log(output_probs + eps), axis=1))\n", - "\n", - "\n", - " def backpropagation(\n", - " self,\n", - " input_data: np.ndarray,\n", - " labels: np.ndarray,\n", - " n_epochs: int,\n", - " learning_rate: float,\n", - " batch_size: int,\n", - " eps: float = 1e-15\n", - " ) -> \"DNN\":\n", - " \"\"\"\n", - " Estimate the weights/biases of the network using backpropagation algorithm.\n", - "\n", - " Parameters:\n", - " - input_data (numpy.ndarray): Input data, shape (n_samples, n_visible).\n", - " - labels (numpy.ndarray): Labels for the input data, shape (n_samples, n_classes).\n", - " - n_epochs (int): Number of training epochs.\n", - " - learning_rate (float): Learning rate for gradient descent.\n", - " - batch_size (int): Size of mini-batches.\n", - " - eps (float): Small value to avoid numerical instability in logarithm calculation. Default is 1e-15.\n", - "\n", - " Returns:\n", - " - DNN: Updated DNN instance.\n", - " \"\"\"\n", - " n_samples = input_data.shape[0]\n", - " \n", - " for epoch in tqdm(range(n_epochs), desc=\"Training\", unit=\"epoch\"):\n", - " for batch_start in range(0, n_samples, batch_size):\n", - " batch_end = min(batch_start + batch_size, n_samples)\n", - " batch_input = input_data[batch_start:batch_end]\n", - " batch_labels = labels[batch_start:batch_end]\n", - "\n", - " # Forward pass\n", - " layer_outputs, output_probs = self.input_output(batch_input)\n", - "\n", - " # Backward pass (update weights and biases)\n", - " self.network[-1].update(batch_labels, layer_outputs[-1], learning_rate)\n", - " for i in range(len(self.network) - 2, -1, -1):\n", - " self.network[i].update(layer_outputs[i], layer_outputs[i + 1], self.network[i + 1].weights, learning_rate)\n", - "\n", - " # Calculate cross entropy after each epoch\n", - " loss = self._cross_entropy(batch_labels, output_probs, eps)\n", - " tqdm.write(f\"Epoch {epoch + 1}/{n_epochs}, Cross Entropy: {loss}\")\n", - "\n", - " return self\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 0: 100%|██████████| 4/4 [00:00<00:00, 446.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.18317.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 4/4 [00:00<00:00, 3997.43it/s]\n", - "Epoch 2: 100%|██████████| 4/4 [00:00<00:00, 2144.88it/s]\n", - "Epoch 3: 100%|██████████| 4/4 [00:00<00:00, 2020.62it/s]\n", - "Epoch 4: 100%|██████████| 4/4 [00:00<00:00, 2675.79it/s]\n", - "Epoch 5: 100%|██████████| 4/4 [00:00<00:00, 2564.15it/s]\n", - "Epoch 6: 100%|██████████| 4/4 [00:00<00:00, 5282.50it/s]\n", - "Epoch 7: 100%|██████████| 4/4 [00:00=4.8.0 (from torch)\n", - " Using cached 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"source": [ - "#!pip install torch torchvision\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "import numpy as np\n", - "from torch.autograd import Variable\n", - "import torchvision.transforms as transforms\n", - "import torchvision.datasets as datasets\n", - "from torchvision.utils import save_image\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the Generator network\n", - "class Generator(nn.Module):\n", - " def __init__(self, z_dim, img_shape):\n", - " super(Generator, self).__init__()\n", - " self.img_shape = img_shape\n", - " self.model = nn.Sequential(\n", - " nn.Linear(z_dim, 256),\n", - " nn.LeakyReLU(0.2),\n", - " nn.Linear(256, 512),\n", - " nn.LeakyReLU(0.2),\n", - " nn.Linear(512, int(np.prod(img_shape))),\n", - " nn.Tanh()\n", - " )\n", - "\n", - " def forward(self, z):\n", - " img = self.model(z)\n", - " img = img.view(img.size(0), *self.img_shape)\n", - " return img\n", - "\n", - "# Define the Discriminator network\n", - "class Discriminator(nn.Module):\n", - " def __init__(self, img_shape):\n", - " super(Discriminator, self).__init__()\n", - " self.model = nn.Sequential(\n", - " nn.Linear(int(np.prod(img_shape)), 512),\n", - " nn.LeakyReLU(0.2),\n", - " nn.Linear(512, 256),\n", - " nn.LeakyReLU(0.2),\n", - " nn.Linear(256, 1),\n", - " nn.Sigmoid()\n", - " )\n", - "\n", - " def forward(self, img):\n", - " flattened = img.view(img.size(0), -1)\n", - " validity = self.model(flattened)\n", - " return validity\n", - "\n", - "class GAN():\n", - " def __init__(self, dataset_name='mnist'):\n", - " # Load data\n", - " self.img_shape = (1, 64, 64) # for MNIST\n", - " self.z_dim = 100\n", - " self.dataset_name = dataset_name\n", - " self.model_file = f'models/{self.dataset_name}_gan_model.pickle'\n", - "\n", - " # Define networks\n", - " self.generator = Generator(self.z_dim, self.img_shape)\n", - " self.discriminator = Discriminator(self.img_shape)\n", - "\n", - " # Optimizers\n", - " self.optimizer_G = optim.Adam(self.generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n", - " self.optimizer_D = optim.Adam(self.discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n", - "\n", - " # Loss function\n", - " self.adversarial_loss = nn.BCELoss()\n", - "\n", - " def load_gan_data(self):\n", - " # MNIST Dataset\n", - " transform = transforms.Compose([\n", - " transforms.ToTensor(),\n", - " transforms.Normalize([0.5], [0.5])\n", - " ])\n", - "\n", - " dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)\n", - " dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True)\n", - " return dataloader\n", - "\n", - " def train(self, epochs, train_loader, sample_interval=1000):\n", - " for epoch in range(epochs):\n", - " for i, (imgs, _) in enumerate(train_loader):\n", - " # Adversarial ground truths\n", - " valid = Variable(torch.FloatTensor(imgs.size(0), 1).fill_(1.0), requires_grad=False)\n", - " fake = Variable(torch.FloatTensor(imgs.size(0), 1).fill_(0.0), requires_grad=False)\n", - "\n", - " # Configure input\n", - " real_imgs = Variable(imgs.type(torch.FloatTensor))\n", - "\n", - " # -----------------\n", - " # Train Generator\n", - " # -----------------\n", - " self.optimizer_G.zero_grad()\n", - "\n", - " # Sample noise as generator input\n", - " z = Variable(torch.FloatTensor(np.random.normal(0, 1, (imgs.size(0), self.z_dim))))\n", - "\n", - " # Generate a batch of images\n", - " gen_imgs = self.generator(z)\n", - "\n", - " # Loss measures generator's ability to fool the discriminator\n", - " g_loss = self.adversarial_loss(self.discriminator(gen_imgs), valid)\n", - "\n", - " g_loss.backward()\n", - " self.optimizer_G.step()\n", - "\n", - " # ---------------------\n", - " # Train Discriminator\n", - " # ---------------------\n", - " self.optimizer_D.zero_grad()\n", - "\n", - " # Measure discriminator's ability to classify real from generated samples\n", - " real_loss = self.adversarial_loss(self.discriminator(real_imgs), valid)\n", - " fake_loss = self.adversarial_loss(self.discriminator(gen_imgs.detach()), fake)\n", - " d_loss = (real_loss + fake_loss) / 2\n", - "\n", - " d_loss.backward()\n", - " self.optimizer_D.step()\n", - "\n", - " print(f\"[Epoch {epoch}/{epochs}] [Batch {i}/{len(train_loader)}] [D loss: {d_loss.item()}] [G loss: {g_loss.item()}]\")\n", - "\n", - " # If at save interval => save generated image samples and model checkpoints\n", - " if i % sample_interval == 0:\n", - " # Save image samples\n", - " # Save model checkpoints \n", - " self.save_sample_images(epoch, i)\n", - " torch.save(self.generator.state_dict(), f'results/generator_epoch{epoch}_batch{i}.pth')\n", - " torch.save(self.discriminator.state_dict(), f'results/discriminator_epoch{epoch}_batch{i}.pth') \n", - " \n", - " def save_sample_images(self, epoch, batch):\n", - " # Generate noise\n", - " z = Variable(torch.FloatTensor(np.random.normal(0, 1, (25, self.z_dim))))\n", - "\n", - " # Generate images from noise\n", - " gen_imgs = self.generator(z).detach()\n", - "\n", - " # Rescale images from [-1, 1] to [0, 1] range\n", - " gen_imgs = (gen_imgs + 1) / 2\n", - "\n", - " # Save image grid\n", - " save_image(gen_imgs.data, f'results/epoch{epoch}_batch{batch}.png', nrow=5, normalize=True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def prepare_set_data(set_set):\n", - " \"\"\"\n", - " Prepare the image tensors and labels from a given dataset.\n", - "\n", - " Parameters:\n", - " set_set (list): The dataset containing images and labels.\n", - "\n", - " Returns:\n", - " set_img_tensors (torch.Tensor): The image tensors of the dataset.\n", - " set_labels (torch.Tensor): The labels of the dataset.\n", - " \"\"\"\n", - " set_images = []\n", - " set_labels = []\n", - "\n", - " # Separate the labels and images from the dataset\n", - " for i in range(len(set_set)):\n", - " set_images.append(set_set[i][0])\n", - " set_labels.append(int(set_set[i][1][-2:]) - 1) # Subtract 1 here (so 0 corresponds to 01, 1 to 02, etc.) because the loss function expects 0-starting labels\n", - "\n", - " set_img_tensors = torch.tensor(set_images, dtype=torch.float32).permute(0, 3, 1, 2)\n", - " set_labels = torch.tensor(set_labels, dtype=torch.long)\n", - "\n", - " return set_img_tensors, set_labels\n", - "\n", - "\n", - "# Put into dataloader because we will use mini-batch gradient descent\n", - "\n", - "# TRAINING SET\n", - "train_dataset = TensorDataset(train_img_tensors, train_labels)\n", - "train_loader = DataLoader(dataset=train_dataset, batch_size=100, shuffle=True)\n", - "\n", - "# TEST SET\n", - "test_dataset = TensorDataset(test_img_tensors, test_labels)\n", - "test_loader = DataLoader(dataset=test_dataset, batch_size=100, shuffle=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Epoch 0/1] [Batch 0/12000] [D loss: 1.0838191509246826] [G loss: 0.6863428950309753]\n", - "[Epoch 0/1] [Batch 1/12000] [D loss: 0.5735496878623962] [G loss: 0.7067567706108093]\n", - "[Epoch 0/1] [Batch 2/12000] [D loss: 0.43297380208969116] [G loss: 0.7209208607673645]\n", - "[Epoch 0/1] [Batch 3/12000] [D loss: 0.4233285188674927] [G loss: 0.7485953569412231]\n", - "[Epoch 0/1] [Batch 4/12000] [D loss: 0.39475032687187195] [G loss: 0.7606310248374939]\n", - "[Epoch 0/1] [Batch 5/12000] [D loss: 0.3162686824798584] [G loss: 0.7600753903388977]\n", - "[Epoch 0/1] [Batch 6/12000] [D loss: 0.30194148421287537] [G loss: 0.791181743144989]\n", - "[Epoch 0/1] [Batch 7/12000] [D loss: 0.3068079948425293] [G loss: 0.7795546650886536]\n", - "[Epoch 0/1] [Batch 8/12000] [D loss: 0.31288668513298035] [G loss: 0.7715029716491699]\n", - "[Epoch 0/1] [Batch 9/12000] [D loss: 0.30776533484458923] [G loss: 0.7781574726104736]\n", - "[Epoch 0/1] [Batch 10/12000] [D loss: 0.312305212020874] [G loss: 0.7667292952537537]\n", - "[Epoch 0/1] [Batch 11/12000] [D loss: 0.3086841106414795] [G loss: 0.7751477956771851]\n", - "[Epoch 0/1] [Batch 12/12000] [D loss: 0.3112143576145172] [G loss: 0.7731603384017944]\n", - "[Epoch 0/1] [Batch 13/12000] [D loss: 0.318729430437088] [G loss: 0.7565591335296631]\n", - "[Epoch 0/1] [Batch 14/12000] [D loss: 0.3198605179786682] [G loss: 0.749590277671814]\n", - "[Epoch 0/1] [Batch 15/12000] [D loss: 0.337536096572876] [G loss: 0.7115561366081238]\n", - "[Epoch 0/1] [Batch 16/12000] [D loss: 0.33885207772254944] [G loss: 0.7088324427604675]\n", - "[Epoch 0/1] [Batch 17/12000] [D loss: 0.35716360807418823] [G loss: 0.6866923570632935]\n", - "[Epoch 0/1] [Batch 18/12000] [D loss: 0.35246148705482483] [G loss: 0.6815169453620911]\n", - "[Epoch 0/1] [Batch 19/12000] [D 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[Batch 1901/12000] [D loss: 0.2411520630121231] [G loss: 0.9632852673530579]\n", - "[Epoch 0/1] [Batch 1902/12000] [D loss: 0.36478137969970703] [G loss: 0.6580111980438232]\n", - "[Epoch 0/1] [Batch 1903/12000] [D loss: 0.25228458642959595] [G loss: 0.925796627998352]\n", - "[Epoch 0/1] [Batch 1904/12000] [D loss: 0.3293400704860687] [G loss: 0.7288447618484497]\n", - "[Epoch 0/1] [Batch 1905/12000] [D loss: 0.47527867555618286] [G loss: 0.4886167049407959]\n", - "[Epoch 0/1] [Batch 1906/12000] [D loss: 0.4056028127670288] [G loss: 0.5875663757324219]\n", - "[Epoch 0/1] [Batch 1907/12000] [D loss: 0.3219776749610901] [G loss: 0.7449415922164917]\n", - "[Epoch 0/1] [Batch 1908/12000] [D loss: 0.3330506980419159] [G loss: 0.7213020324707031]\n", - "[Epoch 0/1] [Batch 1909/12000] [D loss: 0.22348231077194214] [G loss: 1.1070059537887573]\n", - "[Epoch 0/1] [Batch 1910/12000] [D loss: 0.24543322622776031] [G loss: 0.9469972848892212]\n", - "[Epoch 0/1] [Batch 1911/12000] [D loss: 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[Batch 1932/12000] [D loss: 0.17344783246517181] [G loss: 1.2271703481674194]\n", - "[Epoch 0/1] [Batch 1933/12000] [D loss: 0.23418331146240234] [G loss: 0.9836533069610596]\n", - "[Epoch 0/1] [Batch 1934/12000] [D loss: 0.1935058832168579] [G loss: 1.136573076248169]\n", - "[Epoch 0/1] [Batch 1935/12000] [D loss: 0.41059228777885437] [G loss: 1.4102299213409424]\n", - "[Epoch 0/1] [Batch 1936/12000] [D loss: 0.14967961609363556] [G loss: 1.3520593643188477]\n", - "[Epoch 0/1] [Batch 1937/12000] [D loss: 0.23410774767398834] [G loss: 0.9838168025016785]\n", - "[Epoch 0/1] [Batch 1938/12000] [D loss: 0.20879873633384705] [G loss: 1.074786901473999]\n", - "[Epoch 0/1] [Batch 1939/12000] [D loss: 0.22523151338100433] [G loss: 1.0143487453460693]\n", - "[Epoch 0/1] [Batch 1940/12000] [D loss: 0.16872330009937286] [G loss: 1.2503337860107422]\n", - "[Epoch 0/1] [Batch 1941/12000] [D loss: 0.19197677075862885] [G loss: 1.1459015607833862]\n", - "[Epoch 0/1] [Batch 1942/12000] [D loss: 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11922/12000] [D loss: 0.07434841245412827] [G loss: 1.9792730808258057]\n", - "[Epoch 0/1] [Batch 11923/12000] [D loss: 0.17949135601520538] [G loss: 1.198608636856079]\n", - "[Epoch 0/1] [Batch 11924/12000] [D loss: 6.940688133239746] [G loss: 3.150968074798584]\n", - "[Epoch 0/1] [Batch 11925/12000] [D loss: 0.042234670370817184] [G loss: 2.5133039951324463]\n", - "[Epoch 0/1] [Batch 11926/12000] [D loss: 0.03558404743671417] [G loss: 2.678083658218384]\n", - "[Epoch 0/1] [Batch 11927/12000] [D loss: 0.08128765225410461] [G loss: 1.8968005180358887]\n", - "[Epoch 0/1] [Batch 11928/12000] [D loss: 0.8082725405693054] [G loss: 0.22137461602687836]\n", - "[Epoch 0/1] [Batch 11929/12000] [D loss: 0.030659835785627365] [G loss: 2.822157859802246]\n", - "[Epoch 0/1] [Batch 11930/12000] [D loss: 0.00305578182451427] [G loss: 5.1006269454956055]\n", - "[Epoch 0/1] [Batch 11931/12000] [D loss: 0.0110671641305089] [G loss: 3.821672201156616]\n", - "[Epoch 0/1] [Batch 11932/12000] [D loss: 0.047533921897411346] [G loss: 2.4003219604492188]\n", - "[Epoch 0/1] [Batch 11933/12000] [D loss: 0.015518931671977043] [G loss: 3.4880261421203613]\n", - "[Epoch 0/1] [Batch 11934/12000] [D loss: 0.02474316582083702] [G loss: 3.0306999683380127]\n", - "[Epoch 0/1] [Batch 11935/12000] [D loss: 0.008490198291838169] [G loss: 4.084174156188965]\n", - "[Epoch 0/1] [Batch 11936/12000] [D loss: 0.045862480998039246] [G loss: 2.4344727993011475]\n", - "[Epoch 0/1] [Batch 11937/12000] [D loss: 0.09184908866882324] [G loss: 1.7849045991897583]\n", - "[Epoch 0/1] [Batch 11938/12000] [D loss: 0.0246274434030056] [G loss: 3.0352730751037598]\n", - "[Epoch 0/1] [Batch 11939/12000] [D loss: 0.31941962242126465] [G loss: 0.7505747079849243]\n", - "[Epoch 0/1] [Batch 11940/12000] [D loss: 0.021298222243785858] [G loss: 3.1772072315216064]\n", - "[Epoch 0/1] [Batch 11941/12000] [D loss: 0.0045468769967556] [G loss: 4.704710960388184]\n", - "[Epoch 0/1] [Batch 11942/12000] [D loss: 0.0069402121007442474] [G loss: 4.284207820892334]\n", - "[Epoch 0/1] [Batch 11943/12000] [D loss: 0.04212784394621849] [G loss: 2.5157313346862793]\n", - "[Epoch 0/1] [Batch 11944/12000] [D loss: 0.0921362116932869] [G loss: 1.7820618152618408]\n", - "[Epoch 0/1] [Batch 11945/12000] [D loss: 0.03434452787041664] [G loss: 2.712313413619995]\n", - "[Epoch 0/1] [Batch 11946/12000] [D loss: 0.04112602770328522] [G loss: 2.538810968399048]\n", - "[Epoch 0/1] [Batch 11947/12000] [D loss: 0.24235749244689941] [G loss: 0.9567813277244568]\n", - "[Epoch 0/1] [Batch 11948/12000] [D loss: 0.054317522794008255] [G loss: 2.2735869884490967]\n", - "[Epoch 0/1] [Batch 11949/12000] [D loss: 0.0786018893122673] [G loss: 1.9277846813201904]\n", - "[Epoch 0/1] [Batch 11950/12000] [D loss: 0.09216111153364182] [G loss: 1.781815767288208]\n", - "[Epoch 0/1] [Batch 11951/12000] [D loss: 0.059039629995822906] [G loss: 2.1948578357696533]\n", - "[Epoch 0/1] [Batch 11952/12000] [D loss: 0.24018551409244537] 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3.3516857624053955]\n", - "[Epoch 0/1] [Batch 11983/12000] [D loss: 0.019173303619027138] [G loss: 3.2802011966705322]\n", - "[Epoch 0/1] [Batch 11984/12000] [D loss: 0.0782238095998764] [G loss: 1.932238221168518]\n", - "[Epoch 0/1] [Batch 11985/12000] [D loss: 0.16387997567653656] [G loss: 1.2748817205429077]\n", - "[Epoch 0/1] [Batch 11986/12000] [D loss: 0.008408796973526478] [G loss: 4.093726634979248]\n", - "[Epoch 0/1] [Batch 11987/12000] [D loss: 0.044820938259363174] [G loss: 2.456418991088867]\n", - "[Epoch 0/1] [Batch 11988/12000] [D loss: 0.12329575419425964] [G loss: 1.5207854509353638]\n", - "[Epoch 0/1] [Batch 11989/12000] [D loss: 0.07336963713169098] [G loss: 1.9915704727172852]\n", - "[Epoch 0/1] [Batch 11990/12000] [D loss: 0.4363557994365692] [G loss: 0.5409705638885498]\n", - "[Epoch 0/1] [Batch 11991/12000] [D loss: 0.03823548182845116] [G loss: 2.6088359355926514]\n", - "[Epoch 0/1] [Batch 11992/12000] [D loss: 0.05510196089744568] [G loss: 2.260018825531006]\n", - "[Epoch 0/1] [Batch 11993/12000] [D loss: 0.013046055100858212] [G loss: 3.65913987159729]\n", - "[Epoch 0/1] [Batch 11994/12000] [D loss: 0.07849512249231339] [G loss: 1.9290400743484497]\n", - "[Epoch 0/1] [Batch 11995/12000] [D loss: 0.02171831950545311] [G loss: 3.1580917835235596]\n", - "[Epoch 0/1] [Batch 11996/12000] [D loss: 0.16114424169063568] [G loss: 1.2920138835906982]\n", - "[Epoch 0/1] [Batch 11997/12000] [D loss: 0.07179585844278336] [G loss: 2.011718273162842]\n", - "[Epoch 0/1] [Batch 11998/12000] [D loss: 0.06098384037613869] [G loss: 2.164363384246826]\n", - "[Epoch 0/1] [Batch 11999/12000] [D loss: 0.09850254654884338] [G loss: 1.7214127779006958]\n" - ] - } - ], - "source": [ - "# Assume test_data is already defined and processed by your prepared_data function\n", - "# from standard_GAN import GAN\n", - "\n", - "gan = GAN()\n", - "gan.train(epochs=1, train_loader=train_dataset)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - 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a/resultat/rbm/200_Units_1_Chars_Sample_3.npy b/resultat/rbm/200_Units_1_Chars_Sample_3.npy new file mode 100644 index 0000000000000000000000000000000000000000..aed99464f5df804338375eb909135ef3d42ec607 GIT binary patch literal 2688 zcmb`Bv1-FW5JcrRU!m?2P+{yQ7(=>r4sKGo3L=t7VPYc%H^E=wAN40#DIS4A%dO73 z<}tIgvv+xW-)wiY?3De6s_s79u*$=_c@9M$nnU~3mfs(TwyOX9*K*(0z2AM6$GX># z%f-BSD)Mul{2sEwcYPVO?>4=?A&pdTM?ajKlvYjy|Y9I+fmDa!~JCzUE@u>cNMp zJ?zDLn2#m>Q!({Ed$E6v&5=(f|6$+K>XV;}RzE&Wy~n!v7w{dK-?%g%3i`JwFe zbTNO-W?6bkqrZpb`hBnN`KSKxulFsjFJcd0HNKL)P-;)84!vjXhu&qMKGpc4q{qj; zy;hGeZ{^hY^)M%AX?!I)i@I-V^42`m_w_M{q5amr)i<_py(iY^y)d@Nm$Ulup|5AC zIrl<2)O~vT-e-Ska-UvwEa`#tpq0!ab97&lQ Date: Sun, 31 Mar 2024 15:58:36 +0200 Subject: [PATCH 14/16] chore : improvement of figure --- notebook/experiments_ALPHA_DIGITS.ipynb | 10400 ++++++---------------- 1 file changed, 2627 insertions(+), 7773 deletions(-) diff --git a/notebook/experiments_ALPHA_DIGITS.ipynb b/notebook/experiments_ALPHA_DIGITS.ipynb index 861b420..e84affe 100644 --- a/notebook/experiments_ALPHA_DIGITS.ipynb +++ b/notebook/experiments_ALPHA_DIGITS.ipynb @@ -254,7 +254,7 @@ " for sample_idx in range(5):\n", " ax = axes[row_idx, col_idx * 5 + sample_idx]\n", " ax.imshow(generated_images[sample_idx].reshape(20, 16), cmap='plasma')\n", - " ax.set_title(f\"N_char {len(characters)}, Generation 1: {sample_idx+1}\")\n", + " ax.set_title(f\"N_chars {len(characters)}, Generation : {sample_idx+1}\")\n", " ax.axis('off')\n", "\n", " # Enregistrer chaque échantillon généré\n", @@ -271,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -279,14 +279,14 @@ "output_type": "stream", "text": [ "\n", - "Training RBM with 200 hidden units on characters E\n" + "Training RBM with 200 hidden units on characters ['E']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 427.21it/s]\n" + "Epoch 0: 100%|██████████| 3/3 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'2']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 372.20it/s]\n" + "Epoch 0: 100%|██████████| 11/11 [00:00<00:00, 296.06it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.20001.\n" + "Reconstruction error: 0.24198.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 410.55it/s]\n", - "Epoch 2: 100%|██████████| 3/3 [00:00<00:00, 612.04it/s]\n", - "Epoch 3: 100%|██████████| 3/3 [00:00<00:00, 499.68it/s]\n", - "Epoch 4: 100%|██████████| 3/3 [00:00<00:00, 392.64it/s]\n", - "Epoch 5: 100%|██████████| 3/3 [00:00<00:00, 459.33it/s]\n", - "Epoch 6: 100%|██████████| 3/3 [00:00<00:00, 664.01it/s]\n", - "Epoch 7: 100%|██████████| 3/3 [00:00<00:00, 662.57it/s]\n", - "Epoch 8: 100%|██████████| 3/3 [00:00<00:00, 482.81it/s]\n", - "Epoch 9: 100%|██████████| 3/3 [00:00<00:00, 542.86it/s]\n", - "Epoch 10: 100%|██████████| 3/3 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{ "data": { - "image/png": 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+ "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Training RBM with 200 hidden units on characters E\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 386.18it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.20001.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 498.87it/s]\n", - "Epoch 2: 100%|██████████| 3/3 [00:00<00:00, 315.03it/s]\n", - "Epoch 3: 100%|██████████| 3/3 [00:00<00:00, 429.57it/s]\n", - "Epoch 4: 100%|██████████| 3/3 [00:00<00:00, 229.41it/s]\n", - "Epoch 5: 100%|██████████| 3/3 [00:00<00:00, 314.79it/s]\n", - "Epoch 6: 100%|██████████| 3/3 [00:00<00:00, 284.85it/s]\n", - "Epoch 7: 100%|██████████| 3/3 [00:00<00:00, 70.33it/s]\n", - "Epoch 8: 100%|██████████| 3/3 [00:00<00:00, 373.93it/s]\n", - 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DBM" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os\n", + "\n", + "def run_dbm_experiment(hidden_layers_sizes, n_epochs=100, character_sets=[['A', 'B'], ['1', '2', '3', '4'], ['A', 'B', '1', '2']]):\n", + " # Assumes there's only one configuration of hidden layers sizes provided\n", + " layer_sizes = hidden_layers_sizes[0]\n", + "\n", + " # Pour chaque ensemble de caractères, nous allons générer et afficher des images\n", + " for characters in character_sets:\n", + " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", + "\n", + " # Initialiser une nouvelle figure\n", + " plt.figure(figsize=(15, 3)) # Taille ajustée pour l'ensemble de subplots\n", + "\n", + " print(f\"\\nTraining DBN with hidden layers: {layer_sizes}\")\n", + " dbn = DBN(n_visible=data.shape[1], hidden_layer_sizes=layer_sizes, random_state=42)\n", + " dbn.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=16, print_each=1000000)\n", + "\n", + " # Génération de 5 images\n", + " generated_images = dbn.generate_image(n_samples=5)\n", + "\n", + " for img_idx in range(5):\n", + " ax = plt.subplot(1, 5, img_idx + 1)\n", + " ax.imshow(generated_images[img_idx].reshape(20, 16), cmap='plasma') # Assurez-vous que la forme est correcte\n", + " ax.set_title(f\"Sample: {img_idx + 1}\")\n", + " ax.axis('off')\n", + "\n", + " # Enregistrement des images générées\n", + " directory = f\"../resultat/dbn/{'_'.join([str(size) for size in layer_sizes])}_Units_{len(characters)}_Chars\"\n", + " os.makedirs(directory, exist_ok=True)\n", + " for img_idx, img in enumerate(generated_images):\n", + " np.save(f\"{directory}/Sample_{img_idx}_Chars_{''.join(characters)}.npy\", img)\n", + "\n", + " # Enregistrer la figure complète pour cet ensemble de caractères\n", + " plt.tight_layout()\n", + " directory_image = f\"../resultat/images/dbn/{'_'.join(characters)}\"\n", + " os.makedirs(directory_image, exist_ok=True)\n", + " plt.savefig(f\"{directory_image}/dbn_{len(characters)}_chars_{'_'.join([str(size) for size in layer_sizes])}_Units.png\")\n", + "\n", + " # Afficher toutes les figures à la fin de la boucle\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", - "Training RBM with 200 hidden units on characters 2\n" + "Training DBN with hidden layers: [200, 200]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 463.31it/s]\n" + "Epoch 0: 100%|██████████| 5/5 [00:00<00:00, 248.27it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reconstruction error: 0.19603.\n" + "Reconstruction error: 0.21931.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 1: 100%|██████████| 3/3 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Training RBM with 200 hidden units on characters E\n" + "Reconstruction error: 0.00391.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 260.24it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.20001.\n" + "Epoch 1: 0%| | 0/5 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 333.48it/s]\n", - "Epoch 2: 100%|██████████| 3/3 [00:00<00:00, 314.20it/s]\n", - "Epoch 3: 100%|██████████| 3/3 [00:00<00:00, 108.62it/s]\n", - "Epoch 4: 100%|██████████| 3/3 [00:00<00:00, 65.74it/s]\n", - "Epoch 5: 100%|██████████| 3/3 [00:00<00:00, 300.23it/s]\n", - "Epoch 6: 100%|██████████| 3/3 [00:00<00:00, 434.39it/s]\n", - "Epoch 7: 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293.66it/s]\n", - "Epoch 499: 100%|██████████| 3/3 [00:00<00:00, 503.40it/s]\n" - ] + "data": { + "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -8231,85 +3159,11 @@ } ], "source": [ - "# configuration = 2 layer with 200 units each\n", - "configurations_fixe = [200]\n", - "run_rbm_experiment(configurations_fixe, n_epochs=500, character_sets = ['E'])\n", - "run_rbm_experiment(configurations_fixe, n_epochs=500, character_sets = ['E', 'Y'])\n", - "run_rbm_experiment(configurations_fixe, n_epochs=500, character_sets = ['E', 'Y', '2'])\n", - "run_rbm_experiment(configurations_fixe, n_epochs=500, character_sets = ['E', 'Y', 'A', '2'])\n", - "run_rbm_experiment(configurations_fixe, n_epochs=500, character_sets=['E', 'Y', 'A', '2', '7'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Exemple d'utilisation\n", - "hidden_units_sizes = [100, 200, 300, 400, 500, 600, 700]\n", - "run_rbm_experiment(hidden_units_sizes, n_epochs=2, character_sets=[['A', 'B'], ['A', 'B', 'C'], ['A', 'B', 'C', 'D']])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. DBM" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "def run_dbm_experiment(hidden_layers_sizes, n_epochs=100, characters=['A', 'B', '1', '2']):\n", - " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", - "\n", - " # Déterminer le nombre maximum de couches et le nombre unique d'unités\n", - " max_layers = max(len(sizes) for sizes in hidden_layers_sizes)\n", - " unique_units = sorted({sizes[0] for sizes in hidden_layers_sizes})\n", - "\n", - " # Préparer une grille de subplots\n", - " fig, axes = plt.subplots(len(unique_units), max_layers, figsize=(max_layers * 3, len(unique_units) * 3), squeeze=False)\n", - "\n", - " # Initialiser tous les axes comme invisibles; ils seront activés lorsqu'utilisés\n", - " for ax_row in axes:\n", - " for ax in ax_row:\n", - " ax.set_visible(False)\n", - "\n", - " for layer_sizes in hidden_layers_sizes:\n", - " print(f\"\\nTraining DBN with hidden layers: {layer_sizes}\")\n", - " dbn = DBN(n_visible=data.shape[1], hidden_layer_sizes=layer_sizes, random_state=42)\n", - " dbn.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=16, print_each=1000000)\n", - "\n", - " # Génération et affichage d'une image\n", - " generated_image = dbn.generate_image(n_samples=5)\n", - " unit_idx = unique_units.index(layer_sizes[0])\n", - " layer_idx = len(layer_sizes) - 2 # Index 0 pour 2 couches, index 1 pour 3 couches, etc.\n", + "# Example fixed configuration: two layers with 200 units each\n", + "fixed_configuration = [[200, 200]]\n", "\n", - " ax = axes[unit_idx][layer_idx]\n", - " ax.set_visible(True)\n", - " ax.imshow(generated_image[0].reshape(20, 16), cmap='plasma')\n", - " ax.set_title(f\"N_Layers: {len(layer_sizes)}, N_Units: {layer_sizes[0]}, N_Chars: {len(characters)}\")\n", - " ax.axis('off')\n", - "\n", - " # Enregistrement de l'image générée\n", - " directory = f\"../resultat/dbn/{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers\"\n", - " os.makedirs(directory, exist_ok=True)\n", - " np.save(f\"{directory}/Units_{layer_sizes[0]}_Chars_{''.join(characters)}.npy\", generated_image[0])\n", - "\n", - " #save figure\n", - " plt.tight_layout()\n", - " directory_image = \"../resultat/images/dbn\"\n", - " os.makedirs(directory_image, exist_ok=True)\n", - " plt.savefig(f\"{directory_image}/dbn_{len(characters)}_chars_{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers.png\")\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n" + "# Run the experiment with the fixed configuration and different character sets\n", + "run_dbm_experiment(fixed_configuration, n_epochs=2, character_sets=[['A', 'B'], ['1', '2', '3', '4'], ['A', 'B', '1', '2']])\n" ] }, { From 81b5699b055823920ff9a1d662d0d65c5c4f114e Mon Sep 17 00:00:00 2001 From: Yann CHOHO Date: Sun, 31 Mar 2024 16:40:07 +0200 Subject: [PATCH 15/16] chore : factorisation of code --- notebook/experiments_ALPHA_DIGITS.ipynb | 2895 +---------------- .../Sample_0_Chars_AB.npy | Bin 0 -> 2688 bytes .../Sample_1_Chars_AB.npy | Bin 0 -> 2688 bytes .../Sample_2_Chars_AB.npy | Bin 0 -> 2688 bytes .../Sample_3_Chars_AB.npy | Bin 0 -> 2688 bytes .../Sample_4_Chars_AB.npy | Bin 0 -> 2688 bytes .../Sample_0_Chars_1234.npy | Bin 0 -> 2688 bytes .../Sample_0_Chars_AB12.npy | Bin 0 -> 2688 bytes .../Sample_1_Chars_1234.npy | Bin 0 -> 2688 bytes .../Sample_1_Chars_AB12.npy | Bin 0 -> 2688 bytes .../Sample_2_Chars_1234.npy | Bin 0 -> 2688 bytes .../Sample_2_Chars_AB12.npy | Bin 0 -> 2688 bytes .../Sample_3_Chars_1234.npy | Bin 0 -> 2688 bytes .../Sample_3_Chars_AB12.npy | Bin 0 -> 2688 bytes .../Sample_4_Chars_1234.npy | Bin 0 -> 2688 bytes .../Sample_4_Chars_AB12.npy | Bin 0 -> 2688 bytes .../Sample_0_Chars_E.npy | Bin 0 -> 2688 bytes .../Sample_1_Chars_E.npy | Bin 0 -> 2688 bytes .../Sample_2_Chars_E.npy | Bin 0 -> 2688 bytes .../Sample_3_Chars_E.npy | Bin 0 -> 2688 bytes 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.../Sample_4_Chars_EYA27.npy | Bin 0 -> 2688 bytes .../dbn/1_2_3_4/dbn_4_chars_200_200_Units.png | Bin 0 -> 9862 bytes .../dbn/A_B/dbn_2_chars_200_200_Units.png | Bin 0 -> 10251 bytes .../dbn/A_B_1_2/dbn_4_chars_200_200_Units.png | Bin 0 -> 10175 bytes .../E/dbn_1_chars_400_400_400_400_Units.png | Bin 0 -> 9002 bytes .../E_Y/dbn_2_chars_400_400_400_400_Units.png | Bin 0 -> 9204 bytes .../dbn_3_chars_400_400_400_400_Units.png | Bin 0 -> 9092 bytes .../dbn_4_chars_400_400_400_400_Units.png | Bin 0 -> 9217 bytes .../dbn_5_chars_400_400_400_400_Units.png | Bin 0 -> 9111 bytes 49 files changed, 10 insertions(+), 2885 deletions(-) create mode 100644 resultat/dbn/200_200_Units_2_Chars/Sample_0_Chars_AB.npy create mode 100644 resultat/dbn/200_200_Units_2_Chars/Sample_1_Chars_AB.npy create mode 100644 resultat/dbn/200_200_Units_2_Chars/Sample_2_Chars_AB.npy create mode 100644 resultat/dbn/200_200_Units_2_Chars/Sample_3_Chars_AB.npy create mode 100644 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resultat/dbn/400_400_400_400_Units_5_Chars/Sample_4_Chars_EYA27.npy create mode 100644 resultat/images/dbn/1_2_3_4/dbn_4_chars_200_200_Units.png create mode 100644 resultat/images/dbn/A_B/dbn_2_chars_200_200_Units.png create mode 100644 resultat/images/dbn/A_B_1_2/dbn_4_chars_200_200_Units.png create mode 100644 resultat/images/dbn/E/dbn_1_chars_400_400_400_400_Units.png create mode 100644 resultat/images/dbn/E_Y/dbn_2_chars_400_400_400_400_Units.png create mode 100644 resultat/images/dbn/E_Y_A/dbn_3_chars_400_400_400_400_Units.png create mode 100644 resultat/images/dbn/E_Y_A_2/dbn_4_chars_400_400_400_400_Units.png create mode 100644 resultat/images/dbn/E_Y_A_2_7/dbn_5_chars_400_400_400_400_Units.png diff --git a/notebook/experiments_ALPHA_DIGITS.ipynb b/notebook/experiments_ALPHA_DIGITS.ipynb index e84affe..2d9f5a2 100644 --- a/notebook/experiments_ALPHA_DIGITS.ipynb +++ b/notebook/experiments_ALPHA_DIGITS.ipynb @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -225,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -271,2655 +271,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Training RBM with 200 hidden units on characters ['E']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 448.67it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.20001.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 3/3 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# configuration = 2 layer with 200 units each\n", "configurations_fixe = [200]\n", @@ -2935,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2964,7 +318,7 @@ " for img_idx in range(5):\n", " ax = plt.subplot(1, 5, img_idx + 1)\n", " ax.imshow(generated_images[img_idx].reshape(20, 16), cmap='plasma') # Assurez-vous que la forme est correcte\n", - " ax.set_title(f\"Sample: {img_idx + 1}\")\n", + " ax.set_title(f\"N_chars {len(characters)} , generation: {img_idx + 1}\")\n", " ax.axis('off')\n", "\n", " # Enregistrement des images générées\n", @@ -2985,244 +339,15 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Training DBN with hidden layers: [200, 200]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 0: 100%|██████████| 5/5 [00:00<00:00, 248.27it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.21931.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 100%|██████████| 5/5 [00:00<00:00, 269.52it/s]\n", - "Epoch 0: 100%|██████████| 5/5 [00:00<00:00, 876.08it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reconstruction error: 0.00391.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Epoch 1: 0%| | 0/5 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Example fixed configuration: two layers with 200 units each\n", - "fixed_configuration = [[200, 200]]\n", + "fixed_configuration = [[400, 400, 400, 400]]\n", "\n", "# Run the experiment with the fixed configuration and different character sets\n", - "run_dbm_experiment(fixed_configuration, n_epochs=2, character_sets=[['A', 'B'], ['1', '2', '3', '4'], ['A', 'B', '1', '2']])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Exemple d'utilisation avec des configurations générées\n", - "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", - "run_dbm_experiment(configurations, n_epochs=2, characters=['Y'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Exemple d'utilisation avec des configurations générées\n", - "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", - "run_dbm_experiment(configurations, n_epochs=1000, characters=['Y', 'A'])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Exemple d'utilisation avec des configurations générées\n", - "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", - "run_dbm_experiment(configurations, n_epochs=1000, characters=['Y', 'U', 'Z'])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Exemple d'utilisation avec des configurations générées\n", - "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", - "run_dbm_experiment(configurations, n_epochs=1000, characters=['Y', 'A', 'B', '1'])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Exemple d'utilisation avec des configurations générées\n", - "configurations = generate_symmetric_configurations(min_layers = 4, max_layers = 4, min_neurons = 400, max_neurons = 400, step_neurons = 100)\n", - "run_dbm_experiment(configurations, n_epochs=1000, characters=['Y', 'A', 'B', '1', '2'])\n", - "\n" + "run_dbm_experiment(fixed_configuration, n_epochs=500, character_sets=[['E'],['E', 'Y'], ['E', 'Y', 'A'], ['E', 'Y', 'A', '2'], ['E', 'Y', 'A', '2', '7']])\n" ] }, { diff --git a/resultat/dbn/200_200_Units_2_Chars/Sample_0_Chars_AB.npy b/resultat/dbn/200_200_Units_2_Chars/Sample_0_Chars_AB.npy new file mode 100644 index 0000000000000000000000000000000000000000..e2137c984bf9f694dbcd8af1192fc27f0d0ab46a GIT binary patch literal 2688 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zH-Apg8RBjV`5Qw98!Lm-T+D{jEPstWA6k~)!Te_Px@>z=TiiZDSrZZ{!m8T2fyvJI z5`Dng*% z?Y1P$mqXP{7Nd?CTKC{hTU!*qEe(Mty!!OTuMI`F=l9v=fi}+4y(a^<8twP%3H2+f z3$vko9GJvQy9P8|%j^ZxB>f2Nx=R&%PW>zI=P$UqfY5*)bKGm_mpUMNps$P5;%11* zZw8`}-yG`UOHoUb#j8@?z8UM7I{cL$2L3CLu=?zr*S9;iL5PaGeuN3nhmuTZ^lVddkD0H74w{VzpDa%Qhtx4SlRr9L`52}_fWr=bmwC}wVx4Xz`}#9)-#(0B zpbYi;EZnJ z9CbLSh8d8_A4gP0g1HW&IvZB?h)3B&vPK5ueIY+Q~H?L+!jv_a6Tb^Jop2 literal 0 HcmV?d00001 From a061aa94903bab3486f656be1c9d1dff6b147b1b Mon Sep 17 00:00:00 2001 From: Yann CHOHO Date: Sun, 31 Mar 2024 20:55:39 +0200 Subject: [PATCH 16/16] chore : add docstring and comment in english --- notebook/experiments_ALPHA_DIGITS.ipynb | 147 ++++++++++++++++-------- 1 file changed, 99 insertions(+), 48 deletions(-) diff --git a/notebook/experiments_ALPHA_DIGITS.ipynb b/notebook/experiments_ALPHA_DIGITS.ipynb index 2d9f5a2..ef7b765 100644 --- a/notebook/experiments_ALPHA_DIGITS.ipynb +++ b/notebook/experiments_ALPHA_DIGITS.ipynb @@ -47,17 +47,17 @@ "source": [ "def generate_symmetric_configurations(min_layers, max_layers, min_neurons, max_neurons, step_neurons):\n", " \"\"\"\n", - " Générer des configurations symétriques pour les couches cachées du DBN.\n", + " Generate symmetrical configurations for the DBN's hidden layers.\n", "\n", " Args:\n", - " min_layers (int): Nombre minimum de couches cachées.\n", - " max_layers (int): Nombre maximum de couches cachées.\n", - " min_neurons (int): Nombre minimum de neurones par couche.\n", - " max_neurons (int): Nombre maximum de neurones par couche.\n", - " step_neurons (int): Pas d'augmentation du nombre de neurones.\n", + " min_layers (int): Minimum number of hidden layers.\n", + " max_layers (int): Maximum number of hidden layers.\n", + " min_neurons (int): Minimum number of neurons per layer.\n", + " max_neurons (int): Maximum number of neurons per layer.\n", + " step_neurons (int): No increase in the number of neurons.\n", "\n", " Returns:\n", - " List[List[int]]: Liste des configurations symétriques des couches cachées.\n", + " List[List[int]]: List of symmetrical configurations of hidden layers.\n", " \"\"\"\n", " configurations = []\n", " for num_layers in range(min_layers, max_layers + 1):\n", @@ -82,10 +82,21 @@ "outputs": [], "source": [ "def run_rbm_experiment(hidden_units_sizes, n_epochs=100, character_sets=[['A', 'B'], ['1', '2', '3', '4'], ['A', 'B', '1', '2']]):\n", - " # Déterminer le nombre unique d'unités\n", + " \"\"\"\n", + " Conducts an experiment with Restricted Boltzmann Machines (RBMs) on different character sets.\n", + "\n", + " Args:\n", + " hidden_units_sizes (list): A list of sizes for the hidden layers to experiment with.\n", + " n_epochs (int): The number of training epochs. Default is 100.\n", + " character_sets (list of lists): A list containing different sets of characters to train RBMs on.\n", + "\n", + " This function trains an RBM for each combination of character set and hidden unit size,\n", + " generates and saves images representing the learned features, and plots the results in a grid.\n", + " \"\"\"\n", + " # Determine the unique number of units\n", " unique_units = sorted(hidden_units_sizes)\n", "\n", - " # Préparer une grille de subplots\n", + " # Prepare a grid of subplots\n", " fig, axes = plt.subplots(len(character_sets), len(unique_units), figsize=(len(unique_units) * 3, len(character_sets) * 3), squeeze=False)\n", "\n", " for row_idx, characters in enumerate(character_sets):\n", @@ -96,10 +107,10 @@ " rbm = RBM(n_visible=data.shape[1], n_hidden=num_units, random_state=42)\n", " rbm.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=15, print_each=5000)\n", "\n", - " # Génération et affichage d'une image\n", + " # Generate and display an image\n", " generated_image = rbm.generate_image(n_samples=1)\n", "\n", - " # Enregistrer l'image\n", + " # Save the image\n", " save_path = f\"../resultat/rbm/{num_units}_Units_{len(characters)}_Chars.npy\"\n", " os.makedirs(os.path.dirname(save_path), exist_ok=True)\n", " np.save(save_path, generated_image)\n", @@ -110,7 +121,7 @@ " ax.axis('off')\n", "\n", " plt.tight_layout()\n", - " # Enregistrement de l'image générée\n", + " # Save the generated image\n", " directory_image = \"../resultat/images/rbm\"\n", " os.makedirs(directory_image, exist_ok=True)\n", " plt.savefig(f\"{directory_image}/rbm_{len(characters)}_chars_Units_{num_units}_Layers_{character_sets}.png\")\n", @@ -125,16 +136,8 @@ "outputs": [], "source": [ "hidden_units_sizes = [100, 200, 300, 400, 500, 600, 700]\n", - "run_rbm_experiment(hidden_units_sizes, n_epochs=2, character_sets=['Y'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_rbm_experiment(hidden_units_sizes, n_epochs=1000, character_sets=['Y'])\n" + "run_rbm_experiment(hidden_units_sizes, n_epochs=1000, character_sets=['E'])\n", + "run_rbm_experiment(hidden_units_sizes, n_epochs=1000, character_sets=['A'])\n" ] }, { @@ -153,16 +156,28 @@ "import matplotlib.pyplot as plt\n", "\n", "def run_dbm_experiment(hidden_layers_sizes, n_epochs=100, characters=['A', 'B', '1', '2']):\n", + " \"\"\"\n", + " Conducts an experiment with Deep Belief Networks (DBNs) on a set of characters.\n", + "\n", + " Args:\n", + " hidden_layers_sizes (list of lists): A list containing the sizes of hidden layers to experiment with.\n", + " n_epochs (int): The number of training epochs. Default is 100.\n", + " characters (list): The characters to use in the experiment. Default is ['A', 'B', '1', '2'].\n", + "\n", + " This function trains a DBN for each specified configuration of hidden layer sizes,\n", + " generates and saves images representing the learned features, and plots the results in a grid.\n", + " \"\"\"\n", + " # Load the data\n", " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", "\n", - " # Déterminer le nombre maximum de couches et le nombre unique d'unités\n", + " # Determine the maximum number of layers and the unique number of units\n", " max_layers = max(len(sizes) for sizes in hidden_layers_sizes)\n", " unique_units = sorted({sizes[0] for sizes in hidden_layers_sizes})\n", "\n", - " # Préparer une grille de subplots\n", + " # Prepare a grid of subplots\n", " fig, axes = plt.subplots(len(unique_units), max_layers, figsize=(max_layers * 3, len(unique_units) * 3), squeeze=False)\n", "\n", - " # Initialiser tous les axes comme invisibles; ils seront activés lorsqu'utilisés\n", + " # Initialize all axes as invisible; they will be activated when used\n", " for ax_row in axes:\n", " for ax in ax_row:\n", " ax.set_visible(False)\n", @@ -172,10 +187,10 @@ " dbn = DBN(n_visible=data.shape[1], hidden_layer_sizes=layer_sizes, random_state=42)\n", " dbn.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=16, print_each=1000000)\n", "\n", - " # Génération et affichage d'une image\n", + " # Generate and display an image\n", " generated_image = dbn.generate_image(n_samples=1)\n", " unit_idx = unique_units.index(layer_sizes[0])\n", - " layer_idx = len(layer_sizes) - 2 # Index 0 pour 2 couches, index 1 pour 3 couches, etc.\n", + " layer_idx = len(layer_sizes) - 2 # Index 0 for 2 layers, index 1 for 3 layers, etc.\n", "\n", " ax = axes[unit_idx][layer_idx]\n", " ax.set_visible(True)\n", @@ -183,19 +198,18 @@ " ax.set_title(f\"N_Layers: {len(layer_sizes)}, N_Units: {layer_sizes[0]}\")\n", " ax.axis('off')\n", "\n", - " # Enregistrement de l'image générée\n", + " # Save the generated image\n", " directory = f\"../resultat/dbn/{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers\"\n", " os.makedirs(directory, exist_ok=True)\n", " np.save(f\"{directory}/Units_{layer_sizes[0]}_Chars_{''.join(characters)}.npy\", generated_image[0])\n", "\n", - " #save figure\n", + " # Save the figure\n", " plt.tight_layout()\n", " directory_image = \"../resultat/images/dbn\"\n", " os.makedirs(directory_image, exist_ok=True)\n", " plt.savefig(f\"{directory_image}/dbn_{len(characters)}_chars_{layer_sizes[0]}_Units_{len(layer_sizes)}_Layers.png\")\n", " plt.tight_layout()\n", - " plt.show()\n", - "\n" + " plt.show()\n" ] }, { @@ -206,7 +220,7 @@ "source": [ "# Exemple d'utilisation avec des configurations générées\n", "configurations = generate_symmetric_configurations(min_layers = 2, max_layers = 5, min_neurons = 100, max_neurons = 700, step_neurons = 100)\n", - "run_dbm_experiment(configurations, n_epochs=1000, characters=['Y'])" + "run_dbm_experiment(configurations, n_epochs=1, characters=['Y'])" ] }, { @@ -223,6 +237,13 @@ "### 1. RBM" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "we modify the above corresponding function to have plot adapted to our analysis" + ] + }, { "cell_type": "code", "execution_count": null, @@ -234,10 +255,21 @@ "import os\n", "\n", "def run_rbm_experiment(hidden_units_sizes, n_epochs=100, character_sets=[['A', 'B'], ['1', '2', '3', '4'], ['A', 'B', '1', '2']]):\n", + " \"\"\"\n", + " Conducts an experiment with Restricted Boltzmann Machines (RBMs) on different character sets,\n", + " generating multiple samples for each configuration.\n", + "\n", + " Args:\n", + " hidden_units_sizes (list): A list of sizes for the hidden units to experiment with.\n", + " n_epochs (int): The number of training epochs. Default is 100.\n", + " character_sets (list of lists): A list containing different sets of characters to train RBMs on.\n", + "\n", + " This function trains an RBM for each combination of character set and hidden unit size,\n", + " generates and displays five samples from each trained RBM, and saves the results.\n", + " \"\"\"\n", " unique_units = sorted(hidden_units_sizes)\n", "\n", - " # Préparer une grille de subplots\n", - " # Chaque configuration a maintenant 5 colonnes pour les 5 échantillons\n", + " # Prepare a grid of subplots; each configuration now has 5 columns for the 5 samples\n", " fig, axes = plt.subplots(len(character_sets), len(unique_units) * 5, figsize=(len(unique_units) * 3 * 5, len(character_sets) * 3), squeeze=False)\n", "\n", " for row_idx, characters in enumerate(character_sets):\n", @@ -248,16 +280,16 @@ " rbm = RBM(n_visible=data.shape[1], n_hidden=num_units, random_state=42)\n", " rbm.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=15, print_each=5000)\n", "\n", - " # Génération de 5 images\n", + " # Generate 5 images\n", " generated_images = rbm.generate_image(n_samples=5)\n", "\n", " for sample_idx in range(5):\n", " ax = axes[row_idx, col_idx * 5 + sample_idx]\n", " ax.imshow(generated_images[sample_idx].reshape(20, 16), cmap='plasma')\n", - " ax.set_title(f\"N_chars {len(characters)}, Generation : {sample_idx+1}\")\n", + " ax.set_title(f\"N_chars {len(characters)}, Generation: {sample_idx + 1}\")\n", " ax.axis('off')\n", "\n", - " # Enregistrer chaque échantillon généré\n", + " # Save each generated sample\n", " save_path = f\"../resultat/rbm/{num_units}_Units_{len(characters)}_Chars_Sample_{sample_idx}.npy\"\n", " os.makedirs(os.path.dirname(save_path), exist_ok=True)\n", " np.save(save_path, generated_images[sample_idx])\n", @@ -277,7 +309,7 @@ "source": [ "# configuration = 2 layer with 200 units each\n", "configurations_fixe = [200]\n", - "run_rbm_experiment(configurations_fixe, n_epochs=500, character_sets = [['E'],['E', 'Y'], ['E', 'Y', 'A'], ['E', 'Y', 'A', '2'], ['E', 'Y', 'A', '2', '7']])\n" + "run_rbm_experiment(configurations_fixe, n_epochs=2, character_sets = [['E'],['E', 'O'], ['E', 'O', 'A'], ['E', 'O', 'A', '2'], ['E', 'O', 'A', '2', '7']])\n" ] }, { @@ -287,6 +319,13 @@ "### 2. DBM" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "we modify the above corresponding function to have plot adapted to our analysis" + ] + }, { "cell_type": "code", "execution_count": null, @@ -298,42 +337,54 @@ "import os\n", "\n", "def run_dbm_experiment(hidden_layers_sizes, n_epochs=100, character_sets=[['A', 'B'], ['1', '2', '3', '4'], ['A', 'B', '1', '2']]):\n", + " \"\"\"\n", + " Conducts an experiment with Deep Boltzmann Machines (DBMs) on different character sets,\n", + " generating multiple samples for each configuration.\n", + "\n", + " Args:\n", + " hidden_layers_sizes (list of lists): A list containing the sizes of hidden layers to experiment with.\n", + " n_epochs (int): The number of training epochs. Default is 100.\n", + " character_sets (list of lists): A list containing different sets of characters to train DBMs on.\n", + "\n", + " For each character set, this function trains a DBM, generates and displays five samples,\n", + " and saves the generated images and the complete figure for each set.\n", + " \"\"\"\n", " # Assumes there's only one configuration of hidden layers sizes provided\n", " layer_sizes = hidden_layers_sizes[0]\n", "\n", - " # Pour chaque ensemble de caractères, nous allons générer et afficher des images\n", + " # For each set of characters, generate and display images\n", " for characters in character_sets:\n", " data = read_alpha_digit(characters, file_path=ALPHA_DIGIT_PATH)\n", "\n", - " # Initialiser une nouvelle figure\n", - " plt.figure(figsize=(15, 3)) # Taille ajustée pour l'ensemble de subplots\n", + " # Initialize a new figure\n", + " plt.figure(figsize=(15, 3)) # Adjusted size for the set of subplots\n", "\n", " print(f\"\\nTraining DBN with hidden layers: {layer_sizes}\")\n", " dbn = DBN(n_visible=data.shape[1], hidden_layer_sizes=layer_sizes, random_state=42)\n", " dbn.train(data, learning_rate=0.1, n_epochs=n_epochs, batch_size=16, print_each=1000000)\n", "\n", - " # Génération de 5 images\n", + " # Generate 5 images\n", " generated_images = dbn.generate_image(n_samples=5)\n", "\n", " for img_idx in range(5):\n", " ax = plt.subplot(1, 5, img_idx + 1)\n", - " ax.imshow(generated_images[img_idx].reshape(20, 16), cmap='plasma') # Assurez-vous que la forme est correcte\n", - " ax.set_title(f\"N_chars {len(characters)} , generation: {img_idx + 1}\")\n", + " ax.imshow(generated_images[img_idx].reshape(20, 16), cmap='plasma') # Ensure the shape is correct\n", + " ax.set_title(f\"N_chars {len(characters)}, generation: {img_idx + 1}\")\n", " ax.axis('off')\n", "\n", - " # Enregistrement des images générées\n", + " # Save the generated images\n", " directory = f\"../resultat/dbn/{'_'.join([str(size) for size in layer_sizes])}_Units_{len(characters)}_Chars\"\n", " os.makedirs(directory, exist_ok=True)\n", " for img_idx, img in enumerate(generated_images):\n", " np.save(f\"{directory}/Sample_{img_idx}_Chars_{''.join(characters)}.npy\", img)\n", "\n", - " # Enregistrer la figure complète pour cet ensemble de caractères\n", + " # Save the complete figure for this set of characters\n", " plt.tight_layout()\n", " directory_image = f\"../resultat/images/dbn/{'_'.join(characters)}\"\n", " os.makedirs(directory_image, exist_ok=True)\n", " plt.savefig(f\"{directory_image}/dbn_{len(characters)}_chars_{'_'.join([str(size) for size in layer_sizes])}_Units.png\")\n", "\n", - " # Afficher toutes les figures à la fin de la boucle\n", + " # Display all figures at the end of the loop\n", " plt.show()\n" ] }, @@ -347,7 +398,7 @@ "fixed_configuration = [[400, 400, 400, 400]]\n", "\n", "# Run the experiment with the fixed configuration and different character sets\n", - "run_dbm_experiment(fixed_configuration, n_epochs=500, character_sets=[['E'],['E', 'Y'], ['E', 'Y', 'A'], ['E', 'Y', 'A', '2'], ['E', 'Y', 'A', '2', '7']])\n" + "run_dbm_experiment(fixed_configuration, n_epochs=2, character_sets=[['E'],['E', 'Y'], ['E', 'Y', 'A'], ['E', 'Y', 'A', '2'], ['E', 'Y', 'A', '2', '7']])\n" ] }, {