From 6c761393151384d60b24ba1dbdfb3b53aa8d04cc Mon Sep 17 00:00:00 2001 From: VicHofs <57238498+VicHofs@users.noreply.github.com> Date: Mon, 3 Feb 2020 17:36:25 -0300 Subject: [PATCH 01/12] Create conv-net tensorflow model 93% Basic CNN model using TensorFlow 2.0 benchmarks at 93% balanced accuracy with base version --- conv-net tensorflow model 93% | 101 ++++++++++++++++++++++++++++++++++ 1 file changed, 101 insertions(+) create mode 100644 conv-net tensorflow model 93% diff --git a/conv-net tensorflow model 93% b/conv-net tensorflow model 93% new file mode 100644 index 0000000..a3f0e01 --- /dev/null +++ b/conv-net tensorflow model 93% @@ -0,0 +1,101 @@ +########################### +#KMNIST49 CNN model +#by Victor Hofstetter +#02/02/2020 +########################### + + +import tensorflow as tf +import numpy as np +import matplotlib.pyplot as plt + +def balanced_accuracy(test_labels, predictions, outputs): + totals = [] + for cls in range(outputs): + total = 0 + for i in test_labels: + if i == cls: + total = total + 1 + totals.append(total) + + hits = [] + for cls in range(outputs): + total_hits = 0 + for i in range(0, test_labels.shape[0]): + if test_labels[i] == cls == np.argmax(predictions[i]): + total_hits = total_hits + 1 + hits.append(total_hits) + + accuracy_list = [] + for i in range(0, len(hits)): + accuracy = hits[i] / totals[i] + accuracy_list.append(accuracy) + + print(f'The balanced accuracy is: {np.mean(accuracy_list)}') + +train_data = np.load('k49-train-imgs.npz')['arr_0'] +train_labels = np.load('k49-train-labels.npz')['arr_0'] +test_data = np.load('k49-test-imgs.npz')['arr_0'] +test_labels = np.load('k49-test-labels.npz')['arr_0'] + +train_data1 = tf.keras.utils.normalize(train_data, axis=1) +test_data1 = tf.keras.utils.normalize(test_data, axis=1) + +train_data = train_data1.reshape((train_data.shape[0], train_data.shape[1], train_data.shape[2], 1)) +test_data = test_data1.reshape((test_data.shape[0], test_data.shape[1], test_data.shape[2], 1)) + +train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_labels)) +test_dataset = tf.data.Dataset.from_tensor_slices((test_data, test_labels)) + +#outlining number of validation samples (picked 8% of training data) +num_validation_samples = 0.08 * train_data.shape[0] +num_validation_samples = tf.cast(num_validation_samples, tf.int64) + +#isolating test samples +num_test_samples = test_data.shape[0] +num_test_samples = tf.cast(num_test_samples, tf.int64) + +#setting buffer size and shuffling training data +BUFFER_SIZE = 10000 +train_dataset = train_dataset.shuffle(BUFFER_SIZE) +#extracting validation data from grouping into own var +validation_dataset = train_dataset.take(num_validation_samples) +#excluding validation data from grouping and defining as training data +train_dataset = train_dataset.skip(num_validation_samples) + +#setting batch size +BATCH_SIZE = 128 +train_dataset = train_dataset.batch(BATCH_SIZE) +validation_dataset = validation_dataset.batch(num_validation_samples) +test_dataset = test_dataset.batch(num_test_samples) + +#separating validation data into inputs and targets +validation_inputs, validation_targets = next(iter(validation_dataset)) + +input_size = 784 +output_size = 49 +standard_hidden_layer_width = 512 +NUM_EPOCHS = 100 +VALIDATION_STEPS = num_validation_samples // BATCH_SIZE +early_stopping = tf.keras.callbacks.EarlyStopping(patience=2) +reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001) + +model = tf.keras.Sequential([ + tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)), + tf.keras.layers.MaxPooling2D(2, 2), + tf.keras.layers.Conv2D(64, (3,3), activation='relu'), + tf.keras.layers.MaxPooling2D(2,2), + tf.keras.layers.Dropout(0.25), + tf.keras.layers.Flatten(), + tf.keras.layers.Dense(standard_hidden_layer_width, activation='relu'), + tf.keras.layers.Dense(output_size, activation='softmax') + ]) + +model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) + +model.fit(train_dataset, epochs=NUM_EPOCHS, callbacks=[reduce_lr], validation_data=(validation_inputs, validation_targets), validation_steps=VALIDATION_STEPS, verbose=1) + + +predictions = model.predict([test_data]) + +balanced_accuracy(test_labels, predictions, output_size) From a611f418e7c910497e3cace5211a753c480ba5a5 Mon Sep 17 00:00:00 2001 From: VicHofs <57238498+VicHofs@users.noreply.github.com> Date: Mon, 3 Feb 2020 17:46:11 -0300 Subject: [PATCH 02/12] Notebook format Original Jupyter Notebook with outputs and benchmarks. --- k49 conv-net.ipynb | 1469 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1469 insertions(+) create mode 100644 k49 conv-net.ipynb diff --git a/k49 conv-net.ipynb b/k49 conv-net.ipynb new file mode 100644 index 0000000..0be46c3 --- /dev/null +++ b/k49 conv-net.ipynb @@ -0,0 +1,1469 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "train_data = np.load('k49-train-imgs.npz')['arr_0']\n", + "train_labels = np.load('k49-train-labels.npz')['arr_0']\n", + "test_data = np.load('k49-test-imgs.npz')['arr_0']\n", + "test_labels = np.load('k49-test-labels.npz')['arr_0']\n", + "\n", + "train_data1 = tf.keras.utils.normalize(train_data, axis=1)\n", + "test_data1 = tf.keras.utils.normalize(test_data, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "for i in range(25):\n", + " plt.subplot(5,5,i+1)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.grid(False)\n", + " plt.imshow(train_data[i], cmap=plt.cm.binary)\n", + " plt.xlabel(train_labels[i])\n", + "train_data = train_data1.reshape((train_data.shape[0], train_data.shape[1], train_data.shape[2], 1))\n", + "test_data = test_data1.reshape((test_data.shape[0], test_data.shape[1], test_data.shape[2], 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_labels))\n", + "test_dataset = tf.data.Dataset.from_tensor_slices((test_data, test_labels))\n", + "\n", + "#outlining number of validation samples (picked 8% of training data)\n", + "num_validation_samples = 0.08 * train_data.shape[0]\n", + "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n", + "\n", + "#isolating test samples\n", + "num_test_samples = test_data.shape[0]\n", + "num_test_samples = tf.cast(num_test_samples, tf.int64)\n", + "\n", + "#setting buffer size and shuffling training data\n", + "BUFFER_SIZE = 10000\n", + "train_dataset = train_dataset.shuffle(BUFFER_SIZE)\n", + "#extracting validation data from grouping into own var\n", + "validation_dataset = train_dataset.take(num_validation_samples)\n", + "#excluding validation data from grouping and defining as training data\n", + "train_dataset = train_dataset.skip(num_validation_samples)\n", + "\n", + "#setting batch size\n", + "BATCH_SIZE = 128\n", + "train_dataset = train_dataset.batch(BATCH_SIZE)\n", + "validation_dataset = validation_dataset.batch(num_validation_samples)\n", + "test_dataset = test_dataset.batch(num_test_samples)\n", + "\n", + "#separating validation data into inputs and targets\n", + "validation_inputs, validation_targets = next(iter(validation_dataset))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 1671 steps, validate on 18589 samples\n", + "Epoch 1/100\n", + "1671/1671 [==============================] - 163s 98ms/step - loss: 0.6321 - accuracy: 0.8310 - val_loss: 0.2672 - val_accuracy: 0.9257\n", + "Epoch 2/100\n", + "1671/1671 [==============================] - 162s 97ms/step - loss: 0.2518 - accuracy: 0.9290 - val_loss: 0.1946 - val_accuracy: 0.9454\n", + "Epoch 3/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1820 - accuracy: 0.9475 - val_loss: 0.1655 - val_accuracy: 0.9529\n", + "Epoch 4/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1433 - accuracy: 0.9578 - val_loss: 0.1397 - val_accuracy: 0.9599\n", + "Epoch 5/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1160 - accuracy: 0.9649 - val_loss: 0.1258 - val_accuracy: 0.9637\n", + "Epoch 6/100\n", + "1671/1671 [==============================] - 167s 100ms/step - loss: 0.0977 - accuracy: 0.9698 - val_loss: 0.1251 - val_accuracy: 0.9636\n", + "Epoch 7/100\n", + "1671/1671 [==============================] - 163s 98ms/step - loss: 0.0840 - accuracy: 0.9736 - val_loss: 0.1134 - val_accuracy: 0.9694\n", + "Epoch 8/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0739 - accuracy: 0.9762 - val_loss: 0.1036 - val_accuracy: 0.9719\n", + "Epoch 9/100\n", + "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0662 - accuracy: 0.9786 - val_loss: 0.1062 - val_accuracy: 0.9706\n", + "Epoch 10/100\n", + "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0590 - accuracy: 0.9806 - val_loss: 0.0992 - val_accuracy: 0.9732\n", + "Epoch 11/100\n", + "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0559 - accuracy: 0.9817 - val_loss: 0.0910 - val_accuracy: 0.9750\n", + "Epoch 12/100\n", + "1671/1671 [==============================] - 178s 107ms/step - loss: 0.0497 - accuracy: 0.9837 - val_loss: 0.0926 - val_accuracy: 0.9739\n", + "Epoch 13/100\n", + "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0479 - accuracy: 0.9842 - val_loss: 0.0907 - val_accuracy: 0.9767\n", + "Epoch 14/100\n", + "1671/1671 [==============================] - 220s 131ms/step - loss: 0.0434 - accuracy: 0.9851 - val_loss: 0.0873 - val_accuracy: 0.9770\n", + "Epoch 15/100\n", + "1671/1671 [==============================] - 212s 127ms/step - loss: 0.0437 - accuracy: 0.9854 - val_loss: 0.0795 - val_accuracy: 0.9779\n", + "Epoch 16/100\n", + "1671/1671 [==============================] - 210s 126ms/step - loss: 0.0381 - accuracy: 0.9870 - val_loss: 0.0751 - val_accuracy: 0.9796\n", + "Epoch 17/100\n", + "1671/1671 [==============================] - 206s 123ms/step - loss: 0.0385 - accuracy: 0.9869 - val_loss: 0.0711 - val_accuracy: 0.9803\n", + "Epoch 18/100\n", + "1671/1671 [==============================] - 196s 117ms/step - loss: 0.0353 - accuracy: 0.9881 - val_loss: 0.0701 - val_accuracy: 0.9817\n", + "Epoch 19/100\n", + "1671/1671 [==============================] - 178s 107ms/step - loss: 0.0348 - accuracy: 0.9883 - val_loss: 0.0684 - val_accuracy: 0.9832\n", + "Epoch 20/100\n", + "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0338 - accuracy: 0.9887 - val_loss: 0.0635 - val_accuracy: 0.9832\n", + "Epoch 21/100\n", + "1671/1671 [==============================] - 169s 101ms/step - loss: 0.0310 - accuracy: 0.9896 - val_loss: 0.0631 - val_accuracy: 0.9836\n", + "Epoch 22/100\n", + "1671/1671 [==============================] - 170s 102ms/step - loss: 0.0312 - accuracy: 0.9894 - val_loss: 0.0626 - val_accuracy: 0.9842\n", + "Epoch 23/100\n", + "1671/1671 [==============================] - 171s 103ms/step - loss: 0.0298 - accuracy: 0.9898 - val_loss: 0.0580 - val_accuracy: 0.9847\n", + "Epoch 24/100\n", + "1671/1671 [==============================] - 186s 111ms/step - loss: 0.0281 - accuracy: 0.9905 - val_loss: 0.0631 - val_accuracy: 0.9835\n", + "Epoch 25/100\n", + "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0280 - accuracy: 0.9904 - val_loss: 0.0582 - val_accuracy: 0.9835\n", + "Epoch 26/100\n", + "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0268 - accuracy: 0.9908 - val_loss: 0.0586 - val_accuracy: 0.9855\n", + "Epoch 27/100\n", + "1671/1671 [==============================] - 124s 74ms/step - loss: 0.0260 - accuracy: 0.9912 - val_loss: 0.0525 - val_accuracy: 0.9864\n", + "Epoch 28/100\n", + "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0251 - accuracy: 0.9916 - val_loss: 0.0531 - val_accuracy: 0.9862\n", + "Epoch 29/100\n", + "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0248 - accuracy: 0.9915 - val_loss: 0.0533 - val_accuracy: 0.9860\n", + "Epoch 30/100\n", + "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0242 - accuracy: 0.9917 - val_loss: 0.0478 - val_accuracy: 0.9876\n", + "Epoch 31/100\n", + "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0234 - accuracy: 0.9919 - val_loss: 0.0474 - val_accuracy: 0.9871\n", + "Epoch 32/100\n", + "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0228 - accuracy: 0.9923 - val_loss: 0.0497 - val_accuracy: 0.9869\n", + "Epoch 33/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0229 - accuracy: 0.9924 - val_loss: 0.0514 - val_accuracy: 0.9869\n", + "Epoch 34/100\n", + "1671/1671 [==============================] - 128s 77ms/step - loss: 0.0216 - accuracy: 0.9929 - val_loss: 0.0472 - val_accuracy: 0.9877\n", + "Epoch 35/100\n", + "1671/1671 [==============================] - 130s 78ms/step - loss: 0.0220 - accuracy: 0.9924 - val_loss: 0.0476 - val_accuracy: 0.9879\n", + "Epoch 36/100\n", + "1671/1671 [==============================] - 131s 78ms/step - loss: 0.0212 - accuracy: 0.9931 - val_loss: 0.0448 - val_accuracy: 0.9877\n", + "Epoch 37/100\n", + "1671/1671 [==============================] - 129s 77ms/step - loss: 0.0210 - accuracy: 0.9930 - val_loss: 0.0384 - val_accuracy: 0.9895\n", + "Epoch 38/100\n", + "1671/1671 [==============================] - 131s 79ms/step - loss: 0.0200 - accuracy: 0.9933 - val_loss: 0.0380 - val_accuracy: 0.9892\n", + "Epoch 39/100\n", + "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0199 - accuracy: 0.9932 - val_loss: 0.0388 - val_accuracy: 0.9885\n", + "Epoch 40/100\n", + "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0205 - accuracy: 0.9932 - val_loss: 0.0357 - val_accuracy: 0.9898\n", + "Epoch 41/100\n", + "1671/1671 [==============================] - 128s 77ms/step - loss: 0.0191 - accuracy: 0.9936 - val_loss: 0.0352 - val_accuracy: 0.9902\n", + "Epoch 42/100\n", + "1671/1671 [==============================] - 146s 87ms/step - loss: 0.0183 - accuracy: 0.9939 - val_loss: 0.0365 - val_accuracy: 0.9898\n", + "Epoch 43/100\n", + "1671/1671 [==============================] - 149s 89ms/step - loss: 0.0201 - accuracy: 0.9933 - val_loss: 0.0338 - val_accuracy: 0.9904\n", + "Epoch 44/100\n", + "1671/1671 [==============================] - 141s 85ms/step - loss: 0.0182 - accuracy: 0.9937 - val_loss: 0.0335 - val_accuracy: 0.9906\n", + "Epoch 45/100\n", + "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0173 - accuracy: 0.9941 - val_loss: 0.0309 - val_accuracy: 0.9911\n", + "Epoch 46/100\n", + "1671/1671 [==============================] - 129s 77ms/step - loss: 0.0190 - accuracy: 0.9935 - val_loss: 0.0329 - val_accuracy: 0.9910\n", + "Epoch 47/100\n", + "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0176 - accuracy: 0.9943 - val_loss: 0.0324 - val_accuracy: 0.9908\n", + "Epoch 48/100\n", + "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0178 - accuracy: 0.9940 - val_loss: 0.0311 - val_accuracy: 0.9909\n", + "Epoch 49/100\n", + "1671/1671 [==============================] - 126s 76ms/step - loss: 0.0176 - accuracy: 0.9942 - val_loss: 0.0323 - val_accuracy: 0.9907\n", + "Epoch 50/100\n", + "1671/1671 [==============================] - 118s 71ms/step - loss: 0.0159 - accuracy: 0.9948 - val_loss: 0.0258 - val_accuracy: 0.9927\n", + "Epoch 51/100\n", + "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0172 - accuracy: 0.9943 - val_loss: 0.0268 - val_accuracy: 0.9925\n", + "Epoch 52/100\n", + "1671/1671 [==============================] - 118s 70ms/step - loss: 0.0166 - accuracy: 0.9943 - val_loss: 0.0255 - val_accuracy: 0.9932\n", + "Epoch 53/100\n", + "1671/1671 [==============================] - 133s 80ms/step - loss: 0.0164 - accuracy: 0.9946 - val_loss: 0.0237 - val_accuracy: 0.9935\n", + "Epoch 54/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0163 - accuracy: 0.9946 - val_loss: 0.0234 - val_accuracy: 0.9930\n", + "Epoch 55/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1671/1671 [==============================] - 118s 71ms/step - loss: 0.0157 - accuracy: 0.9947 - val_loss: 0.0242 - val_accuracy: 0.9934\n", + "Epoch 56/100\n", + "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0160 - accuracy: 0.9948 - val_loss: 0.0226 - val_accuracy: 0.9930\n", + "Epoch 57/100\n", + "1671/1671 [==============================] - 121s 72ms/step - loss: 0.0155 - accuracy: 0.9948 - val_loss: 0.0251 - val_accuracy: 0.9926\n", + "Epoch 58/100\n", + "1671/1671 [==============================] - 123s 73ms/step - loss: 0.0162 - accuracy: 0.9946 - val_loss: 0.0238 - val_accuracy: 0.9930\n", + "Epoch 59/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0152 - accuracy: 0.9950 - val_loss: 0.0226 - val_accuracy: 0.9933\n", + "Epoch 60/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0158 - accuracy: 0.9947 - val_loss: 0.0282 - val_accuracy: 0.9920\n", + "Epoch 61/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0151 - accuracy: 0.9950 - val_loss: 0.0232 - val_accuracy: 0.9933\n", + "Epoch 62/100\n", + "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0146 - accuracy: 0.9952 - val_loss: 0.0264 - val_accuracy: 0.9925\n", + "Epoch 63/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0146 - accuracy: 0.9953 - val_loss: 0.0201 - val_accuracy: 0.9938\n", + "Epoch 64/100\n", + "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0146 - accuracy: 0.9953 - val_loss: 0.0248 - val_accuracy: 0.9925\n", + "Epoch 65/100\n", + "1671/1671 [==============================] - 124s 74ms/step - loss: 0.0144 - accuracy: 0.9953 - val_loss: 0.0204 - val_accuracy: 0.9937\n", + "Epoch 66/100\n", + "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0136 - accuracy: 0.9955 - val_loss: 0.0198 - val_accuracy: 0.9940\n", + "Epoch 67/100\n", + "1671/1671 [==============================] - 132s 79ms/step - loss: 0.0138 - accuracy: 0.9953 - val_loss: 0.0227 - val_accuracy: 0.9927\n", + "Epoch 68/100\n", + "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0141 - accuracy: 0.9955 - val_loss: 0.0198 - val_accuracy: 0.9941\n", + "Epoch 69/100\n", + "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0138 - accuracy: 0.9955 - val_loss: 0.0222 - val_accuracy: 0.9937\n", + "Epoch 70/100\n", + "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0139 - accuracy: 0.9956 - val_loss: 0.0169 - val_accuracy: 0.9950\n", + "Epoch 71/100\n", + "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0132 - accuracy: 0.9957 - val_loss: 0.0195 - val_accuracy: 0.9946\n", + "Epoch 72/100\n", + "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0136 - accuracy: 0.9955 - val_loss: 0.0211 - val_accuracy: 0.9939\n", + "Epoch 73/100\n", + "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0130 - accuracy: 0.9958 - val_loss: 0.0219 - val_accuracy: 0.9937\n", + "Epoch 74/100\n", + "1671/1671 [==============================] - 141s 84ms/step - loss: 0.0137 - accuracy: 0.9957 - val_loss: 0.0177 - val_accuracy: 0.9939\n", + "Epoch 75/100\n", + "1671/1671 [==============================] - 175s 105ms/step - loss: 0.0135 - accuracy: 0.9957 - val_loss: 0.0194 - val_accuracy: 0.9945: 0.99\n", + "Epoch 76/100\n", + "1671/1671 [==============================] - 148s 88ms/step - loss: 0.0128 - accuracy: 0.9958 - val_loss: 0.0214 - val_accuracy: 0.9938\n", + "Epoch 77/100\n", + "1671/1671 [==============================] - 151s 90ms/step - loss: 0.0118 - accuracy: 0.9962 - val_loss: 0.0178 - val_accuracy: 0.9944\n", + "Epoch 78/100\n", + "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0132 - accuracy: 0.9958 - val_loss: 0.0202 - val_accuracy: 0.9943\n", + "Epoch 79/100\n", + "1671/1671 [==============================] - 189s 113ms/step - loss: 0.0130 - accuracy: 0.9957 - val_loss: 0.0182 - val_accuracy: 0.9941\n", + "Epoch 80/100\n", + "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0129 - accuracy: 0.9958 - val_loss: 0.0181 - val_accuracy: 0.9945\n", + "Epoch 81/100\n", + "1671/1671 [==============================] - 159s 95ms/step - loss: 0.0127 - accuracy: 0.9959 - val_loss: 0.0157 - val_accuracy: 0.9955\n", + "Epoch 82/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0121 - accuracy: 0.9961 - val_loss: 0.0184 - val_accuracy: 0.9948\n", + "Epoch 83/100\n", + "1671/1671 [==============================] - 163s 98ms/step - loss: 0.0123 - accuracy: 0.9961 - val_loss: 0.0163 - val_accuracy: 0.9954\n", + "Epoch 84/100\n", + "1671/1671 [==============================] - 175s 105ms/step - loss: 0.0125 - accuracy: 0.9960 - val_loss: 0.0188 - val_accuracy: 0.9948\n", + "Epoch 85/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0125 - accuracy: 0.9961 - val_loss: 0.0152 - val_accuracy: 0.9952\n", + "Epoch 86/100\n", + "1671/1671 [==============================] - 160s 96ms/step - loss: 0.0130 - accuracy: 0.9958 - val_loss: 0.0161 - val_accuracy: 0.9953\n", + "Epoch 87/100\n", + "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0120 - accuracy: 0.9961 - val_loss: 0.0168 - val_accuracy: 0.9956\n", + "Epoch 88/100\n", + "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0120 - accuracy: 0.9960 - val_loss: 0.0169 - val_accuracy: 0.9949\n", + "Epoch 89/100\n", + "1671/1671 [==============================] - 133s 80ms/step - loss: 0.0129 - accuracy: 0.9959 - val_loss: 0.0155 - val_accuracy: 0.9955\n", + "Epoch 90/100\n", + "1671/1671 [==============================] - 145s 87ms/step - loss: 0.0117 - accuracy: 0.9962 - val_loss: 0.0147 - val_accuracy: 0.9956\n", + "Epoch 91/100\n", + "1671/1671 [==============================] - 143s 85ms/step - loss: 0.0115 - accuracy: 0.9964 - val_loss: 0.0143 - val_accuracy: 0.9956\n", + "Epoch 92/100\n", + "1671/1671 [==============================] - 142s 85ms/step - loss: 0.0116 - accuracy: 0.9964 - val_loss: 0.0192 - val_accuracy: 0.9943\n", + "Epoch 93/100\n", + "1671/1671 [==============================] - 137s 82ms/step - loss: 0.0117 - accuracy: 0.9963 - val_loss: 0.0156 - val_accuracy: 0.9952\n", + "Epoch 94/100\n", + "1671/1671 [==============================] - 131s 78ms/step - loss: 0.0118 - accuracy: 0.9963 - val_loss: 0.0137 - val_accuracy: 0.9955\n", + "Epoch 95/100\n", + "1671/1671 [==============================] - 142s 85ms/step - loss: 0.0115 - accuracy: 0.9962 - val_loss: 0.0123 - val_accuracy: 0.9961\n", + "Epoch 96/100\n", + "1671/1671 [==============================] - 145s 87ms/step - loss: 0.0115 - accuracy: 0.9964 - val_loss: 0.0134 - val_accuracy: 0.9958\n", + "Epoch 97/100\n", + "1671/1671 [==============================] - 141s 85ms/step - loss: 0.0109 - accuracy: 0.9965 - val_loss: 0.0136 - val_accuracy: 0.9955\n", + "Epoch 98/100\n", + "1671/1671 [==============================] - 143s 86ms/step - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.0133 - val_accuracy: 0.9960\n", + "Epoch 99/100\n", + "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0121 - accuracy: 0.9960 - val_loss: 0.0147 - val_accuracy: 0.9954\n", + "Epoch 100/100\n", + "1671/1671 [==============================] - 139s 83ms/step - loss: 0.0111 - accuracy: 0.9966 - val_loss: 0.0159 - val_accuracy: 0.9952\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_size = 784\n", + "output_size = 49\n", + "standard_hidden_layer_width = 512\n", + "NUM_EPOCHS = 100\n", + "VALIDATION_STEPS = num_validation_samples // BATCH_SIZE\n", + "early_stopping = tf.keras.callbacks.EarlyStopping(patience=2)\n", + "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),\n", + " tf.keras.layers.MaxPooling2D(2, 2),\n", + " tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n", + " tf.keras.layers.MaxPooling2D(2,2),\n", + " tf.keras.layers.Dropout(0.25),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(standard_hidden_layer_width, activation='relu'),\n", + " tf.keras.layers.Dense(output_size, activation='softmax')\n", + " ])\n", + "\n", + "model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "model.fit(train_dataset, epochs=NUM_EPOCHS, callbacks=[reduce_lr], validation_data=(validation_inputs, validation_targets), validation_steps=VALIDATION_STEPS, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From C:\\Users\\victo\\Anaconda3\\envs\\py3-TF2.0\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "If using Keras pass *_constraint arguments to layers.\n", + "INFO:tensorflow:Assets written to: k49-convnet\\assets\n" + ] + } + ], + "source": [ + "model.save('k49-convnet')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "complete_model = tf.keras.models.load_model('k49-convnet')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = complete_model.predict([test_data])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.1654630e-22, 5.9298899e-24, 1.2344980e-27, ..., 3.2112436e-19,\n", + " 6.4444245e-17, 1.7431504e-16],\n", + " [1.9990182e-06, 6.2519321e-24, 6.3204732e-22, ..., 2.7016511e-09,\n", + " 8.3437172e-20, 2.5418602e-25],\n", + " [9.8383986e-22, 1.2532079e-32, 2.7175545e-23, ..., 5.8478419e-34,\n", + " 1.5754820e-33, 9.4036669e-23],\n", + " ...,\n", + " [1.4244270e-18, 1.0000000e+00, 1.5626800e-25, ..., 0.0000000e+00,\n", + " 3.8913057e-24, 1.7517804e-19],\n", + " [1.5374325e-17, 1.8800380e-12, 6.4201010e-27, ..., 3.5142543e-17,\n", + " 4.8406890e-11, 1.0647082e-18],\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 8.1922249e-34,\n", + " 1.0000000e+00, 1.0231144e-35]], dtype=float32)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ひ\n" + ] + } + ], + "source": [ + "map = ['あ','い','う','え','お','か','き','く','け','こ','さ','し','す','せ','そ','た','ち','つ','て','と','な','に','ぬ','ね','の','は','ひ','ふ','へ','ほ','ま','み','む','め','も','や','ゆ','よ','ら','り','る','れ','ろ','わ','ゐ','ゑ','を','ん','ゝ']\n", + "\n", + "plt.imshow(test_data1[38546], cmap=plt.cm.binary_r)\n", + "plt.show()\n", + "print(map[np.argmax(predictions[38546])])\n", + "\n", + "for i in range(50):\n", + " plt.imshow(test_data1[i], cmap=plt.cm.binary_r)\n", + " plt.show()\n", + " print(map[np.argmax(predictions[i])])" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1000, 1000, 1000, 126, 1000, 1000, 1000, 1000, 767, 1000, 1000, 1000, 1000, 678, 629, 1000, 418, 1000, 1000, 1000, 1000, 1000, 336, 399, 1000, 1000, 836, 1000, 1000, 324, 1000, 498, 280, 552, 1000, 1000, 260, 1000, 1000, 1000, 1000, 1000, 348, 390, 68, 64, 1000, 1000, 574]\n", + "[960, 970, 971, 113, 957, 889, 931, 929, 722, 926, 954, 921, 909, 593, 562, 963, 405, 959, 933, 945, 909, 941, 315, 364, 934, 931, 799, 910, 945, 294, 967, 466, 249, 528, 970, 948, 246, 980, 918, 927, 928, 968, 331, 354, 50, 58, 964, 978, 474]\n", + "0.930142277358246\n" + ] + } + ], + "source": [ + "totals = []\n", + "for cls in range(49):\n", + " total = 0\n", + " for i in test_labels:\n", + " if i == cls:\n", + " total = total + 1\n", + " totals.append(total)\n", + "\n", + "hits = []\n", + "for cls in range(49):\n", + " total_hits = 0\n", + " for i in range(0,test_labels.shape[0]):\n", + " if test_labels[i] == cls == np.argmax(predictions[i]):\n", + " total_hits = total_hits + 1\n", + " hits.append(total_hits)\n", + " \n", + "accuracy_list = []\n", + "for i in range(0,len(hits)):\n", + " accuracy = hits[i]/totals[i]\n", + " accuracy_list.append(accuracy)\n", + "\n", + "print(np.mean(accuracy_list))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:py3-TF2.0]", + "language": "python", + "name": "conda-env-py3-TF2.0-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From fd3d574267b3664edc1ca8d3f7d326fa928d75ac Mon Sep 17 00:00:00 2001 From: VicHofs <57238498+VicHofs@users.noreply.github.com> Date: Mon, 3 Feb 2020 17:47:11 -0300 Subject: [PATCH 03/12] conv-net TensorFlow model full code Full code not in Notebook format --- conv-net tensorflow model 93% => conv-net tensorflow model | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename conv-net tensorflow model 93% => conv-net tensorflow model (100%) diff --git a/conv-net tensorflow model 93% b/conv-net tensorflow model similarity index 100% rename from conv-net tensorflow model 93% rename to conv-net tensorflow model From 94877d72887f15005085255848b47e546afa7e98 Mon Sep 17 00:00:00 2001 From: VicHofs <57238498+VicHofs@users.noreply.github.com> Date: Mon, 3 Feb 2020 18:02:12 -0300 Subject: [PATCH 04/12] Update k49 conv-net.ipynb --- k49 conv-net.ipynb | 1185 +------------------------------------------- 1 file changed, 4 insertions(+), 1181 deletions(-) diff --git a/k49 conv-net.ipynb b/k49 conv-net.ipynb index 0be46c3..156de45 100644 --- a/k49 conv-net.ipynb +++ b/k49 conv-net.ipynb @@ -97,209 +97,7 @@ "Train for 1671 steps, validate on 18589 samples\n", "Epoch 1/100\n", "1671/1671 [==============================] - 163s 98ms/step - loss: 0.6321 - accuracy: 0.8310 - val_loss: 0.2672 - val_accuracy: 0.9257\n", - "Epoch 2/100\n", - "1671/1671 [==============================] - 162s 97ms/step - loss: 0.2518 - accuracy: 0.9290 - val_loss: 0.1946 - val_accuracy: 0.9454\n", - "Epoch 3/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1820 - accuracy: 0.9475 - val_loss: 0.1655 - val_accuracy: 0.9529\n", - "Epoch 4/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1433 - accuracy: 0.9578 - val_loss: 0.1397 - val_accuracy: 0.9599\n", - "Epoch 5/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1160 - accuracy: 0.9649 - val_loss: 0.1258 - val_accuracy: 0.9637\n", - "Epoch 6/100\n", - "1671/1671 [==============================] - 167s 100ms/step - loss: 0.0977 - accuracy: 0.9698 - val_loss: 0.1251 - val_accuracy: 0.9636\n", - "Epoch 7/100\n", - "1671/1671 [==============================] - 163s 98ms/step - loss: 0.0840 - accuracy: 0.9736 - val_loss: 0.1134 - val_accuracy: 0.9694\n", - "Epoch 8/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0739 - accuracy: 0.9762 - val_loss: 0.1036 - val_accuracy: 0.9719\n", - "Epoch 9/100\n", - "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0662 - accuracy: 0.9786 - val_loss: 0.1062 - val_accuracy: 0.9706\n", - "Epoch 10/100\n", - "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0590 - accuracy: 0.9806 - val_loss: 0.0992 - val_accuracy: 0.9732\n", - "Epoch 11/100\n", - "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0559 - accuracy: 0.9817 - val_loss: 0.0910 - val_accuracy: 0.9750\n", - "Epoch 12/100\n", - "1671/1671 [==============================] - 178s 107ms/step - loss: 0.0497 - accuracy: 0.9837 - val_loss: 0.0926 - val_accuracy: 0.9739\n", - "Epoch 13/100\n", - "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0479 - accuracy: 0.9842 - val_loss: 0.0907 - val_accuracy: 0.9767\n", - "Epoch 14/100\n", - "1671/1671 [==============================] - 220s 131ms/step - loss: 0.0434 - accuracy: 0.9851 - val_loss: 0.0873 - val_accuracy: 0.9770\n", - "Epoch 15/100\n", - "1671/1671 [==============================] - 212s 127ms/step - loss: 0.0437 - accuracy: 0.9854 - val_loss: 0.0795 - val_accuracy: 0.9779\n", - "Epoch 16/100\n", - "1671/1671 [==============================] - 210s 126ms/step - loss: 0.0381 - accuracy: 0.9870 - val_loss: 0.0751 - val_accuracy: 0.9796\n", - "Epoch 17/100\n", - "1671/1671 [==============================] - 206s 123ms/step - loss: 0.0385 - accuracy: 0.9869 - val_loss: 0.0711 - val_accuracy: 0.9803\n", - "Epoch 18/100\n", - "1671/1671 [==============================] - 196s 117ms/step - loss: 0.0353 - accuracy: 0.9881 - val_loss: 0.0701 - val_accuracy: 0.9817\n", - "Epoch 19/100\n", - "1671/1671 [==============================] - 178s 107ms/step - loss: 0.0348 - accuracy: 0.9883 - val_loss: 0.0684 - val_accuracy: 0.9832\n", - "Epoch 20/100\n", - "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0338 - accuracy: 0.9887 - val_loss: 0.0635 - val_accuracy: 0.9832\n", - "Epoch 21/100\n", - "1671/1671 [==============================] - 169s 101ms/step - loss: 0.0310 - accuracy: 0.9896 - val_loss: 0.0631 - val_accuracy: 0.9836\n", - "Epoch 22/100\n", - "1671/1671 [==============================] - 170s 102ms/step - loss: 0.0312 - accuracy: 0.9894 - val_loss: 0.0626 - val_accuracy: 0.9842\n", - "Epoch 23/100\n", - "1671/1671 [==============================] - 171s 103ms/step - loss: 0.0298 - accuracy: 0.9898 - val_loss: 0.0580 - val_accuracy: 0.9847\n", - "Epoch 24/100\n", - "1671/1671 [==============================] - 186s 111ms/step - loss: 0.0281 - accuracy: 0.9905 - val_loss: 0.0631 - val_accuracy: 0.9835\n", - "Epoch 25/100\n", - "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0280 - accuracy: 0.9904 - val_loss: 0.0582 - val_accuracy: 0.9835\n", - "Epoch 26/100\n", - "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0268 - accuracy: 0.9908 - val_loss: 0.0586 - val_accuracy: 0.9855\n", - "Epoch 27/100\n", - "1671/1671 [==============================] - 124s 74ms/step - loss: 0.0260 - accuracy: 0.9912 - val_loss: 0.0525 - val_accuracy: 0.9864\n", - "Epoch 28/100\n", - "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0251 - accuracy: 0.9916 - val_loss: 0.0531 - val_accuracy: 0.9862\n", - "Epoch 29/100\n", - "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0248 - accuracy: 0.9915 - val_loss: 0.0533 - val_accuracy: 0.9860\n", - "Epoch 30/100\n", - "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0242 - accuracy: 0.9917 - val_loss: 0.0478 - val_accuracy: 0.9876\n", - "Epoch 31/100\n", - "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0234 - accuracy: 0.9919 - val_loss: 0.0474 - val_accuracy: 0.9871\n", - "Epoch 32/100\n", - "1671/1671 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[==============================] - 141s 85ms/step - loss: 0.0182 - accuracy: 0.9937 - val_loss: 0.0335 - val_accuracy: 0.9906\n", - "Epoch 45/100\n", - "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0173 - accuracy: 0.9941 - val_loss: 0.0309 - val_accuracy: 0.9911\n", - "Epoch 46/100\n", - "1671/1671 [==============================] - 129s 77ms/step - loss: 0.0190 - accuracy: 0.9935 - val_loss: 0.0329 - val_accuracy: 0.9910\n", - "Epoch 47/100\n", - "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0176 - accuracy: 0.9943 - val_loss: 0.0324 - val_accuracy: 0.9908\n", - "Epoch 48/100\n", - "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0178 - accuracy: 0.9940 - val_loss: 0.0311 - val_accuracy: 0.9909\n", - "Epoch 49/100\n", - "1671/1671 [==============================] - 126s 76ms/step - loss: 0.0176 - accuracy: 0.9942 - val_loss: 0.0323 - val_accuracy: 0.9907\n", - "Epoch 50/100\n", - "1671/1671 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[==============================] - 142s 85ms/step - loss: 0.0116 - accuracy: 0.9964 - val_loss: 0.0192 - val_accuracy: 0.9943\n", - "Epoch 93/100\n", - "1671/1671 [==============================] - 137s 82ms/step - loss: 0.0117 - accuracy: 0.9963 - val_loss: 0.0156 - val_accuracy: 0.9952\n", - "Epoch 94/100\n", - "1671/1671 [==============================] - 131s 78ms/step - loss: 0.0118 - accuracy: 0.9963 - val_loss: 0.0137 - val_accuracy: 0.9955\n", - "Epoch 95/100\n", - "1671/1671 [==============================] - 142s 85ms/step - loss: 0.0115 - accuracy: 0.9962 - val_loss: 0.0123 - val_accuracy: 0.9961\n", - "Epoch 96/100\n", - "1671/1671 [==============================] - 145s 87ms/step - loss: 0.0115 - accuracy: 0.9964 - val_loss: 0.0134 - val_accuracy: 0.9958\n", - "Epoch 97/100\n", - "1671/1671 [==============================] - 141s 85ms/step - loss: 0.0109 - accuracy: 0.9965 - val_loss: 0.0136 - val_accuracy: 0.9955\n", - "Epoch 98/100\n", - "1671/1671 [==============================] - 143s 86ms/step - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.0133 - val_accuracy: 0.9960\n", - "Epoch 99/100\n", - "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0121 - accuracy: 0.9960 - val_loss: 0.0147 - val_accuracy: 0.9954\n", - "Epoch 100/100\n", + "Epoch 100/100\n", "1671/1671 [==============================] - 139s 83ms/step - loss: 0.0111 - accuracy: 0.9966 - val_loss: 0.0159 - val_accuracy: 0.9952\n" ] }, @@ -409,982 +207,7 @@ "predictions" ] }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ひ\n" - ] - } - ], - "source": [ + "source": [ "map = ['あ','い','う','え','お','か','き','く','け','こ','さ','し','す','せ','そ','た','ち','つ','て','と','な','に','ぬ','ね','の','は','ひ','ふ','へ','ほ','ま','み','む','め','も','や','ゆ','よ','ら','り','る','れ','ろ','わ','ゐ','ゑ','を','ん','ゝ']\n", "\n", "plt.imshow(test_data1[38546], cmap=plt.cm.binary_r)\n", @@ -1408,7 +231,7 @@ "text": [ "[1000, 1000, 1000, 126, 1000, 1000, 1000, 1000, 767, 1000, 1000, 1000, 1000, 678, 629, 1000, 418, 1000, 1000, 1000, 1000, 1000, 336, 399, 1000, 1000, 836, 1000, 1000, 324, 1000, 498, 280, 552, 1000, 1000, 260, 1000, 1000, 1000, 1000, 1000, 348, 390, 68, 64, 1000, 1000, 574]\n", "[960, 970, 971, 113, 957, 889, 931, 929, 722, 926, 954, 921, 909, 593, 562, 963, 405, 959, 933, 945, 909, 941, 315, 364, 934, 931, 799, 910, 945, 294, 967, 466, 249, 528, 970, 948, 246, 980, 918, 927, 928, 968, 331, 354, 50, 58, 964, 978, 474]\n", - "0.930142277358246\n" + "The Balanced Accuracy is: 0.930142277358246\n" ] } ], @@ -1434,7 +257,7 @@ " accuracy = hits[i]/totals[i]\n", " accuracy_list.append(accuracy)\n", "\n", - "print(np.mean(accuracy_list))" + "print(f'The balanced accuracy is: {np.mean(accuracy_list)}')" ] }, { From c19b6375ca59d35ae9be026056e6745e3baacae1 Mon Sep 17 00:00:00 2001 From: VicHofs <57238498+VicHofs@users.noreply.github.com> Date: Mon, 3 Feb 2020 18:08:27 -0300 Subject: [PATCH 05/12] Add notebook format Jupyter Notebook format of code --- k49 cnn notebook.ipynb | 438 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 438 insertions(+) create mode 100644 k49 cnn notebook.ipynb diff --git a/k49 cnn notebook.ipynb b/k49 cnn notebook.ipynb new file mode 100644 index 0000000..dea16a7 --- /dev/null +++ b/k49 cnn notebook.ipynb @@ -0,0 +1,438 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "train_data = np.load('k49-train-imgs.npz')['arr_0']\n", + "train_labels = np.load('k49-train-labels.npz')['arr_0']\n", + "test_data = np.load('k49-test-imgs.npz')['arr_0']\n", + "test_labels = np.load('k49-test-labels.npz')['arr_0']\n", + "\n", + "train_data1 = tf.keras.utils.normalize(train_data, axis=1)\n", + "test_data1 = tf.keras.utils.normalize(test_data, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "for i in range(25):\n", + " plt.subplot(5,5,i+1)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.grid(False)\n", + " plt.imshow(train_data[i], cmap=plt.cm.binary)\n", + " plt.xlabel(train_labels[i])\n", + "train_data = train_data1.reshape((train_data.shape[0], train_data.shape[1], train_data.shape[2], 1))\n", + "test_data = test_data1.reshape((test_data.shape[0], test_data.shape[1], test_data.shape[2], 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_labels))\n", + "test_dataset = tf.data.Dataset.from_tensor_slices((test_data, test_labels))\n", + "\n", + "#outlining number of validation samples (picked 8% of training data)\n", + "num_validation_samples = 0.08 * train_data.shape[0]\n", + "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n", + "\n", + "#isolating test samples\n", + "num_test_samples = test_data.shape[0]\n", + "num_test_samples = tf.cast(num_test_samples, tf.int64)\n", + "\n", + "#setting buffer size and shuffling training data\n", + "BUFFER_SIZE = 10000\n", + "train_dataset = train_dataset.shuffle(BUFFER_SIZE)\n", + "#extracting validation data from grouping into own var\n", + "validation_dataset = train_dataset.take(num_validation_samples)\n", + "#excluding validation data from grouping and defining as training data\n", + "train_dataset = train_dataset.skip(num_validation_samples)\n", + "\n", + "#setting batch size\n", + "BATCH_SIZE = 128\n", + "train_dataset = train_dataset.batch(BATCH_SIZE)\n", + "validation_dataset = validation_dataset.batch(num_validation_samples)\n", + "test_dataset = test_dataset.batch(num_test_samples)\n", + "\n", + "#separating validation data into inputs and targets\n", + "validation_inputs, validation_targets = next(iter(validation_dataset))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 1671 steps, validate on 18589 samples\n", + "Epoch 1/100\n", + "1671/1671 [==============================] - 163s 98ms/step - loss: 0.6321 - accuracy: 0.8310 - val_loss: 0.2672 - val_accuracy: 0.9257\n", + "Epoch 2/100\n", + "1671/1671 [==============================] - 162s 97ms/step - loss: 0.2518 - accuracy: 0.9290 - val_loss: 0.1946 - val_accuracy: 0.9454\n", + "Epoch 3/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1820 - accuracy: 0.9475 - val_loss: 0.1655 - val_accuracy: 0.9529\n", + "Epoch 4/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1433 - accuracy: 0.9578 - val_loss: 0.1397 - val_accuracy: 0.9599\n", + "Epoch 5/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1160 - accuracy: 0.9649 - val_loss: 0.1258 - val_accuracy: 0.9637\n", + "Epoch 6/100\n", + "1671/1671 [==============================] - 167s 100ms/step - loss: 0.0977 - accuracy: 0.9698 - val_loss: 0.1251 - val_accuracy: 0.9636\n", + "Epoch 7/100\n", + "1671/1671 [==============================] - 163s 98ms/step - loss: 0.0840 - accuracy: 0.9736 - val_loss: 0.1134 - val_accuracy: 0.9694\n", + "Epoch 8/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0739 - accuracy: 0.9762 - val_loss: 0.1036 - val_accuracy: 0.9719\n", + "Epoch 9/100\n", + "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0662 - accuracy: 0.9786 - val_loss: 0.1062 - val_accuracy: 0.9706\n", + "Epoch 10/100\n", + "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0590 - accuracy: 0.9806 - val_loss: 0.0992 - val_accuracy: 0.9732\n", + "Epoch 11/100\n", + "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0559 - accuracy: 0.9817 - val_loss: 0.0910 - val_accuracy: 0.9750\n", + "Epoch 12/100\n", + "1671/1671 [==============================] - 178s 107ms/step - loss: 0.0497 - accuracy: 0.9837 - val_loss: 0.0926 - val_accuracy: 0.9739\n", + "Epoch 13/100\n", + "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0479 - accuracy: 0.9842 - val_loss: 0.0907 - val_accuracy: 0.9767\n", + "Epoch 14/100\n", + "1671/1671 [==============================] - 220s 131ms/step - loss: 0.0434 - accuracy: 0.9851 - val_loss: 0.0873 - val_accuracy: 0.9770\n", + "Epoch 15/100\n", + "1671/1671 [==============================] - 212s 127ms/step - loss: 0.0437 - accuracy: 0.9854 - val_loss: 0.0795 - val_accuracy: 0.9779\n", + "Epoch 16/100\n", + "1671/1671 [==============================] - 210s 126ms/step - loss: 0.0381 - accuracy: 0.9870 - val_loss: 0.0751 - val_accuracy: 0.9796\n", + "Epoch 17/100\n", + "1671/1671 [==============================] - 206s 123ms/step - loss: 0.0385 - accuracy: 0.9869 - val_loss: 0.0711 - val_accuracy: 0.9803\n", + "Epoch 18/100\n", + "1671/1671 [==============================] - 196s 117ms/step - loss: 0.0353 - accuracy: 0.9881 - val_loss: 0.0701 - val_accuracy: 0.9817\n", + "Epoch 19/100\n", + "1671/1671 [==============================] - 178s 107ms/step - loss: 0.0348 - accuracy: 0.9883 - val_loss: 0.0684 - val_accuracy: 0.9832\n", + "Epoch 20/100\n", + "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0338 - accuracy: 0.9887 - val_loss: 0.0635 - val_accuracy: 0.9832\n", + "Epoch 21/100\n", + "1671/1671 [==============================] - 169s 101ms/step - loss: 0.0310 - accuracy: 0.9896 - val_loss: 0.0631 - val_accuracy: 0.9836\n", + "Epoch 22/100\n", + "1671/1671 [==============================] - 170s 102ms/step - loss: 0.0312 - accuracy: 0.9894 - val_loss: 0.0626 - val_accuracy: 0.9842\n", + "Epoch 23/100\n", + "1671/1671 [==============================] - 171s 103ms/step - loss: 0.0298 - accuracy: 0.9898 - val_loss: 0.0580 - val_accuracy: 0.9847\n", + "Epoch 24/100\n", + "1671/1671 [==============================] - 186s 111ms/step - loss: 0.0281 - accuracy: 0.9905 - val_loss: 0.0631 - val_accuracy: 0.9835\n", + "Epoch 25/100\n", + "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0280 - accuracy: 0.9904 - val_loss: 0.0582 - val_accuracy: 0.9835\n", + "Epoch 26/100\n", + "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0268 - accuracy: 0.9908 - val_loss: 0.0586 - val_accuracy: 0.9855\n", + "Epoch 27/100\n", + "1671/1671 [==============================] - 124s 74ms/step - loss: 0.0260 - accuracy: 0.9912 - val_loss: 0.0525 - val_accuracy: 0.9864\n", + "Epoch 28/100\n", + "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0251 - accuracy: 0.9916 - val_loss: 0.0531 - val_accuracy: 0.9862\n", + "Epoch 29/100\n", + "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0248 - accuracy: 0.9915 - val_loss: 0.0533 - val_accuracy: 0.9860\n", + "Epoch 30/100\n", + "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0242 - accuracy: 0.9917 - val_loss: 0.0478 - val_accuracy: 0.9876\n", + "Epoch 31/100\n", + "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0234 - accuracy: 0.9919 - val_loss: 0.0474 - val_accuracy: 0.9871\n", + "Epoch 32/100\n", + "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0228 - accuracy: 0.9923 - val_loss: 0.0497 - val_accuracy: 0.9869\n", + "Epoch 33/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0229 - accuracy: 0.9924 - val_loss: 0.0514 - val_accuracy: 0.9869\n", + "Epoch 34/100\n", + "1671/1671 [==============================] - 128s 77ms/step - loss: 0.0216 - accuracy: 0.9929 - val_loss: 0.0472 - val_accuracy: 0.9877\n", + "Epoch 35/100\n", + "1671/1671 [==============================] - 130s 78ms/step - loss: 0.0220 - accuracy: 0.9924 - val_loss: 0.0476 - val_accuracy: 0.9879\n", + "Epoch 36/100\n", + "1671/1671 [==============================] - 131s 78ms/step - loss: 0.0212 - accuracy: 0.9931 - val_loss: 0.0448 - val_accuracy: 0.9877\n", + "Epoch 37/100\n", + "1671/1671 [==============================] - 129s 77ms/step - loss: 0.0210 - accuracy: 0.9930 - val_loss: 0.0384 - val_accuracy: 0.9895\n", + "Epoch 38/100\n", + "1671/1671 [==============================] - 131s 79ms/step - loss: 0.0200 - accuracy: 0.9933 - val_loss: 0.0380 - val_accuracy: 0.9892\n", + "Epoch 39/100\n", + "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0199 - accuracy: 0.9932 - val_loss: 0.0388 - val_accuracy: 0.9885\n", + "Epoch 40/100\n", + "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0205 - accuracy: 0.9932 - val_loss: 0.0357 - val_accuracy: 0.9898\n", + "Epoch 41/100\n", + "1671/1671 [==============================] - 128s 77ms/step - loss: 0.0191 - accuracy: 0.9936 - val_loss: 0.0352 - val_accuracy: 0.9902\n", + "Epoch 42/100\n", + "1671/1671 [==============================] - 146s 87ms/step - loss: 0.0183 - accuracy: 0.9939 - val_loss: 0.0365 - val_accuracy: 0.9898\n", + "Epoch 43/100\n", + "1671/1671 [==============================] - 149s 89ms/step - loss: 0.0201 - accuracy: 0.9933 - val_loss: 0.0338 - val_accuracy: 0.9904\n", + "Epoch 44/100\n", + "1671/1671 [==============================] - 141s 85ms/step - loss: 0.0182 - accuracy: 0.9937 - val_loss: 0.0335 - val_accuracy: 0.9906\n", + "Epoch 45/100\n", + "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0173 - accuracy: 0.9941 - val_loss: 0.0309 - val_accuracy: 0.9911\n", + "Epoch 46/100\n", + "1671/1671 [==============================] - 129s 77ms/step - loss: 0.0190 - accuracy: 0.9935 - val_loss: 0.0329 - val_accuracy: 0.9910\n", + "Epoch 47/100\n", + "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0176 - accuracy: 0.9943 - val_loss: 0.0324 - val_accuracy: 0.9908\n", + "Epoch 48/100\n", + "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0178 - accuracy: 0.9940 - val_loss: 0.0311 - val_accuracy: 0.9909\n", + "Epoch 49/100\n", + "1671/1671 [==============================] - 126s 76ms/step - loss: 0.0176 - accuracy: 0.9942 - val_loss: 0.0323 - val_accuracy: 0.9907\n", + "Epoch 50/100\n", + "1671/1671 [==============================] - 118s 71ms/step - loss: 0.0159 - accuracy: 0.9948 - val_loss: 0.0258 - val_accuracy: 0.9927\n", + "Epoch 51/100\n", + "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0172 - accuracy: 0.9943 - val_loss: 0.0268 - val_accuracy: 0.9925\n", + "Epoch 52/100\n", + "1671/1671 [==============================] - 118s 70ms/step - loss: 0.0166 - accuracy: 0.9943 - val_loss: 0.0255 - val_accuracy: 0.9932\n", + "Epoch 53/100\n", + "1671/1671 [==============================] - 133s 80ms/step - loss: 0.0164 - accuracy: 0.9946 - val_loss: 0.0237 - val_accuracy: 0.9935\n", + "Epoch 54/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0163 - accuracy: 0.9946 - val_loss: 0.0234 - val_accuracy: 0.9930\n", + "Epoch 55/100\n", + "1671/1671 [==============================] - 118s 71ms/step - loss: 0.0157 - accuracy: 0.9947 - val_loss: 0.0242 - val_accuracy: 0.9934\n", + "Epoch 56/100\n", + "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0160 - accuracy: 0.9948 - val_loss: 0.0226 - val_accuracy: 0.9930\n", + "Epoch 57/100\n", + "1671/1671 [==============================] - 121s 72ms/step - loss: 0.0155 - accuracy: 0.9948 - val_loss: 0.0251 - val_accuracy: 0.9926\n", + "Epoch 58/100\n", + "1671/1671 [==============================] - 123s 73ms/step - loss: 0.0162 - accuracy: 0.9946 - val_loss: 0.0238 - val_accuracy: 0.9930\n", + "Epoch 59/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0152 - accuracy: 0.9950 - val_loss: 0.0226 - val_accuracy: 0.9933\n", + "Epoch 60/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0158 - accuracy: 0.9947 - val_loss: 0.0282 - val_accuracy: 0.9920\n", + "Epoch 61/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0151 - accuracy: 0.9950 - val_loss: 0.0232 - val_accuracy: 0.9933\n", + "Epoch 62/100\n", + "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0146 - accuracy: 0.9952 - val_loss: 0.0264 - val_accuracy: 0.9925\n", + "Epoch 63/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0146 - accuracy: 0.9953 - val_loss: 0.0201 - val_accuracy: 0.9938\n", + "Epoch 64/100\n", + "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0146 - accuracy: 0.9953 - val_loss: 0.0248 - val_accuracy: 0.9925\n", + "Epoch 65/100\n", + "1671/1671 [==============================] - 124s 74ms/step - loss: 0.0144 - accuracy: 0.9953 - val_loss: 0.0204 - val_accuracy: 0.9937\n", + "Epoch 66/100\n", + "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0136 - accuracy: 0.9955 - val_loss: 0.0198 - val_accuracy: 0.9940\n", + "Epoch 67/100\n", + "1671/1671 [==============================] - 132s 79ms/step - loss: 0.0138 - accuracy: 0.9953 - val_loss: 0.0227 - val_accuracy: 0.9927\n", + "Epoch 68/100\n", + "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0141 - accuracy: 0.9955 - val_loss: 0.0198 - val_accuracy: 0.9941\n", + "Epoch 69/100\n", + "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0138 - accuracy: 0.9955 - val_loss: 0.0222 - val_accuracy: 0.9937\n", + "Epoch 70/100\n", + "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0139 - accuracy: 0.9956 - val_loss: 0.0169 - val_accuracy: 0.9950\n", + "Epoch 71/100\n", + "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0132 - accuracy: 0.9957 - val_loss: 0.0195 - val_accuracy: 0.9946\n", + "Epoch 72/100\n", + "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0136 - accuracy: 0.9955 - val_loss: 0.0211 - val_accuracy: 0.9939\n", + "Epoch 73/100\n", + "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0130 - accuracy: 0.9958 - val_loss: 0.0219 - val_accuracy: 0.9937\n", + "Epoch 74/100\n", + "1671/1671 [==============================] - 141s 84ms/step - loss: 0.0137 - accuracy: 0.9957 - val_loss: 0.0177 - val_accuracy: 0.9939\n", + "Epoch 75/100\n", + "1671/1671 [==============================] - 175s 105ms/step - loss: 0.0135 - accuracy: 0.9957 - val_loss: 0.0194 - val_accuracy: 0.9945: 0.99\n", + "Epoch 76/100\n", + "1671/1671 [==============================] - 148s 88ms/step - loss: 0.0128 - accuracy: 0.9958 - val_loss: 0.0214 - val_accuracy: 0.9938\n", + "Epoch 77/100\n", + "1671/1671 [==============================] - 151s 90ms/step - loss: 0.0118 - accuracy: 0.9962 - val_loss: 0.0178 - val_accuracy: 0.9944\n", + "Epoch 78/100\n", + "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0132 - accuracy: 0.9958 - val_loss: 0.0202 - val_accuracy: 0.9943\n", + "Epoch 79/100\n", + "1671/1671 [==============================] - 189s 113ms/step - loss: 0.0130 - accuracy: 0.9957 - val_loss: 0.0182 - val_accuracy: 0.9941\n", + "Epoch 80/100\n", + "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0129 - accuracy: 0.9958 - val_loss: 0.0181 - val_accuracy: 0.9945\n", + "Epoch 81/100\n", + "1671/1671 [==============================] - 159s 95ms/step - loss: 0.0127 - accuracy: 0.9959 - val_loss: 0.0157 - val_accuracy: 0.9955\n", + "Epoch 82/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0121 - accuracy: 0.9961 - val_loss: 0.0184 - val_accuracy: 0.9948\n", + "Epoch 83/100\n", + "1671/1671 [==============================] - 163s 98ms/step - loss: 0.0123 - accuracy: 0.9961 - val_loss: 0.0163 - val_accuracy: 0.9954\n", + "Epoch 84/100\n", + "1671/1671 [==============================] - 175s 105ms/step - loss: 0.0125 - accuracy: 0.9960 - val_loss: 0.0188 - val_accuracy: 0.9948\n", + "Epoch 85/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0125 - accuracy: 0.9961 - val_loss: 0.0152 - val_accuracy: 0.9952\n", + "Epoch 86/100\n", + "1671/1671 [==============================] - 160s 96ms/step - loss: 0.0130 - accuracy: 0.9958 - val_loss: 0.0161 - val_accuracy: 0.9953\n", + "Epoch 87/100\n", + "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0120 - accuracy: 0.9961 - val_loss: 0.0168 - val_accuracy: 0.9956\n", + "Epoch 88/100\n", + "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0120 - accuracy: 0.9960 - val_loss: 0.0169 - val_accuracy: 0.9949\n", + "Epoch 89/100\n", + "1671/1671 [==============================] - 133s 80ms/step - loss: 0.0129 - accuracy: 0.9959 - val_loss: 0.0155 - val_accuracy: 0.9955\n", + "Epoch 90/100\n", + "1671/1671 [==============================] - 145s 87ms/step - loss: 0.0117 - accuracy: 0.9962 - val_loss: 0.0147 - val_accuracy: 0.9956\n", + "Epoch 91/100\n", + "1671/1671 [==============================] - 143s 85ms/step - loss: 0.0115 - accuracy: 0.9964 - val_loss: 0.0143 - val_accuracy: 0.9956\n", + "Epoch 92/100\n", + "1671/1671 [==============================] - 142s 85ms/step - loss: 0.0116 - accuracy: 0.9964 - val_loss: 0.0192 - val_accuracy: 0.9943\n", + "Epoch 93/100\n", + "1671/1671 [==============================] - 137s 82ms/step - loss: 0.0117 - accuracy: 0.9963 - val_loss: 0.0156 - val_accuracy: 0.9952\n", + "Epoch 94/100\n", + "1671/1671 [==============================] - 131s 78ms/step - loss: 0.0118 - accuracy: 0.9963 - val_loss: 0.0137 - val_accuracy: 0.9955\n", + "Epoch 95/100\n", + "1671/1671 [==============================] - 142s 85ms/step - loss: 0.0115 - accuracy: 0.9962 - val_loss: 0.0123 - val_accuracy: 0.9961\n", + "Epoch 96/100\n", + "1671/1671 [==============================] - 145s 87ms/step - loss: 0.0115 - accuracy: 0.9964 - val_loss: 0.0134 - val_accuracy: 0.9958\n", + "Epoch 97/100\n", + "1671/1671 [==============================] - 141s 85ms/step - loss: 0.0109 - accuracy: 0.9965 - val_loss: 0.0136 - val_accuracy: 0.9955\n", + "Epoch 98/100\n", + "1671/1671 [==============================] - 143s 86ms/step - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.0133 - val_accuracy: 0.9960\n", + "Epoch 99/100\n", + "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0121 - accuracy: 0.9960 - val_loss: 0.0147 - val_accuracy: 0.9954\n", + "Epoch 100/100\n", + "1671/1671 [==============================] - 139s 83ms/step - loss: 0.0111 - accuracy: 0.9966 - val_loss: 0.0159 - val_accuracy: 0.9952\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_size = 784\n", + "output_size = 49\n", + "standard_hidden_layer_width = 512\n", + "NUM_EPOCHS = 100\n", + "VALIDATION_STEPS = num_validation_samples // BATCH_SIZE\n", + "early_stopping = tf.keras.callbacks.EarlyStopping(patience=2)\n", + "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),\n", + " tf.keras.layers.MaxPooling2D(2, 2),\n", + " tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n", + " tf.keras.layers.MaxPooling2D(2,2),\n", + " tf.keras.layers.Dropout(0.25),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(standard_hidden_layer_width, activation='relu'),\n", + " tf.keras.layers.Dense(output_size, activation='softmax')\n", + " ])\n", + "\n", + "model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "model.fit(train_dataset, epochs=NUM_EPOCHS, callbacks=[reduce_lr], validation_data=(validation_inputs, validation_targets), validation_steps=VALIDATION_STEPS, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From C:\\Users\\victo\\Anaconda3\\envs\\py3-TF2.0\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "If using Keras pass *_constraint arguments to layers.\n", + "INFO:tensorflow:Assets written to: k49-convnet\\assets\n" + ] + } + ], + "source": [ + "model.save('k49-convnet')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "complete_model = tf.keras.models.load_model('k49-convnet')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = complete_model.predict([test_data])" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1000, 1000, 1000, 126, 1000, 1000, 1000, 1000, 767, 1000, 1000, 1000, 1000, 678, 629, 1000, 418, 1000, 1000, 1000, 1000, 1000, 336, 399, 1000, 1000, 836, 1000, 1000, 324, 1000, 498, 280, 552, 1000, 1000, 260, 1000, 1000, 1000, 1000, 1000, 348, 390, 68, 64, 1000, 1000, 574]\n", + "[960, 970, 971, 113, 957, 889, 931, 929, 722, 926, 954, 921, 909, 593, 562, 963, 405, 959, 933, 945, 909, 941, 315, 364, 934, 931, 799, 910, 945, 294, 967, 466, 249, 528, 970, 948, 246, 980, 918, 927, 928, 968, 331, 354, 50, 58, 964, 978, 474]\n", + "0.930142277358246\n" + ] + } + ], + "source": [ + "totals = []\n", + "for cls in range(49):\n", + " total = 0\n", + " for i in test_labels:\n", + " if i == cls:\n", + " total = total + 1\n", + " totals.append(total)\n", + "\n", + "hits = []\n", + "for cls in range(49):\n", + " total_hits = 0\n", + " for i in range(0,test_labels.shape[0]):\n", + " if test_labels[i] == cls == np.argmax(predictions[i]):\n", + " total_hits = total_hits + 1\n", + " hits.append(total_hits)\n", + " \n", + "accuracy_list = []\n", + "for i in range(0,len(hits)):\n", + " accuracy = hits[i]/totals[i]\n", + " accuracy_list.append(accuracy)\n", + "\n", + "print(np.mean(accuracy_list))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:py3-TF2.0]", + "language": "python", + "name": "conda-env-py3-TF2.0-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 2b566f633692dcaf4b3b030267b08a7958ee124a Mon Sep 17 00:00:00 2001 From: VicHofs <57238498+VicHofs@users.noreply.github.com> Date: Mon, 3 Feb 2020 18:09:18 -0300 Subject: [PATCH 06/12] Delete k49 conv-net.ipynb --- k49 conv-net.ipynb | 292 --------------------------------------------- 1 file changed, 292 deletions(-) delete mode 100644 k49 conv-net.ipynb diff --git a/k49 conv-net.ipynb b/k49 conv-net.ipynb deleted file mode 100644 index 156de45..0000000 --- a/k49 conv-net.ipynb +++ /dev/null @@ -1,292 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "train_data = np.load('k49-train-imgs.npz')['arr_0']\n", - "train_labels = np.load('k49-train-labels.npz')['arr_0']\n", - "test_data = np.load('k49-test-imgs.npz')['arr_0']\n", - "test_labels = np.load('k49-test-labels.npz')['arr_0']\n", - "\n", - "train_data1 = tf.keras.utils.normalize(train_data, axis=1)\n", - "test_data1 = tf.keras.utils.normalize(test_data, axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,10))\n", - "for i in range(25):\n", - " plt.subplot(5,5,i+1)\n", - " plt.xticks([])\n", - " plt.yticks([])\n", - " plt.grid(False)\n", - " plt.imshow(train_data[i], cmap=plt.cm.binary)\n", - " plt.xlabel(train_labels[i])\n", - "train_data = train_data1.reshape((train_data.shape[0], train_data.shape[1], train_data.shape[2], 1))\n", - "test_data = test_data1.reshape((test_data.shape[0], test_data.shape[1], test_data.shape[2], 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_labels))\n", - "test_dataset = tf.data.Dataset.from_tensor_slices((test_data, test_labels))\n", - "\n", - "#outlining number of validation samples (picked 8% of training data)\n", - "num_validation_samples = 0.08 * train_data.shape[0]\n", - "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n", - "\n", - "#isolating test samples\n", - "num_test_samples = test_data.shape[0]\n", - "num_test_samples = tf.cast(num_test_samples, tf.int64)\n", - "\n", - "#setting buffer size and shuffling training data\n", - "BUFFER_SIZE = 10000\n", - "train_dataset = train_dataset.shuffle(BUFFER_SIZE)\n", - "#extracting validation data from grouping into own var\n", - "validation_dataset = train_dataset.take(num_validation_samples)\n", - "#excluding validation data from grouping and defining as training data\n", - "train_dataset = train_dataset.skip(num_validation_samples)\n", - "\n", - "#setting batch size\n", - "BATCH_SIZE = 128\n", - "train_dataset = train_dataset.batch(BATCH_SIZE)\n", - "validation_dataset = validation_dataset.batch(num_validation_samples)\n", - "test_dataset = test_dataset.batch(num_test_samples)\n", - "\n", - "#separating validation data into inputs and targets\n", - "validation_inputs, validation_targets = next(iter(validation_dataset))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train for 1671 steps, validate on 18589 samples\n", - "Epoch 1/100\n", - "1671/1671 [==============================] - 163s 98ms/step - loss: 0.6321 - accuracy: 0.8310 - val_loss: 0.2672 - val_accuracy: 0.9257\n", - "Epoch 100/100\n", - "1671/1671 [==============================] - 139s 83ms/step - loss: 0.0111 - accuracy: 0.9966 - val_loss: 0.0159 - val_accuracy: 0.9952\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "input_size = 784\n", - "output_size = 49\n", - "standard_hidden_layer_width = 512\n", - "NUM_EPOCHS = 100\n", - "VALIDATION_STEPS = num_validation_samples // BATCH_SIZE\n", - "early_stopping = tf.keras.callbacks.EarlyStopping(patience=2)\n", - "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)\n", - "\n", - "model = tf.keras.Sequential([\n", - " tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),\n", - " tf.keras.layers.MaxPooling2D(2, 2),\n", - " tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n", - " tf.keras.layers.MaxPooling2D(2,2),\n", - " tf.keras.layers.Dropout(0.25),\n", - " tf.keras.layers.Flatten(),\n", - " tf.keras.layers.Dense(standard_hidden_layer_width, activation='relu'),\n", - " tf.keras.layers.Dense(output_size, activation='softmax')\n", - " ])\n", - "\n", - "model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "model.fit(train_dataset, epochs=NUM_EPOCHS, callbacks=[reduce_lr], validation_data=(validation_inputs, validation_targets), validation_steps=VALIDATION_STEPS, verbose=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\victo\\Anaconda3\\envs\\py3-TF2.0\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "If using Keras pass *_constraint arguments to layers.\n", - "INFO:tensorflow:Assets written to: k49-convnet\\assets\n" - ] - } - ], - "source": [ - "model.save('k49-convnet')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "complete_model = tf.keras.models.load_model('k49-convnet')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = complete_model.predict([test_data])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1.1654630e-22, 5.9298899e-24, 1.2344980e-27, ..., 3.2112436e-19,\n", - " 6.4444245e-17, 1.7431504e-16],\n", - " [1.9990182e-06, 6.2519321e-24, 6.3204732e-22, ..., 2.7016511e-09,\n", - " 8.3437172e-20, 2.5418602e-25],\n", - " [9.8383986e-22, 1.2532079e-32, 2.7175545e-23, ..., 5.8478419e-34,\n", - " 1.5754820e-33, 9.4036669e-23],\n", - " ...,\n", - " [1.4244270e-18, 1.0000000e+00, 1.5626800e-25, ..., 0.0000000e+00,\n", - " 3.8913057e-24, 1.7517804e-19],\n", - " [1.5374325e-17, 1.8800380e-12, 6.4201010e-27, ..., 3.5142543e-17,\n", - " 4.8406890e-11, 1.0647082e-18],\n", - " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 8.1922249e-34,\n", - " 1.0000000e+00, 1.0231144e-35]], dtype=float32)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "predictions" - ] - }, - "source": [ - "map = ['あ','い','う','え','お','か','き','く','け','こ','さ','し','す','せ','そ','た','ち','つ','て','と','な','に','ぬ','ね','の','は','ひ','ふ','へ','ほ','ま','み','む','め','も','や','ゆ','よ','ら','り','る','れ','ろ','わ','ゐ','ゑ','を','ん','ゝ']\n", - "\n", - "plt.imshow(test_data1[38546], cmap=plt.cm.binary_r)\n", - "plt.show()\n", - "print(map[np.argmax(predictions[38546])])\n", - "\n", - "for i in range(50):\n", - " plt.imshow(test_data1[i], cmap=plt.cm.binary_r)\n", - " plt.show()\n", - " print(map[np.argmax(predictions[i])])" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1000, 1000, 1000, 126, 1000, 1000, 1000, 1000, 767, 1000, 1000, 1000, 1000, 678, 629, 1000, 418, 1000, 1000, 1000, 1000, 1000, 336, 399, 1000, 1000, 836, 1000, 1000, 324, 1000, 498, 280, 552, 1000, 1000, 260, 1000, 1000, 1000, 1000, 1000, 348, 390, 68, 64, 1000, 1000, 574]\n", - "[960, 970, 971, 113, 957, 889, 931, 929, 722, 926, 954, 921, 909, 593, 562, 963, 405, 959, 933, 945, 909, 941, 315, 364, 934, 931, 799, 910, 945, 294, 967, 466, 249, 528, 970, 948, 246, 980, 918, 927, 928, 968, 331, 354, 50, 58, 964, 978, 474]\n", - "The Balanced Accuracy is: 0.930142277358246\n" - ] - } - ], - "source": [ - "totals = []\n", - "for cls in range(49):\n", - " total = 0\n", - " for i in test_labels:\n", - " if i == cls:\n", - " total = total + 1\n", - " totals.append(total)\n", - "\n", - "hits = []\n", - "for cls in range(49):\n", - " total_hits = 0\n", - " for i in range(0,test_labels.shape[0]):\n", - " if test_labels[i] == cls == np.argmax(predictions[i]):\n", - " total_hits = total_hits + 1\n", - " hits.append(total_hits)\n", - " \n", - "accuracy_list = []\n", - "for i in range(0,len(hits)):\n", - " accuracy = hits[i]/totals[i]\n", - " accuracy_list.append(accuracy)\n", - "\n", - "print(f'The balanced accuracy is: {np.mean(accuracy_list)}')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:py3-TF2.0]", - "language": "python", - "name": "conda-env-py3-TF2.0-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 72d85d97edf799783c0526bc1035e93b0018d7dc Mon Sep 17 00:00:00 2001 From: VicHofs <57238498+VicHofs@users.noreply.github.com> Date: Mon, 3 Feb 2020 18:11:59 -0300 Subject: [PATCH 07/12] Update k49 cnn notebook.ipynb --- k49 cnn notebook.ipynb | 197 +---------------------------------------- 1 file changed, 1 insertion(+), 196 deletions(-) diff --git a/k49 cnn notebook.ipynb b/k49 cnn notebook.ipynb index dea16a7..b3fa5c4 100644 --- a/k49 cnn notebook.ipynb +++ b/k49 cnn notebook.ipynb @@ -97,202 +97,7 @@ "Train for 1671 steps, validate on 18589 samples\n", "Epoch 1/100\n", "1671/1671 [==============================] - 163s 98ms/step - loss: 0.6321 - accuracy: 0.8310 - val_loss: 0.2672 - val_accuracy: 0.9257\n", - "Epoch 2/100\n", - "1671/1671 [==============================] - 162s 97ms/step - loss: 0.2518 - accuracy: 0.9290 - val_loss: 0.1946 - val_accuracy: 0.9454\n", - "Epoch 3/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1820 - accuracy: 0.9475 - val_loss: 0.1655 - val_accuracy: 0.9529\n", - "Epoch 4/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1433 - accuracy: 0.9578 - val_loss: 0.1397 - val_accuracy: 0.9599\n", - "Epoch 5/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1160 - accuracy: 0.9649 - val_loss: 0.1258 - val_accuracy: 0.9637\n", - "Epoch 6/100\n", - "1671/1671 [==============================] - 167s 100ms/step - loss: 0.0977 - accuracy: 0.9698 - val_loss: 0.1251 - val_accuracy: 0.9636\n", - "Epoch 7/100\n", - "1671/1671 [==============================] - 163s 98ms/step - loss: 0.0840 - accuracy: 0.9736 - val_loss: 0.1134 - val_accuracy: 0.9694\n", - "Epoch 8/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0739 - accuracy: 0.9762 - val_loss: 0.1036 - val_accuracy: 0.9719\n", - "Epoch 9/100\n", - "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0662 - accuracy: 0.9786 - val_loss: 0.1062 - val_accuracy: 0.9706\n", - "Epoch 10/100\n", - "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0590 - accuracy: 0.9806 - val_loss: 0.0992 - val_accuracy: 0.9732\n", - "Epoch 11/100\n", - "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0559 - accuracy: 0.9817 - val_loss: 0.0910 - val_accuracy: 0.9750\n", - "Epoch 12/100\n", - "1671/1671 [==============================] - 178s 107ms/step - loss: 0.0497 - accuracy: 0.9837 - val_loss: 0.0926 - val_accuracy: 0.9739\n", - "Epoch 13/100\n", - "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0479 - accuracy: 0.9842 - val_loss: 0.0907 - val_accuracy: 0.9767\n", - "Epoch 14/100\n", - "1671/1671 [==============================] - 220s 131ms/step - loss: 0.0434 - accuracy: 0.9851 - val_loss: 0.0873 - val_accuracy: 0.9770\n", - "Epoch 15/100\n", - "1671/1671 [==============================] - 212s 127ms/step - loss: 0.0437 - accuracy: 0.9854 - val_loss: 0.0795 - val_accuracy: 0.9779\n", - "Epoch 16/100\n", - "1671/1671 [==============================] - 210s 126ms/step - loss: 0.0381 - accuracy: 0.9870 - val_loss: 0.0751 - val_accuracy: 0.9796\n", - "Epoch 17/100\n", - "1671/1671 [==============================] - 206s 123ms/step - loss: 0.0385 - accuracy: 0.9869 - val_loss: 0.0711 - val_accuracy: 0.9803\n", - "Epoch 18/100\n", - "1671/1671 [==============================] - 196s 117ms/step - loss: 0.0353 - accuracy: 0.9881 - val_loss: 0.0701 - val_accuracy: 0.9817\n", - "Epoch 19/100\n", - "1671/1671 [==============================] - 178s 107ms/step - loss: 0.0348 - accuracy: 0.9883 - val_loss: 0.0684 - val_accuracy: 0.9832\n", - "Epoch 20/100\n", - "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0338 - accuracy: 0.9887 - val_loss: 0.0635 - val_accuracy: 0.9832\n", - "Epoch 21/100\n", - "1671/1671 [==============================] - 169s 101ms/step - loss: 0.0310 - accuracy: 0.9896 - val_loss: 0.0631 - val_accuracy: 0.9836\n", - "Epoch 22/100\n", - "1671/1671 [==============================] - 170s 102ms/step - loss: 0.0312 - accuracy: 0.9894 - val_loss: 0.0626 - val_accuracy: 0.9842\n", - "Epoch 23/100\n", - "1671/1671 [==============================] - 171s 103ms/step - loss: 0.0298 - accuracy: 0.9898 - val_loss: 0.0580 - val_accuracy: 0.9847\n", - "Epoch 24/100\n", - "1671/1671 [==============================] - 186s 111ms/step - loss: 0.0281 - accuracy: 0.9905 - val_loss: 0.0631 - val_accuracy: 0.9835\n", - "Epoch 25/100\n", - "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0280 - accuracy: 0.9904 - val_loss: 0.0582 - val_accuracy: 0.9835\n", - "Epoch 26/100\n", - "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0268 - accuracy: 0.9908 - val_loss: 0.0586 - val_accuracy: 0.9855\n", - "Epoch 27/100\n", - "1671/1671 [==============================] - 124s 74ms/step - loss: 0.0260 - accuracy: 0.9912 - val_loss: 0.0525 - val_accuracy: 0.9864\n", - "Epoch 28/100\n", - "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0251 - accuracy: 0.9916 - val_loss: 0.0531 - val_accuracy: 0.9862\n", - "Epoch 29/100\n", - "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0248 - accuracy: 0.9915 - val_loss: 0.0533 - val_accuracy: 0.9860\n", - "Epoch 30/100\n", - "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0242 - accuracy: 0.9917 - val_loss: 0.0478 - val_accuracy: 0.9876\n", - "Epoch 31/100\n", - "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0234 - accuracy: 0.9919 - val_loss: 0.0474 - val_accuracy: 0.9871\n", - "Epoch 32/100\n", - "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0228 - accuracy: 0.9923 - val_loss: 0.0497 - val_accuracy: 0.9869\n", - "Epoch 33/100\n", - "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0229 - accuracy: 0.9924 - val_loss: 0.0514 - val_accuracy: 0.9869\n", - "Epoch 34/100\n", - "1671/1671 [==============================] - 128s 77ms/step - loss: 0.0216 - accuracy: 0.9929 - val_loss: 0.0472 - val_accuracy: 0.9877\n", - "Epoch 35/100\n", - "1671/1671 [==============================] - 130s 78ms/step - loss: 0.0220 - accuracy: 0.9924 - val_loss: 0.0476 - val_accuracy: 0.9879\n", - "Epoch 36/100\n", - "1671/1671 [==============================] - 131s 78ms/step - loss: 0.0212 - accuracy: 0.9931 - val_loss: 0.0448 - val_accuracy: 0.9877\n", - "Epoch 37/100\n", - "1671/1671 [==============================] - 129s 77ms/step - loss: 0.0210 - accuracy: 0.9930 - val_loss: 0.0384 - val_accuracy: 0.9895\n", - "Epoch 38/100\n", - "1671/1671 [==============================] - 131s 79ms/step - loss: 0.0200 - accuracy: 0.9933 - val_loss: 0.0380 - val_accuracy: 0.9892\n", - "Epoch 39/100\n", - "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0199 - accuracy: 0.9932 - val_loss: 0.0388 - val_accuracy: 0.9885\n", - "Epoch 40/100\n", - "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0205 - accuracy: 0.9932 - val_loss: 0.0357 - val_accuracy: 0.9898\n", - "Epoch 41/100\n", - "1671/1671 [==============================] - 128s 77ms/step - loss: 0.0191 - accuracy: 0.9936 - val_loss: 0.0352 - val_accuracy: 0.9902\n", - "Epoch 42/100\n", - "1671/1671 [==============================] - 146s 87ms/step - loss: 0.0183 - accuracy: 0.9939 - val_loss: 0.0365 - val_accuracy: 0.9898\n", - "Epoch 43/100\n", - "1671/1671 [==============================] - 149s 89ms/step - loss: 0.0201 - accuracy: 0.9933 - val_loss: 0.0338 - val_accuracy: 0.9904\n", - "Epoch 44/100\n", - "1671/1671 [==============================] - 141s 85ms/step - loss: 0.0182 - accuracy: 0.9937 - val_loss: 0.0335 - val_accuracy: 0.9906\n", - "Epoch 45/100\n", - "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0173 - accuracy: 0.9941 - val_loss: 0.0309 - val_accuracy: 0.9911\n", - "Epoch 46/100\n", - "1671/1671 [==============================] - 129s 77ms/step - loss: 0.0190 - accuracy: 0.9935 - val_loss: 0.0329 - val_accuracy: 0.9910\n", - "Epoch 47/100\n", - "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0176 - accuracy: 0.9943 - val_loss: 0.0324 - val_accuracy: 0.9908\n", - "Epoch 48/100\n", - "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0178 - accuracy: 0.9940 - val_loss: 0.0311 - val_accuracy: 0.9909\n", - "Epoch 49/100\n", - "1671/1671 [==============================] - 126s 76ms/step - loss: 0.0176 - accuracy: 0.9942 - val_loss: 0.0323 - val_accuracy: 0.9907\n", - "Epoch 50/100\n", - "1671/1671 [==============================] - 118s 71ms/step - loss: 0.0159 - accuracy: 0.9948 - val_loss: 0.0258 - val_accuracy: 0.9927\n", - "Epoch 51/100\n", - "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0172 - accuracy: 0.9943 - val_loss: 0.0268 - val_accuracy: 0.9925\n", - "Epoch 52/100\n", - "1671/1671 [==============================] - 118s 70ms/step - loss: 0.0166 - accuracy: 0.9943 - val_loss: 0.0255 - val_accuracy: 0.9932\n", - "Epoch 53/100\n", - "1671/1671 [==============================] - 133s 80ms/step - loss: 0.0164 - accuracy: 0.9946 - val_loss: 0.0237 - val_accuracy: 0.9935\n", - "Epoch 54/100\n", - "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0163 - accuracy: 0.9946 - val_loss: 0.0234 - val_accuracy: 0.9930\n", - "Epoch 55/100\n", - "1671/1671 [==============================] - 118s 71ms/step - loss: 0.0157 - accuracy: 0.9947 - val_loss: 0.0242 - val_accuracy: 0.9934\n", - "Epoch 56/100\n", - "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0160 - accuracy: 0.9948 - val_loss: 0.0226 - val_accuracy: 0.9930\n", - "Epoch 57/100\n", - "1671/1671 [==============================] - 121s 72ms/step - loss: 0.0155 - accuracy: 0.9948 - val_loss: 0.0251 - val_accuracy: 0.9926\n", - "Epoch 58/100\n", - "1671/1671 [==============================] - 123s 73ms/step - loss: 0.0162 - accuracy: 0.9946 - val_loss: 0.0238 - val_accuracy: 0.9930\n", - "Epoch 59/100\n", - "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0152 - accuracy: 0.9950 - val_loss: 0.0226 - val_accuracy: 0.9933\n", - "Epoch 60/100\n", - "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0158 - accuracy: 0.9947 - val_loss: 0.0282 - val_accuracy: 0.9920\n", - "Epoch 61/100\n", - "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0151 - accuracy: 0.9950 - val_loss: 0.0232 - val_accuracy: 0.9933\n", - "Epoch 62/100\n", - "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0146 - accuracy: 0.9952 - val_loss: 0.0264 - val_accuracy: 0.9925\n", - "Epoch 63/100\n", - "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0146 - accuracy: 0.9953 - val_loss: 0.0201 - val_accuracy: 0.9938\n", - "Epoch 64/100\n", - "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0146 - accuracy: 0.9953 - val_loss: 0.0248 - val_accuracy: 0.9925\n", - "Epoch 65/100\n", - "1671/1671 [==============================] - 124s 74ms/step - loss: 0.0144 - accuracy: 0.9953 - val_loss: 0.0204 - val_accuracy: 0.9937\n", - "Epoch 66/100\n", - "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0136 - accuracy: 0.9955 - val_loss: 0.0198 - val_accuracy: 0.9940\n", - "Epoch 67/100\n", - "1671/1671 [==============================] - 132s 79ms/step - loss: 0.0138 - accuracy: 0.9953 - val_loss: 0.0227 - val_accuracy: 0.9927\n", - "Epoch 68/100\n", - "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0141 - accuracy: 0.9955 - val_loss: 0.0198 - val_accuracy: 0.9941\n", - "Epoch 69/100\n", - "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0138 - accuracy: 0.9955 - val_loss: 0.0222 - val_accuracy: 0.9937\n", - "Epoch 70/100\n", - "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0139 - accuracy: 0.9956 - val_loss: 0.0169 - val_accuracy: 0.9950\n", - "Epoch 71/100\n", - "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0132 - accuracy: 0.9957 - val_loss: 0.0195 - val_accuracy: 0.9946\n", - "Epoch 72/100\n", - "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0136 - accuracy: 0.9955 - val_loss: 0.0211 - val_accuracy: 0.9939\n", - "Epoch 73/100\n", - "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0130 - accuracy: 0.9958 - val_loss: 0.0219 - val_accuracy: 0.9937\n", - "Epoch 74/100\n", - "1671/1671 [==============================] - 141s 84ms/step - loss: 0.0137 - accuracy: 0.9957 - val_loss: 0.0177 - val_accuracy: 0.9939\n", - "Epoch 75/100\n", - "1671/1671 [==============================] - 175s 105ms/step - loss: 0.0135 - accuracy: 0.9957 - val_loss: 0.0194 - val_accuracy: 0.9945: 0.99\n", - "Epoch 76/100\n", - "1671/1671 [==============================] - 148s 88ms/step - loss: 0.0128 - accuracy: 0.9958 - val_loss: 0.0214 - val_accuracy: 0.9938\n", - "Epoch 77/100\n", - "1671/1671 [==============================] - 151s 90ms/step - loss: 0.0118 - accuracy: 0.9962 - val_loss: 0.0178 - val_accuracy: 0.9944\n", - "Epoch 78/100\n", - "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0132 - accuracy: 0.9958 - val_loss: 0.0202 - val_accuracy: 0.9943\n", - "Epoch 79/100\n", - "1671/1671 [==============================] - 189s 113ms/step - loss: 0.0130 - accuracy: 0.9957 - val_loss: 0.0182 - val_accuracy: 0.9941\n", - "Epoch 80/100\n", - "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0129 - accuracy: 0.9958 - val_loss: 0.0181 - val_accuracy: 0.9945\n", - "Epoch 81/100\n", - "1671/1671 [==============================] - 159s 95ms/step - loss: 0.0127 - accuracy: 0.9959 - val_loss: 0.0157 - val_accuracy: 0.9955\n", - "Epoch 82/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0121 - accuracy: 0.9961 - val_loss: 0.0184 - val_accuracy: 0.9948\n", - "Epoch 83/100\n", - "1671/1671 [==============================] - 163s 98ms/step - loss: 0.0123 - accuracy: 0.9961 - val_loss: 0.0163 - val_accuracy: 0.9954\n", - "Epoch 84/100\n", - "1671/1671 [==============================] - 175s 105ms/step - loss: 0.0125 - accuracy: 0.9960 - val_loss: 0.0188 - val_accuracy: 0.9948\n", - "Epoch 85/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0125 - accuracy: 0.9961 - val_loss: 0.0152 - val_accuracy: 0.9952\n", - "Epoch 86/100\n", - "1671/1671 [==============================] - 160s 96ms/step - loss: 0.0130 - accuracy: 0.9958 - val_loss: 0.0161 - val_accuracy: 0.9953\n", - "Epoch 87/100\n", - "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0120 - accuracy: 0.9961 - val_loss: 0.0168 - val_accuracy: 0.9956\n", - "Epoch 88/100\n", - "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0120 - accuracy: 0.9960 - val_loss: 0.0169 - val_accuracy: 0.9949\n", - "Epoch 89/100\n", - "1671/1671 [==============================] - 133s 80ms/step - loss: 0.0129 - accuracy: 0.9959 - val_loss: 0.0155 - val_accuracy: 0.9955\n", - "Epoch 90/100\n", - "1671/1671 [==============================] - 145s 87ms/step - loss: 0.0117 - accuracy: 0.9962 - val_loss: 0.0147 - val_accuracy: 0.9956\n", - "Epoch 91/100\n", - "1671/1671 [==============================] - 143s 85ms/step - loss: 0.0115 - accuracy: 0.9964 - val_loss: 0.0143 - val_accuracy: 0.9956\n", - "Epoch 92/100\n", - "1671/1671 [==============================] - 142s 85ms/step - loss: 0.0116 - accuracy: 0.9964 - val_loss: 0.0192 - val_accuracy: 0.9943\n", - "Epoch 93/100\n", - "1671/1671 [==============================] - 137s 82ms/step - loss: 0.0117 - accuracy: 0.9963 - val_loss: 0.0156 - val_accuracy: 0.9952\n", - "Epoch 94/100\n", - "1671/1671 [==============================] - 131s 78ms/step - loss: 0.0118 - accuracy: 0.9963 - val_loss: 0.0137 - val_accuracy: 0.9955\n", - "Epoch 95/100\n", - "1671/1671 [==============================] - 142s 85ms/step - loss: 0.0115 - accuracy: 0.9962 - val_loss: 0.0123 - val_accuracy: 0.9961\n", - "Epoch 96/100\n", - "1671/1671 [==============================] - 145s 87ms/step - loss: 0.0115 - accuracy: 0.9964 - val_loss: 0.0134 - val_accuracy: 0.9958\n", - "Epoch 97/100\n", - "1671/1671 [==============================] - 141s 85ms/step - loss: 0.0109 - accuracy: 0.9965 - val_loss: 0.0136 - val_accuracy: 0.9955\n", - "Epoch 98/100\n", - "1671/1671 [==============================] - 143s 86ms/step - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.0133 - val_accuracy: 0.9960\n", - "Epoch 99/100\n", - "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0121 - accuracy: 0.9960 - val_loss: 0.0147 - val_accuracy: 0.9954\n", + "...\n" "Epoch 100/100\n", "1671/1671 [==============================] - 139s 83ms/step - loss: 0.0111 - accuracy: 0.9966 - val_loss: 0.0159 - val_accuracy: 0.9952\n" ] From 9b829ce134384daa2c8fff57de6f8a306f845482 Mon Sep 17 00:00:00 2001 From: VicHofs <57238498+VicHofs@users.noreply.github.com> Date: Mon, 3 Feb 2020 18:14:08 -0300 Subject: [PATCH 08/12] Delete k49 cnn notebook.ipynb --- k49 cnn notebook.ipynb | 243 ----------------------------------------- 1 file changed, 243 deletions(-) delete mode 100644 k49 cnn notebook.ipynb diff --git a/k49 cnn notebook.ipynb b/k49 cnn notebook.ipynb deleted file mode 100644 index b3fa5c4..0000000 --- a/k49 cnn notebook.ipynb +++ /dev/null @@ -1,243 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "train_data = np.load('k49-train-imgs.npz')['arr_0']\n", - "train_labels = np.load('k49-train-labels.npz')['arr_0']\n", - "test_data = np.load('k49-test-imgs.npz')['arr_0']\n", - "test_labels = np.load('k49-test-labels.npz')['arr_0']\n", - "\n", - "train_data1 = tf.keras.utils.normalize(train_data, axis=1)\n", - "test_data1 = tf.keras.utils.normalize(test_data, axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,10))\n", - "for i in range(25):\n", - " plt.subplot(5,5,i+1)\n", - " plt.xticks([])\n", - " plt.yticks([])\n", - " plt.grid(False)\n", - " plt.imshow(train_data[i], cmap=plt.cm.binary)\n", - " plt.xlabel(train_labels[i])\n", - "train_data = train_data1.reshape((train_data.shape[0], train_data.shape[1], train_data.shape[2], 1))\n", - "test_data = test_data1.reshape((test_data.shape[0], test_data.shape[1], test_data.shape[2], 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_labels))\n", - "test_dataset = tf.data.Dataset.from_tensor_slices((test_data, test_labels))\n", - "\n", - "#outlining number of validation samples (picked 8% of training data)\n", - "num_validation_samples = 0.08 * train_data.shape[0]\n", - "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n", - "\n", - "#isolating test samples\n", - "num_test_samples = test_data.shape[0]\n", - "num_test_samples = tf.cast(num_test_samples, tf.int64)\n", - "\n", - "#setting buffer size and shuffling training data\n", - "BUFFER_SIZE = 10000\n", - "train_dataset = train_dataset.shuffle(BUFFER_SIZE)\n", - "#extracting validation data from grouping into own var\n", - "validation_dataset = train_dataset.take(num_validation_samples)\n", - "#excluding validation data from grouping and defining as training data\n", - "train_dataset = train_dataset.skip(num_validation_samples)\n", - "\n", - "#setting batch size\n", - "BATCH_SIZE = 128\n", - "train_dataset = train_dataset.batch(BATCH_SIZE)\n", - "validation_dataset = validation_dataset.batch(num_validation_samples)\n", - "test_dataset = test_dataset.batch(num_test_samples)\n", - "\n", - "#separating validation data into inputs and targets\n", - "validation_inputs, validation_targets = next(iter(validation_dataset))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train for 1671 steps, validate on 18589 samples\n", - "Epoch 1/100\n", - "1671/1671 [==============================] - 163s 98ms/step - loss: 0.6321 - accuracy: 0.8310 - val_loss: 0.2672 - val_accuracy: 0.9257\n", - "...\n" - "Epoch 100/100\n", - "1671/1671 [==============================] - 139s 83ms/step - loss: 0.0111 - accuracy: 0.9966 - val_loss: 0.0159 - val_accuracy: 0.9952\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "input_size = 784\n", - "output_size = 49\n", - "standard_hidden_layer_width = 512\n", - "NUM_EPOCHS = 100\n", - "VALIDATION_STEPS = num_validation_samples // BATCH_SIZE\n", - "early_stopping = tf.keras.callbacks.EarlyStopping(patience=2)\n", - "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)\n", - "\n", - "model = tf.keras.Sequential([\n", - " tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),\n", - " tf.keras.layers.MaxPooling2D(2, 2),\n", - " tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n", - " tf.keras.layers.MaxPooling2D(2,2),\n", - " tf.keras.layers.Dropout(0.25),\n", - " tf.keras.layers.Flatten(),\n", - " tf.keras.layers.Dense(standard_hidden_layer_width, activation='relu'),\n", - " tf.keras.layers.Dense(output_size, activation='softmax')\n", - " ])\n", - "\n", - "model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "model.fit(train_dataset, epochs=NUM_EPOCHS, callbacks=[reduce_lr], validation_data=(validation_inputs, validation_targets), validation_steps=VALIDATION_STEPS, verbose=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\victo\\Anaconda3\\envs\\py3-TF2.0\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "If using Keras pass *_constraint arguments to layers.\n", - "INFO:tensorflow:Assets written to: k49-convnet\\assets\n" - ] - } - ], - "source": [ - "model.save('k49-convnet')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "complete_model = tf.keras.models.load_model('k49-convnet')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = complete_model.predict([test_data])" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1000, 1000, 1000, 126, 1000, 1000, 1000, 1000, 767, 1000, 1000, 1000, 1000, 678, 629, 1000, 418, 1000, 1000, 1000, 1000, 1000, 336, 399, 1000, 1000, 836, 1000, 1000, 324, 1000, 498, 280, 552, 1000, 1000, 260, 1000, 1000, 1000, 1000, 1000, 348, 390, 68, 64, 1000, 1000, 574]\n", - "[960, 970, 971, 113, 957, 889, 931, 929, 722, 926, 954, 921, 909, 593, 562, 963, 405, 959, 933, 945, 909, 941, 315, 364, 934, 931, 799, 910, 945, 294, 967, 466, 249, 528, 970, 948, 246, 980, 918, 927, 928, 968, 331, 354, 50, 58, 964, 978, 474]\n", - "0.930142277358246\n" - ] - } - ], - "source": [ - "totals = []\n", - "for cls in range(49):\n", - " total = 0\n", - " for i in test_labels:\n", - " if i == cls:\n", - " total = total + 1\n", - " totals.append(total)\n", - "\n", - "hits = []\n", - "for cls in range(49):\n", - " total_hits = 0\n", - " for i in range(0,test_labels.shape[0]):\n", - " if test_labels[i] == cls == np.argmax(predictions[i]):\n", - " total_hits = total_hits + 1\n", - " hits.append(total_hits)\n", - " \n", - "accuracy_list = []\n", - "for i in range(0,len(hits)):\n", - " accuracy = hits[i]/totals[i]\n", - " accuracy_list.append(accuracy)\n", - "\n", - "print(np.mean(accuracy_list))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:py3-TF2.0]", - "language": "python", - "name": "conda-env-py3-TF2.0-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 46e08b0dca059d988d02d8080a848fa56f01f0b9 Mon Sep 17 00:00:00 2001 From: VicHofs <57238498+VicHofs@users.noreply.github.com> Date: Mon, 3 Feb 2020 18:15:03 -0300 Subject: [PATCH 09/12] Add Notebook format Jupyter Notebook format with complete code --- k49 cnn notebook.ipynb | 438 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 438 insertions(+) create mode 100644 k49 cnn notebook.ipynb diff --git a/k49 cnn notebook.ipynb b/k49 cnn notebook.ipynb new file mode 100644 index 0000000..dea16a7 --- /dev/null +++ b/k49 cnn notebook.ipynb @@ -0,0 +1,438 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "train_data = np.load('k49-train-imgs.npz')['arr_0']\n", + "train_labels = np.load('k49-train-labels.npz')['arr_0']\n", + "test_data = np.load('k49-test-imgs.npz')['arr_0']\n", + "test_labels = np.load('k49-test-labels.npz')['arr_0']\n", + "\n", + "train_data1 = tf.keras.utils.normalize(train_data, axis=1)\n", + "test_data1 = tf.keras.utils.normalize(test_data, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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urPrvv/+27LAZLLqyXXr16qW6RYsWql2X1fLly1W731P8zmHatF8oWrRo0OMMateubR2/9957qrFaulsC4Mknn1RdokSJsOfpZ/B76bp7sSFvsN87BO9DEZFatWqpxnIgAwcOtOywNEiywV9xQgghhPgCLnoIIYQQ4guS3r2FLi13O/XQoUOZvqZ58+bWMWYfTJ06VfXZZ59t2WElYD/iRvpjBoab0YFgZWS3imjTpk1Vly1bVjWzspIfrJA+evRoawwrpGMzWNflNGvWLNW33HKLaqwWLGJ/d7AC+M8//xxwfgcPHrSO3XOS0MCGo3ivu5+n2zCUZI9OnToFHHvssces419//TWkc6J7cuzYsapnzJgR8L2xYXCpUqVCep94wp0eQgghhPgCLnoIIYQQ4gu46CGEEEKIL0j6mJ46deqodjvEbt++XTWm4o0fP96yw/Q+7DQ8e/Zsy86PMT1YEdf1IQeK43HTUTGOp02bNtYYY3dSC/y+YHXzyZMnW3bt2rVT/eKLL6p2SxM0a9ZMNcbs4fuIiPTv3181pra7cSX43Rw8eLA1hvEoJHQwfbly5cqqsZyIiP2cxQrPJDLg8/naa6+1xvC36+WXX1YdKO7VZdu2bdYxdmp/5ZVXVLsxmx07dgzp/LGEOz2EEEII8QVc9BBCCCHEFyS9ewvTyhcsWGCNYdregw8+qDpYWh2ez92exa1yv1RnxqatbkNCBN0S559/vjWGael0Z6U26DrGiuYVK1a07EaMGKE6WLVtvAfd5qEIuk7KlSun+u6777bsrrvuOtWYak3CB8sRLFu2THWoJURI5HFdtS+88IJqdH3deOONlt2WLVuy/F5YcR9d2iIiRYoUUY2lKeKJP365CSGEEOJ7uOghhBBCiC9IevcWkpaWZh1jlHqo4NY4NicUsbNCsJlpKvPJJ5+odpvY1atXT3W3bt1Uuxla+fPnj9Lsoo+bJYS42YJEZObMmZn+vXv37tZx8eLFM7Vzs63QXYZNS93X471+2WWXqWaWUPhg89fff/9dNTZoFrGbDRcrVkz1jh07LLsHHnhAdf369VV36NDBsmNz4dBw75VQQy4uvfRS1W6lZmxiOmTIENXBmv0i7vOydevWqtHdjX+PNdzpIYQQQogv4KKHEEIIIb6Aix5CCCGE+IKUiumJBBs3blR9zjnnWGN+ieNBv+z8+fMD2qFvHztbd+nSJRrTiiiY/vzNN9+oxq7eInaafo0aNawx9HljLIOfwc7l1atXV33nnXeG9Po9e/ZYx4G+f/fcc4913Lx58xBnSBCsWt+7d29rDKvTb968OeA5jDGqMe6qcePGlt3cuXNVY6zWtGnTLDus3o0pz8TGLeGQN2/eLJ/DjbfEMi9YauTqq6+27Hbv3h3S+XGOPXv2VN2yZUvLLpaxd9zpIYQQQogv4KKHEEIIIb6A7i2xU7HXrFmj2q0u6RewKufatWtDeg1uT4abShkquGW6b98+a+ydd95RjemY7tYvurQ+/fRT1eiecVm0aJF1XLhwYdX333+/6kDp2KmI+/mvX79edYMGDVQHS+8/cuSI6meeecYa27Rpk+oLL7xQtVtpmYTHihUrVE+cONEaQ1dVMNAOnwNDhw617N5//33Vw4YNU41uNBHb9dWnT5+Q5uBHwnFnZQW831auXGmNYdr70qVLQzrfH3/8oXrevHkBzxdtuNNDCCGEEF/ARQ8hhBBCfAEXPYQQQgjxBYzpETuOB1Mz3TQ9v4AxOKF2RR83bpxqt4v99ddfH3AM3wtLnS9YsMCy++qrr1TPnj1bNcaQiIhs3bpVtRtblF3cFFHsXNy2bVvVforpccvO//XXX6rr1q2rGtOaReyO2xjHg2XwRewyEZjmWqBAgTBnTJAKFSqodts/BGvBEoiOHTuqxjY17nHJkiVVu+UMnnzySdXYUqROnTpZng+JDCVKlLCO8XncqFEj1T/88ENI53NLU8QS7vQQQgghxBdw0UMIIYQQX0D3lojMmTNHNVaRrV27djymE3fKlCmj+sEHH1T9yiuvWHbbtm1TvXPnTtUDBgyw7EaNGqX63HPPtcZw2xSrYbuVeEOtAIpuFEyfRXeKaxdqam6hQoWs47Jly6ouX758SOdINdzrhNvW6CpGF7KIyIgRI1SPHj064PmxurfbqZ1kH3RvVatWzRrDVOSTTjr+U4FuSxGRiy66SHX//v1VBytVcfPNN6t+6aWXrDGs9I7uErq3EgdMl580aZLq9u3bW3ZLliyJ2ZxChTs9hBBCCPEFXPQQQgghxBfQvSUib7/9tupevXqp9kuDURfcyr733ntV41a4iMjgwYNVo3vLrdKLx6FWeA6XXLlyqS5XrpzqqlWrWna4RY9VnF03GLrf2rVrZ42dfvrpqosWLRrmjJObvXv3BhzD7LaxY8daY24mXAYXXHCBdYzuVb/ej7Fi4MCB1nGPHj1UY3ai674OJ5MOryXeRyK2e2vVqlVZPjeJLdiI+fPPP7fGMJzhl19+UY2/MbGGOz2EEEII8QVc9BBCCCHEF3DRQwghhBBf4MuYnoULF1rH2D17woQJMZ5NYoNp31hZWUSkSpUqqrFr7o4dOyw7jJNxO/JiZc9AcR4idmxNhw4dVNeqVcuyQ18xvgY7BovY1Wf79u2r2u0GHukO8amGm+6Pn2vNmjVVn3nmmZYdxlRh7FWTJk0sO1Zejh1uBfqmTZuqxhRlt7p2drnyyiutYyyNsX379oi+F4ku+fPnt47xd+Gee+5R7cZxxRI+0QkhhBDiC7joIYQQQogvMKFWoxURMcZsE5F10ZsOyYSKnudFvIMlr2Xc4PVMHXgtU4uIX09ey7gR8FpmadFDCCGEEJKs0L1FCCGEEF/ARQ8hhBBCfEHKLXqMMbmNMd8aY5YaY34yxgxM/3sRY8xcY8zq9P8vHO+5khNjjHnZGLPVGLMc/namMeZrY8wyY8xsY8wp8ZwjCQ1jTHljzDxjzC/p92bP9L/z3kwy+JxNXYwx1Y0xS+B/fxljep34lclBysX0mGNFJPJ5nrfPGJNTRL4QkZ4icq2I7PQ870ljzIMiUtjzvD7xnCs5McaYC0Vkn4hM9DyvdvrfvhOR3p7nfWaMuVlEKnme93A850lOjDGmtIiU9jxvsTGmgIgsEpFWItJFeG8mFXzO+gNjTA4R+UNE6nuelxIB2Sm30+MdI6PDZc70/3kicrWIvJr+91fl2MOWJDie5y0QkZ3On6uLyIJ0PVdEWsd0UiQsPM/b5Hne4nS9V0R+EZGywnsz6eBz1jc0EZHfUmXBI5KCix6RY6tTY8wSEdkqInM9z1soIiU9z9skcuzhKyIlgp2DJDTLRaRlur5ORMrHcS4kDIwxaSJylojw3kxS+Jz1BTeIyJR4TyKSpOSix/O8I57n1RGRciJyrjGmdrznRCLKzSLSzRizSEQKiMi/cZ4PyQLGmPwiMl1Eenme91e850PCg8/Z1MYYc7Ic+4/LafGeSyRJyUVPBp7n7RaR+SJyuYhsSY8pyIgt2BrHqZFs4HneCs/zLvU872w59l8hv8V7TiQ00uM/povIJM/zZqT/mfdmEsPnbMpyhYgs9jxvS7wnEklSbtFjjClujCmUrvOISFMRWSEis0Skc7pZZxGZGZ8ZkuxijCmR/v//E5GHROSF+M6IhEJ68OtLIvKL53nDYYj3ZpLB56wvaCcp5toSSc3srTPkWABdDjm2qHvT87zHjDFFReRNEakgIutF5DrP89wAWZJgGGOmiEhjESkmIltE5BERyS8i3dJNZohIXy/VvsgpiDGmkYh8LiLLRORo+p/7ybG4Ht6bSQSfs6mNMSaviGwQkVM9z9sT7/lEkpRb9BBCCCGEZEbKubcIIYQQQjKDix5CCCGE+AIuegghhBDiC7joIYQQQogv4KKHEEIIIb6Aix5CCCGE+IKTsmJcrFgxLy0tLUpTIZmxdu1a2b59u4n0eaN5LQ8fPmwdY1mEY/XpjnPkyBHVf/11vCPBvn37LLu8efOqLlmyZETmGQ8WLVq03fO84pE+bzSv56FDhwIe586d2xr73/+y/t9Ru3fvVr1mzRpr7OSTT1Zds2ZN1e73KB4k470ZLnv2HC/VsnWrXWQZ7/f8+fOrLl26tGV30klZ+rmJOdG4NxPxWu7cebxsUs6cOa2xAgUKxHo6USHYtczStzAtLU2+//77yMyKhES9evWict5oXku8qURE/v33eGss98GHP3gfffSR6i+++MKyw8/hnnvuUZ0IP35ZwRgTlW7F0byeW7bYVeg3btyoukaNGtYYLk5DZcaMGaq7dOlijZUtW1b1N998o9p9WMeDZLw3swL+B8n777+v+tlnn7Xstm3bpvr8889X/cgjj1h2RYsWjfQUI0o07s1EvJZTphwvsuwuTJs0aRKzOUWTYNcysZfeJClxFyw33XSTavwvdxF7QbRr1y7VbtFMvFEXLFigGn8wRcLbaSDBwesiIjJ27FjVO3bssMamTp2qOtSFyamnnqra3VVCWEg1tuC9VKdOHdV4z4qILF68OFPtXv+nnnpKdY4cOSI2T/JfcJEjYj+D8T9i3OenH+AvBCGEEEJ8ARc9hBBCCPEFXPQQQgghxBcwpodEnNNPP906zpMnj+o//vgj2+cfNGiQasbwRJ/TTjvNOh45cqTqcuXKWWPLli1TXbdu3ZDOj9l4bqA7xib8888/qt3YMBJ5MEkAr/M555xj2c2bNy/T148bN846btSokeprrrkmElMkAXCDzefMmZOpzpcvX8zmlCjwF4MQQgghvoCLHkIIIYT4Arq3SMSpVKmSdTxr1izVmzdvtsYwjfX3339XvWHDBsuuUKFCqqtVqxaReZLwQBdUs2bNrLHPPvtM9SmnnKIa09JFbLdk8eLHa4hVqVLFsmvRooXqggULhjljEg4HDx5UjQUJv/zyy5Be7xYYHTp0qGp0deH1J+GDqehujaT27durPu+88wKeA6/5888/r/rrr7+27K644grVmA6fDHCnhxBCCCG+gIseQgghhPiChHVv/f3336rdCqDo6iCJT7AsHtxCrVWrlmosbS8i8uGHH6rGzB3MFhIRGTFihGp0hzRs2NCya9u27YmmnZJgBtSBAwesscKFC2f6GjfjDrez58+fb4299dZbqgcMGKB6/Pjxlt0NN9ygGt1lbjbJqlWrMp0TCQ+3Uu/evXtVY6sJEZE33nhDNVZadl3PofLtt9+qHj58uGrXFeP2cyOhsWjRItX4+ylitwBZsWKFatdVOXfuXNV4/V0wZGHJkiWq8fkrkpjZtYk3I0IIIYSQKMBFDyGEEEJ8ARc9hBBCCPEFCRPT43ZQvvXWW1X/8MMP1hjGcbgVXElig7EBIiKffPKJaowx6dixo2WHVZ67dOmi+rXXXrPsjh49mun7/vTTT9axX2N6MI24fv361hhWycV4qGnTpll2GBNQunRpa6xy5cqq77zzzkzPHQy30nKDBg1Ceh2xwXsJY2nc+wXH8LqK/DeWMrvgMx6remOatIjIww8/rBrjN7FCNPkv+/fvV33o0CFrDKvYjx49OtPXiIjUqFFDdbB4HLxmo0aNUu3GCY4dO/ZE04453OkhhBBCiC/goocQQgghviBhfEOHDx+2jr/66ivVbsosukQuu+yy6E6MZJs///xTdevWrQPaYXXfO+64wxrDKs87duxQfemll1p29erVU41bum5qu18pVqyY6n79+lljt912m+oSJUqodt0cmFK8e/duawy3s8OpnH377bdbx1j5lQTGTSPv0aOHakxFd11JoYLX3HVhhAOew01zxjIITz/9tOomTZpk+31TGXQtYxNfEZGdO3eqRtd1586dLbs2bdqoLlCggGo3jOSee+5R/fHHH6t2S1NgmIrbqDZecKeHEEIIIb6Aix5CCCGE+IKEcW/h9puIyKZNm1S7UftYEfbzzz9Xjdt7JHF4+eWXVa9du9Yaw0aUN954o+oLLrjAsqtevbrqMWPGqHazgrAqKbq3yDHwXnIbBV500UWqy5Urp/rXX3+17Jo2baq6d+/e1lh2m8FipWYSHKyEe+2111pja9asyfL53GrYtWvXVo2u0G7dull2GzduzPR8biZehw4dVM+ePaNLmpwAACAASURBVFv19u3bLTvM1sUMwHfffdeyq1q1aqbv61fOPPNM1b/88os1ho2e8d5GF1ZWwN9adG+5WdiYNUv3FiGEEEJIDOGihxBCCCG+gIseQgghhPiCsGN63DRWrADp+oYDgdVzd+3aZY2hb9CtLomdop988knVmN4okhjd2N0U0Vy5csVpJrEDfbwi/+3ki/z++++qu3btqtr1/77zzjuqy5QpE/B82DUar39aWlrgCQcBu3znyJHDGps8ebJqTOHMnz9/WO8Va9x/T6AYCfffg3FBGJNFos/KlStV33zzzapDjeFxnz9YEmDgwIHWWJUqVVSHk+ruxhk999xzmb7vAw88YNlh3N/q1atVX3LJJZbduHHjVLulSxKxu3e0wfu5cOHC1ph7nF0CxXElQ9Vs/30zCCGEEOJLuOghhBBCiC8I273lpsRdf/31qitUqKDadXVt2bIlU/33339bdujScrfMihcvrvqLL75Qja6uzI7jAaZcioh06tRJdaNGjVTnzJkzZnMKF9cFiVumWGEVG4KKiGzdujXgOcuWLav6+eefV41p0SIiefPmzfT169ats46xWjducWNjRRHbNdC8eXPVrivupZdeUu1WDcdmfZhGj+cT+W/F2WTjgw8+sI5PO+001S1atIj1dHwFumtFRHr16qXabcQcCq1atbKOsYI2Vup1mThxomq3Qj6C93P//v2tsTx58qhG15eb5ty9e3fV+OxwXSr4bMWyGCIiF198sepkcLkkOm7l9UAhC+5njc+KRIE7PYQQQgjxBVz0EEIIIcQXcNFDCCGEEF8QdkwPlrwWseMWHnzwQdU//vhjuG+huPEu6DccOnSo6iuvvDLb7xVpsPS6iB37VLNmTdWPPfaYZXfhhRdGd2Jh4Pr8sT1Bnz59VLs+f0xrdrtBY5mBli1bhjSP77//XrXbhdstaR+IV155RfWnn34a8PUYo+CCZRswzRbjgETsGLdkAWP2nnjiCWsMYykwLk/EjvNKltT9RMaNp3LLQQQCu2Jj+Yf27dtbdsHieLA0CMbbuTE4GLeJz4FatWoFPDemV7du3doa27Nnj2osBYExdCJ2PB928xaxW1bUqFEj4DySmSNHjljHGHMY6dIobkxksWLFVP/111+qMW5LRKRSpUoRnUck4E4PIYQQQnwBFz2EEEII8QUR67KObgasnDlr1izLDqt+YgdWl9y5c6t2U4AxPRi32RKRJk2aWMd16tRRjR3ie/ToYdlht/BEAdPSRWy3x4wZMwK+bufOnarRHSliu/uCged/5JFHVLvuKExTP+WUU1S7KZcIbpNfeuml1hi6F9x0TPwe3nXXXaqx0riIyOOPPx7wvRMJ/Izatm2r2i0n8dRTT6mePn26Nda4cWPVWBEbOzuL2KUb0BVDbFx3lls2IQP3M8QQA9SuayoYWP4BK0G7YImKO+64Q3WoqeJuZXA83/Lly1WPGjUq4DncitRvvvmm6oceeijgeyUzS5YssY47duyo+uqrr7bGsNp9ONXp3Q736NbG513p0qUtu0QsxcKdHkIIIYT4Ai56CCGEEOILorKvjJHj1113nTWG7oPZs2erdqPDsQKom8GwefNm1Ynu3nIZOXKkasx0QFeXyHH3lpuxEGuwIuzcuXOtsddeey2kc3Tu3Fl1z549rTF0R+HW/fz58y07zM7AytCY3SEict5556levHix6iFDhoQ0V9edgJ+/m400ZcqUTM/hZjBUq1ZN9dKlS0OaRzzA7yO6FdBNKCLy6KOPqna30TGj5JlnnlHtZoDhtvcNN9ygOpirC7fR3YqwDRo0yPTcyc7XX38dkh1WIBYR6du3r+pA1cxdMANRxG4EipmK5cuXt+ywgvLJJ58c0nsFA68fulJ//vlnyy5YJhu6XTE84uyzz872/BIFN4Ma70X83ETshs34exrM1bVv3z7V3333nTWGlZYxm7Zu3bqWXZEiRQKeP15wp4cQQgghvoCLHkIIIYT4Ai56CCGEEOILYp4rihVAMcUOffIidryE65P+5ptvVNeuXTvSU4wqWB0U40Xc9M7LLrtMRP7bZTnWYIq5m24eKH22TZs21vGwYcNUYwyPiP3vwziE5557zrLDEgYYa+DGimBKKsalhIpbPRlTfDGFV8Tuco2lCNxYA/Rru//+eIK+eBG7SjXG1uD1ExG55ZZbVAf794wePVq1WyEW40Dee++9TF8jIlK0aFHVeI+sWrXKssNYKYw3cEtG4Hc2GWJ/MK4iGO3atbOOQ43jwWq6999/vzWGVbnxOmMKuIhI9erVQ3qvcMDvzX333WeNYSV1t0wElkMZP368ardKND5Xkg03/g3jFt1nH6acY0X1OXPmWHb4/MQuAW5JAHxu42carKxAopA4T2BCCCGEkCjCRQ8hhBBCfEHClEJ1U+Jwu7Jq1arWGG5r4la2m47opg4nArhFj1v8N910k2WHTfdizfDhw1Xj9id+1iL2Fiq6tCZNmmTZBUtDxrRmdGmhu0jELn3Qr1+/gOfLLq7LDlPn3erR2JDx8ssvV40umUQGq9aK2C6MN954Q7Wblh4q6D5yK47jfbBw4ULVbgV3vC/wNehiE7Gr02KJBLdh7KZNm1S77hz8d2ITzXgSatNWTCkPxqFDh6xjdBW/9dZbAV93++23q8bPN5Zg41QRkcqVK6tevXq1NYa/H+jeclP7Q60InwxguESw0hj4+/n7779bY1hy4v/+7/9Uu+5SfPZhenzJkiWzMOP4wJ0eQgghhPgCLnoIIYQQ4gti7t7Cpoa4LfbCCy9Ydlh1F7e/RUSKFy+uetq0aaqx0amIyMsvv6za3eZOBBo2bKja3T7MyKrISoPAcPniiy+s4wEDBqgOVhH67rvvVo2ugqxUZUWXJGZSuFkWbuXlUMBr7rorMCsEv08lSpSw7DBjBLd0RexKy1ilOFlwm7BiVkfLli0j+l7oihARKVWqlGrcincrs4famBTdoVOnTlXdvn17y27FihWq3ergmHWH7jjX1RpLmjZtah0vW7YsUzvXpYyfG7r+sCK8iMjMmTMDvnezZs1UDx48WLWbiRcrXLcxuttd9xaCri7MSEs1MNMPG4K6oCt03Lhx1hjeA8Fcpr1791btNmlOdLjTQwghhBBfwEUPIYQQQnwBFz2EEEII8QVRienBtF+3ezj6yrHKY/369S07t1osgv7qt99+W/VHH31k2V1xxRWqP/vsM9WFCxcOeO5YgnENd911lzWWEe8UrS7rR44c0bR4NyU50HueddZZ1jGmm4fbXXnz5s2qsRsypk6K2PE0oZYi+Pvvv1UXK1bMGsMqydi92f13YOkAN2asRYsWquMV55AdHn/8cesYPxO3Qng4YCzFhg0brLFWrVqpxu/Rq6++atl16dJFtfuMCARWPXerY2NHdzd+COM9rrnmGtULFiyw7Nwu49EE7wkRkWeffVY1pp+7cXmNGzdWvW7dOtVbt24N+F74vBSxK3QnYrfs0qVLZ/k1O3fujMJMEoO5c+eqdksTBMKNpcW4IEw/d7u2428wPivcCuKhllyIJdzpIYQQQogv4KKHEEIIIb4gbPeWWzF49uzZqseOHava3XYNxMqVK63jPn36qMbUSRG7yShuf9erV8+yw2Z6WJUTq5AmCm4Tv4yKs1jpN5Js27ZNtzaDbfmefvrpqt2KreG6tJDWrVurxi3UX3/91bLDysiYHn/zzTdbduj6wvRc3OIXETlw4IDqihUrqsaGe66d26ASU/aTkWhUT0XX9oQJE1S7DYXxHkY3BaaUi/y3kWRWcdOcH3nkEdXucwWfQVgyA797IvbWfrSpWbOmdYyueXRVuaUt3Ar3GRQoUMA6xvvPdXcmenXd9evXZ/k1oVauTgbca/7+++9n+RzBGtpiGQ5sDi4S2P2dDE18udNDCCGEEF/ARQ8hhBBCfEGW3FubNm2SQYMGiYjIiy++aI257oOs0qhRI+s41KaS7777rmo3Yv3gwYOqMWsjGcjY8o/WdmHOnDkDbl9XqlRJ9TvvvKP61FNPjfg8ChUqpDpYpD9meWGV5Ndff92yS0tLU71x48aA50O3HboCXLfttddeq3rUqFHWWLAKwX4FM37wM0Z3lojtZkA3heuOwjGsYB4ueI4LLrjAGsPsF6zSHc/MPPceveqqq1TjZx0MvG/diszoPk9014TbDBizf0MlElmJicratWuzfQ68J2699VbV2Iw4GMmQxcqdHkIIIYT4Ai56CCGEEOILuOghhBBCiC/IUlBCrly5NGbCTaXE+BnsGN6uXTvLDisjYzo7xo6IiMyZM0e1W5V04sSJqh9++GHV2KVbxK42WaFCBSHHyZ8/v/pv3Q7mmJ4YjTgeBH3F2NkbO0MHw+2ujLE/GJvUpEkTyw6/D1i1103pxVIC2LXdz2Ds3NChQ60xrKR92223hXQ+rLbtxm24VYKzC5YkqFatmjWGzybsQO+mBscSNwYFSzRgRWm37AR2Z8f7262qnky45QtCrTqM1aTdrvXJjPt5VK9eXfXXX38d0jm6du1qHePvaarGLHKnhxBCCCG+gIseQgghhPiCLO1fFSlSRF0fboVGdG/hFrK7RfbSSy+pRvcWvt49f+XKla0xrESKabHuVrjrFiPHyZUrl36uw4cPj/NsjlGmTJmAYyVKlFCNW/QffvihZYcVRqtUqaLadbVkVLx2cd0riZ7GGwqYuo9b/eiGDsamTZusY2yO6zb0/PHHH0M657x581Rj+rrrTo1mw8KBAwdax7/99pvqDz74QPWyZcssu4xmqfFwe2FpD/wM3cq6mOoe6nVOdP7880/r2K2ejqDbvE2bNqoj7S6NJ8H+/cHALgbusx9TzvG54abDY6kRfDa792sifve400MIIYQQX8BFDyGEEEJ8QcTCs0OtxIhZNJgps3//fssOj93tZdyix6qRgVwWJHFZtGiR6gEDBgS0w+axwRqkYkVmzN5zswMDZYdhJpGI3QDTrSqcqLhul969e6vu0aOH6vPOOy/gObZv3676sssus8bwfhwzZow1hhlziPu5PvHEE5naudlFoVaCDQe3+jN+RyZPnqz6nHPOsewynnXxru6LVaNRpxJ4r1999dXWmJu5iWBYRY0aNVQnorslUuCzzwWbQ2Nm4pVXXmnZoTt/1apVqt1Gre79nIHr3rroootUY2hLPCs3c6eHEEIIIb6Aix5CCCGE+AIuegghhBDiC2JechH9jn379lU9fvx4yw795a6vcvDgwarPPffcyE6QRJX333/fOr733ntVY7oyliUQsVN1MU06X758lt3YsWNVly9fXvWzzz4b1nwfe+wx1W4JBLcCeKLw008/WcfTp09XjTFwLujr79Chg2o3pg7TfoPF0WFsUf/+/a2xBQsWqMbPsW3btgHPF20wzuCmm26K2zz8DsbvYXmEUMshiIjUrVtXdTy/U7EkWLVtjMlx4xsR/N3F+9d9zmKpGLTDKuEiIm+++abqRx99VDWWE4k13OkhhBBCiC/goocQQgghviCuHcXQvYWapBYHDhxQje4sETslHDnttNOsY6w+imnM3bt3t+zQFdq6dWvVu3btCn3CAKZrz5492xrDFFpMCY03L774onWMVabXrVun2v1Mrr/+etVz585V7bqXMZ07WMVqTFEdOXJkQDt0SbppySQ1QZfIwoULrTEssRBq40y3VMLLL7+sOlil91SiQYMGqq+55hprbPny5arR1YXPZhGRwoULq8YK5a57C5ud/vDDD6pPOeUUyw7LYhQsWDD4PyBGcKeHEEIIIb6Aix5CCCGE+AIuegghhBDiC+Ia00P8AaaguuXyA8X0uH/HY4wxceNsli5dqhr92OGC3bbnzJljjWEp9htvvDHb7xUpgpV4nzZtmurPP//cGsM4HqRdu3bWMfrmMfVcRGTcuHGqMVXebY2BKcUYfxDv1g4kemAneEybxrIQIsHbSyB16tRRjXFhInbrCb+A3c5nzJhhjWFcH7bowL+L2LE6jRs3Vv3NN99YdthuomfPnqq7detm2WHZkHC7wkca7vQQQgghxBdw0UMIIYQQX0D3Fok6uO3qVt5u2LCh6lDTyteuXRtwzK1GnIHbrTtPnjyq9+/fH/B86CJzz9G1a9dMxzp27BjwfLHArbr83HPPqf7yyy9Vo7vBBd1Mr732mjX21VdfqcYqzu7rLr74YtWlSpWy7Hr16qW6QIECAedBkhfXRY1uLHSzui4WBKt1u9X38Xtdq1atsOeZrBw6dMg6RreV6ybGMQRDD0REZs2apXrx4sWqu3TpYtk9/vjjqkuXLq3afUYmIok/Q0IIIYSQCMBFDyGEEEJ8Ad1bJKZUr17dOn7ggQdU9+vXT7Wb7ZNd3IrJmAGGVYVdVxe6aPbu3WuNHTx4UPXGjRsjMc2I4Dbza9Wqlervv/9e9cqVKy07rII9ZswY1W4l1UKFCqnG7AwRe3s7Gba6SfbBjB90ibhZf1jht1y5cqrLli1r2eEz4sILL1SNFcNF/lsl2G8Eq4YeKujuFrEbvGL1/EceecSyC5YhmujwqUQIIYQQX8BFDyGEEEJ8ARc9hBBCCPEFjOkhcQU7KmM3ZDfddfPmzardzsAI+rkxpRPjUERE+vTpoxrTYosUKRLwfMmCm66KcVPDhg1TnTdvXsvumWeeUY3VWAlBMJZNxC5pgBV5sWO3iB2TM3jwYNUVK1a07BKlcm+qgrF8bnmNW265RTWWGAiU8p6McKeHEEIIIb6Aix5CCCGE+AKTldRgY8w2EVkXvemQTKjoeV7xE5tlDV7LuMHrmTrwWqYWEb+evJZxI+C1zNKihxBCCCEkWaF7ixBCCCG+gIseQgghhPgCXyx6jDE5jDE/GGPmxHsuJHSMMbmNMd8aY5YaY34yxgxM//ujxpg/jDFL0v93ZbznSk6MMaa8MWaeMeaX9OvZM/3vRYwxc40xq9P/v/CJzkXiS6BrCeO9jTGeMaZYvOZIsob7O2mMedwY82P6M/YjY0yZE50jGfBFTI8x5l4RqScip3ie1zze8yGhYY4VnMnned4+Y0xOEflCRHqKyOUiss/zvGFBT0ASCmNMaREp7XneYmNMARFZJCKtRKSLiOz0PO9JY8yDIlLY87w+QU5F4kyga+l53s/GmPIi8qKInCYiZ3uetz2ecyWh4f5OGmNO8Tzvr/SxHiJS0/O8rnGdZARI+Z0eY0w5EblKjt2EJInwjrEv/TBn+v9Sf5Weoniet8nzvMXpeq+I/CIiZUXkahF5Nd3sVTm2ECIJTJBrKSIyQkQeEN6rSUNmv5MZC5508kmKXM+UX/SIyEg5dgMePZEhSTzSt1yXiMhWEZnred7C9KG707deX6Y7JPkwxqSJyFkislBESnqet0nk2I+piJSI38xIVsFraYxpKSJ/eJ63NK6TIlkl099JY8xgY8wGEekgIgPiMbFIk9KLHmNMcxHZ6nneonjPhYSH53lHPM+rIyLlRORcY0xtERkjIpVFpI6IbBKRZ4KcgiQYxpj8IjJdRHo5/zVJkgy8liJyWET6S4r8OPqFYL+Tnuf19zyvvIhMEpG7Yz65KJDSix4ROV9EWhpj1orIVBG5xBjzenynRMLB87zdIjJfRC73PG9L+mLoqIiMF5Fz4zo5EjLpsVnTRWSS53kz0v+8JT1GJCNWZGu85kdCJ5NrWVlEKonI0vRnbjkRWWyMKRW/WZIQCOV3crKItI71xKJBSi96PM/r63leOc/z0kTkBhH51PO8jid4GUkQjDHFjTGF0nUeEWkqIisyfiDTuUZElsdjfiRrpAemvyQiv3ieNxyGZolI53TdWURmxnpuJGtkdi09z1vmeV4Jz/PS0p+5G0Wkrud5m4OcisSZQL+TxpiqYNZSRFZkeoIkI3Vap5JUpLSIvGqMySHHFuhvep43xxjzmjGmjhwLrFsrInfEcY4kdM4XkU4isiw9TktEpJ+IPCkibxpjbhGR9SJyXZzmR0In02vped57cZwTiSxPGmOqy7E4n3UikvSZWyI+SVknhBBCCElp9xYhhBBCSAZc9BBCCCHEF3DRQwghhBBfwEUPIYQQQnwBFz2EEEII8QVc9BBCCCHEF2SpTk+xYsW8tLS0KE0lumBq/rG6WsnB2rVrZfv27RGfcLyu5dGjdgu0nTt3qt6zZ4/qvXv3Bnzd//53fK2eK1cuyy5Hjhyq8+fPrzpPnjyWHY7lzJkzpLlHgkWLFm33PK94pM+L13PDhg3WWPny5SP9dnHHLbWB35etW48XdD7ppMCPOPe7mC9fPtUFCxZUnTt37kxfn2r3pt+Jxr3Jaxkfgl3LLC160tLS5Pvvv4/MrGLMv//+qxp/GEXsH9FEWxDVq1cvKueN17Xcv3+/dTx16lTV7713vK7ZJ598YtkdPHhQNS5gqlSpYtkVKFBA9Xnnnae6Tp06lh2OlS5dWmKFMWZdNM6L1/Pee++1xp555nhrsnh9v91FSnbncejQIet43rx5qp999lnVRYsWDXgO97t47rnHu5m0aNFCdfXq1TN9fardm34nGvcmr2V8CHYt6d4ihBBCiC9IqTYU7rb+q6++qvrzzz9XXahQIcsOt7VxK7tixYqW3VVXXaW6du3a2Zusj3j//fdV9+3b1xpbunSpanRVXXTRRZbdqaeeqnrduuOL+IULF1p26C779NNPA84Jd3eaNWum+s4777TsGjRoEPAciUqpUnZ/x4EDB6oeMOB4A2zc4XTBnVF3VwXvl2Dgzt3dd9sNmm+++WbV/fv3V+3uAKEL6rvvvlN9zz33WHbbt29X3a5dO9WXX365Zffbb7+p7tSpkzU2bdo01Q8//LDqr7/+2rJzdw0JIckDd3oIIYQQ4gu46CGEEEKIL+CihxBCCCG+IClietzUUkxt/vjjj1U/8cQTlt2SJUuy/F4Yr+CmMg8aNEj1aaedprply5aW3a233qraja9ItOywSIExIG+99ZY11rVrV9VuKjrG7jz11FOq69ata9lh6vGRI0dUT58+3bK7/vrrQ5rvpk2bVE+cOFH1Rx99ZNnh8emnnx7SueNN7969rWOMp8HP9b777rPs1qxZo3rMmDGqx48fb9k1b9484HvjvTp06FDV//zzj2WHMTP4HShZsqRl1717d9UYW4MZWiIiHTp0UO1mZyIYG4aZfiIi+/btU33gwAHV+L0UEZk8eXLA8xNCEhvu9BBCCCHEF3DRQwghhBBfkLDurZ9//ln1yJEjrbEFCxaoXrt2rWosYBcMN+UWi93htvbhw4ctO9z+xoJTixYtsuxw+7tz587WGKbautWEkw0s7tatWzfVWCpAxP58e/ToYY0NGTJEdd68eUN6X3Rf/PjjjyG9xnUrusXyMti8ebN1jK4hLIAnEjzlO5648xo9erTqp59+WjW6jkTs64kVq3ft2hXwvdDVKCLy2muvqcaqxqtXr7bsqlatqnrw4MGqV65cadmhixk//7PPPjvgnIKBLi33/sbvBH6Gy5YtC+u9CCGJR2I+tQkhhBBCIgwXPYQQQgjxBTF3b2F2B1bjHTt2rGU3adIk1ehWCpfChQurbty4sTWGLjJ0b7iujkC4rpIVK1aoxiwVEbuacL9+/VQnQ1NINwMHq+q++eabqt3mnm+88Ybqpk2bWmPBMm0CgVlG6Lpx3xs/34YNG1p2WBkaK/26fPHFF6rxOyny34q+iQp+xg8++KBq1w2G1wldlMGqj7vZeOiq+vLLL1Wju0xE5K677lL9yCOPqMb+VyJ2D7ZQK0EHA7/DrmsOQbdaq1atsv2+hJDEgDs9hBBCCPEFXPQQQgghxBdw0UMIIYQQXxD1mB43HmfChAmqseNzsLTYcMGO6TiPOXPmWHbo28cUczem54cfflCNXZ3d1FfEHXvhhRdUL1++XDV+LiIilStXDnjOeOGmbGMcD6bf479RRKRFixbZfm/s9I3p8X/99Zdld+2116rG8gBuPMiUKVNU9+rVS7X73cAYNOzCLZI8MT2B2L17t3V85plnqg4Wx4M89NBD1jHG55QoUSLg6/Czw9dccskllt2oUaNU4/co1Pm5YKVprCIuYlerxjIZF1xwQVjvRUi8cL/bP/30k2r3vg9E2bJlVbudBU455ZRszC6+cKeHEEIIIb6Aix5CCCGE+IKouLe2bt2qGlNkRWy3AlY/zgqYTopb3tjo0wXTl4M1IsWU2Ztuuskaw21BTHPH6tEitqsKmySK2G4aTIfu2LGjZTdz5kwRCe46izVTp04NOHbllVeqdv8tkQDTnz/99FPV1apVs+xGjBihOliKM7oPX3zxRdVuSj26IN3Udvw+FCpUKOB7JSpuc89vvvkmpNehS9GtoPzMM8+EdA78XLHkwyuvvGLZFStWTDW6wN0moIFwq7Q///zzqvv06WONYUNhbHBLSDKwZcsW1bfccos1hl0M0IWMISAiIhdffLFqdHeffPLJlh3ei1ixHBs5i9jPmHr16ql2S1jEEu70EEIIIcQXcNFDCCGEEF8QlT1cdB+5LpFwXFoVKlSwju+8807VWNnVjSjHZpRuNWEEK9Oeeuqpqt2GoLhVh7p+/fqWXdu2bVW77r1nn3020zksXLjQOv7oo49E5L/ZSbEG3QPYZFXEdh+h+zASjTg3bNhgHaOrEd0h999/v2XnfldCAa8lZn+J2FkPmLEnYrtxk9G9hdkZIv+trhwIdAdi5pVI6E108d4866yzVGOGpIidQRlqQ1rEdZeh+w3vU5HwqoOTyIONbzEcwH2+J2rD31jhZhfj9/nzzz+3xipVqqQaq627n6nbmDmDjRs3WsfNmzdXje4tzHYVsd1nmHH50ksvWXZnnHFGpu8bDfz9rSGEEEKIb+CihxBCCCG+gIseQgghhPiCiMX0YCXb++67T3WwWBrE7cx9ww03qMaO2CJ2unEwvy52cf/jjz8C2mEcSCR8ixjr4sacE5cOswAAIABJREFU4OeEae9up/YPP/xQRET27NmT7flkB0yDxPmK2NcB0xvDBeN4unTpYo1hd3qsoNyhQ4dsvy9y0UUXWceYGu3Go7lVT5MNt2Ly+vXrVeP30f13Y/orXousgN3TMe6vXbt2lt3ixYtVhxPf5paxYCp6YvDxxx+rHjp0qDWG8SM7d+5UjXEpIiLt27dX7VZHL1y4cETmmcjgs1nkv+VREIzdwTibQDE8Lu7nicduHA+Cz45FixapduPpME6wUaNGIc0pXLjTQwghhBBfwEUPIYQQQnxB2Hu97lZz7969Va9atSqkc+DWGqahi9hbnqFuSbvbbJiu6jY+RdC15laezC5uCjW6A4K5Bl5//fWIziNccHsS00dFRMqUKaMaq2RnBazg2bhx40z/LiLSunVr1YMHD1btukWzi+tmxO+UuxWc7K4SrD4uYqesozv4q6++suzQ9RjMvYyfpZsOj+n+VatWVd2sWTPLDt1bpUuXDvhegUj2a5SIuA0r8RrVrFlTtdukEjn99NNVY6VeEbtaL16/3377zbL7v//7P9UffPCBNYaNZStWrBhwHsmMW005UINtEZECBQqoDrWsBOJWt8eU865du6qeO3duwHPg88Ct5I5uaPd5U6RIkaxN9gRwp4cQQgghvoCLHkIIIYT4Ai56CCGEEOILwnZ4Y3qryH/TmQOBfkdMT3XT0sPxxWekeWeAvuZglC9fXrVbljvSnH322aoxHiXU1P5YU7RoUdXoFxYR+fPPP1VjiwZ8jYgdC+O2srjuuutU43cI43tE7BivSMfxIJiGL/JfvzkSbhxTouD69suVK6caYyR+/fVXy+6JJ54I6fx43d2SEfgdQV8/psO7NGjQIKT3JZEB49l+//131dj6R0TkiiuuUO3et4HA1i9DhgwJ6TVuiYgjR46ofv/9962x66+/XnVGSx+R6D/fY4n7Gxlq+nkkwHZNb7/9turu3btbdm4bmEBgjM+gQYOssWHDhqmOROsR7vQQQgghxBdw0UMIIYQQX5BlH1LGVvTs2bOtv2M3bgTTD0VExo4dqxo7WgdzIwTj77//Vu1ukwaqZuxucd5yyy2qo91pGdMug3Wcz/g8An2usQJTFdH9IRK4A7K7zbp69WrVrhsTXVotW7ZUPXHiRMuuYMGCWZh1+Lhp+bil7n5Hw+n6nUi41wkr3mKXZrfadjhbzG66MX526JYOVlXWTWUl0eXxxx9X/fLLL6vGLt0iobu0skuwciLoYhMR6d+/v2qs7v/OO++EfM5EB8s+iAT/rcCwikiDvxHPPfecNYZuMHSLBwvncM+BZRDcCuvhwJ0eQgghhPgCLnoIIYQQ4gvCdm9h5k4o9hngFnq4Li0EXSehVoJ2K7s2adJEdaQj4N1/P1amxeh7161SrFgxEflvU7lYg9fokksuscaef/551bidvH//fstu0qRJqt1K3rj1jBVWY+XOcsGmeCK2C/Lyyy+3xlKtqSG6dqdMmaLabQgZDrt27bKO0VWFWZZuRWbM5sLXbN682bILVv032fj33381O9Z1x9SqVUv1BRdcoDoSbhp0YYmIPPnkk6pbtGihun79+tl+r0jj/pZgM13M7MIGwiIiDz30UHQnFmHw+Tlw4EBrLFgD5Ei4hULBzazt16+famweO3LkSMsOfyfdfwdmc+Ez2A23CBXu9BBCCCHEF3DRQwghhBBfwEUPIYQQQnxBlmJ6/vnnH/n5559FRGTJkiXWGPpUsVKmG9+B1RWxk3hWquxiqh76Nbdt2xbS693u0tGMH3H//VOnTlXtxvEgGfEVsayyeSIwpVzEjsHB6+qCZQtefPFFa6xTp06q45U+evjwYdUzZ860xvB7/fTTTwccSwXS0tJU161bV3Uk4mXcztxYTgLjplxfP6ZN473jVl/v3LlztueYKBw9elS7ZLtlODDG74wzzlDdpk0by65QoUKq8XvqfmcxdfjBBx+0xvCzx07a0ayIHi5ueRKsII1gd3ARkfvuuy9qc4oG99xzj2r3HsAYUbcsC6aOxxIsb/Hoo4+qdtcP8+bNC3iOdevWqZ42bZpq/CyyNKewXkUIIYQQkmRw0UMIIYQQX5Al99bu3btl1qxZIiLy5ZdfWmPohqldu7Zq3JoSsSs5Y9XXSy+9NOD7YuM7EbuJ2Zw5cwLaIQ0bNlTtpvqF09w0VDK2qTNwP49AZLgI3ZT3eOI2fDzttNNUr1ixQjW6SUTsNNHWrVtbY5FoIJddMBXaTRHG73Wqpai7oCvWLU+QXYK5Atu3b68aq6+K2KUQ3njjDdWffPKJZZdK7q3cuXPr5+C6adA9jBVuH3744Wy/b40aNazj2267TbVbWT9WYEkStxntd999p9q9NzFlfePGjardEIhgbpVEAUuDYEiIC/7+uaUfEsEliS43LIEgEvp1wPIZrksXG4cHI/6/OIQQQgghMYCLHkIIIYT4giz5dU455RStXjx+/HhrDBtHbtiwwXoNghUl3377bdVNmzYN+L5oJyIyfPhw1ZgB5WY64RYfbothVeho42aGValSRTVuu7pkVJt0K9nGE7fBJkbSY9Xl6667zrLDTKBEAbeCX3jhBdVuE9jq1aurxsZ6qcimTZtUu9cwu9x0003WMbp9r7nmmoCvQ1c5VgJ2t8MxYzTaTYNjieuW6N69u2rMfHzrrbcsO8z6CpTJ5OK6pQsUKBDqNKNG1apVVbvui8qVK6t2GxS72UEZuNm0iZK9hc+jjBCSDNDFG6zqMp7DbTCaSFnAIiJ16tSxjnF+wUI6MHtxzJgx1hi6e4PBnR5CCCGE+AIuegghhBDiC7joIYQQQogvyFJMT758+aRevXoiItKtWzdrDFMmMQ6lQoUKlh36HbH67VVXXWXZFS1aVPX9999vjWFFZgTjZURsH9+ZZ56Z6WuijdsN2u0ynoGb0nvXXXeJiMiAAQOiM7EIgPEWbuXYRAermWJsElayFbErBKdaBeaDBw9ax1h24OKLL47oe7llIcKppoplJ9zKzb/++qtqjMNKZfC76nbRvuyyyzLVeI1F7PgJN6YnEcpJIO7917hx40y1iEiZMmVU4+9Arly5Ap7D/WyiDcajjh49WjVWLhYR2bt3b0jnw+vlxvQkGu788J4N9TpguRqR0OOzEutbTQghhBASJbjoIYQQQogvyHIp4ox0UKzWKSKyfft21RMmTFD9448/WnaB0tHcFFlMO3XTDJHixYurfuihh6wxTJWOZsqe+2/C7Tls2icisnjx4kzP0a5dO+s4o9rkiBEjIjFF34Pp2CIiPXr0UI3pzu73Olil8GRn6dKl1nHJkiVVJ2J6PrrA3fsCq2r7xb0VDEzvxu/0vffeG/A1FStWjOqcYglW3cdK8jlz5rTssKwJlq6IBmvWrLGO77zzTtVYYRwbIGcF/L1r1KhRWOeIFW4pmwsuuEB1qO6tVatWWccZzdBPBHd6CCGEEOILuOghhBBCiC8Iu9OmW2kYt01xaxyrJ4sca1qaGW4l3GBglDpGursuolhVocQK1CIirVq1Uu1uweG2Hm5HYqVVkeOVWBMtgyKZwCzC66+/3hr77bffVN94442qBw0aZNml8ufvuojcJoWJxumnn67avS7YhNit/ux3OnTooBqb/4rY2aWJVP09u2B4RPPmzeM4k+O4nQUwgzQSYIjIySefHNFzRxustu52ewiEW8m9Vq1aIb0udZ/ohBBCCCEAFz2EEEII8QVc9BBCCCHEF4Qd0+NSokQJ1bfffrtqTGUXERk3bpxqrLAZKNYnM0qXLq0a/XpuOmI0wTR67IIrYqcmurEkffv2VY3VqgsXLhzpKfoS7N6NsTpffPGFZde1a1fVGHeWbL7wrPLnn3+qfuedd6yx5557LtbTyRL4jKlZs6Y1tn79etUYr4WduP0Kfm5vvPGGNTZ58mTVWLmZRJ633noroudzK1RfccUVET1/LGnfvr1qt8zNwoULVeN31K3A7KbBB4I7PYQQQgjxBVz0EEIIIcQXRMy9hWBlV6yMKWI3T8MUPtc1hc3Y3CZxffr0UY2urmiDVV+xeqe7ZdypUyfVzzzzjDXmNrQk2QPdGiIiDz74oOoFCxaoRreqiJ3WjOmtqc4HH3ygGpv/iiSXe+OSSy6xjseOHav6u+++U033ls2FF14Y9JhEj4xm3Rl8/fXXWT4Hut/dsIpQU7bDwQ1Twd8xt5lwOGSUaBH5bymNf//9V3Ukmj5zp4cQQgghvoCLHkIIIYT4gqi4txDXnTN48GDVa9euVT1//nzLDqspY3NIkf82hYwWboO4zp07q8YMkcsvv9yyGzJkiGq6syIDNnWdOXOm6qFDh1p2uA37+uuvq3arssaqWneigc38ypQpY425x4mM26B41KhRqjEj6YYbbojZnAgJhtu8GF2y6MJxSUtLUz1p0iTV55xzjmUX6erx6Cbu1q2bNYa/yZh5FYk5uOeIhEvLOn9Ez0YIIYQQkqBw0UMIIYQQX8BFDyGEEEJ8QdRjelwwxRx97yNHjrTsDh48qNpNe4+0j+/IkSOqly9frrpnz56W3caNG1V37NhRtdu5GCugksiAVTmx4ne1atUsO/R5n3vuudGfWJKB30033iWZ4pzca1uwYEHVn332meodO3ZYdkWLFo3uxAgJgFsxed68earx984FK/fHsgQDpqK7sZOYYn748GHVyVDRnjs9hBBCCPEFXPQQQgghxBfE3L2FlCpVSjWmeYvYKcqRTsX7+++/reOnn35a9cSJE1W7W+PYkLFDhw6q3YrRJPusWrXKOr711ltVY+rnI488YtlVrVo1uhNLcrp37646GbaiA+HO/cwzz1SNqbbTp0+37NA1SkgscSu/n3feeXGaSWicddZZ8Z5CVOBODyGEEEJ8ARc9hBBCCPEFXPQQQgghxBfENaYHcdNlI5E+i6W9N2zYoHru3LmWHcbqIG5n7jZt2qiOdJyRX8F0R+wA7pYBwGNM/UymNOtEIFXjz5o1a6Z6wYIFqqdOnWrZMaaHEH/DX25CCCGE+AIuegghhBDiCxLGvXXo0CHrGKskB6vAfPToUdXYYVvEToOfMWOG6p07d1p2tWrVUo3p625KIV1a2Wf9+vXW8cMPP6x69uzZqh977DHLji4tgmBJC5H/dpzOYOnSpdbxrl27VBcuXDjyEyOEJDT8FSeEEEKIL+CihxBCCCG+IK7urS1btqieM2eONYYNBK+++mpr7KefflKNGT/uVvbatWtVY6PTJk2aWHb9+/dXHcuGbqmK63qYNWuW6l69elljmzZtUv3KK6+oTuZmmCT6uN8HbEBaqVIl1WvWrLHs0LVN9xYh/oM7PYQQQgjxBVz0EEIIIcQXcNFDCCGEEF8Q15iekiVLqr7llltCfl2dOnUy1SQxWL16tXU8bNgw1RUqVLDG+vbtq7pt27aqGcNDROz4sEBaxI4BnDx5suoWLVpYdsWLF4/0FAkhSQR3egghhBDiC7joIYQQQogvMO42cVBjY7aJyLroTYdkQkXP8yK+J89rGTd4PVMHXsvUIuLXk9cybgS8llla9BBCCCGEJCt0bxFCCCHEF3DRQwghhBBfkHKLHmNMeWPMPGPML8aYn4wxPZ3x3sYYzxhTLF5zJKHBa5maGGNyGGN+MMbMgb91N8asTL/OT8VzfuTEBLo3jTGPGmP+MMYsSf/flfGeKzkxfrqeca3TEyUOi8h9nuctNsYUEJFFxpi5nuf9bIwpLyLNRGR9fKdIQoTXMjXpKSK/iMgpIiLGmItF5GoROcPzvIPGmBLxnBwJiUzvzfSxEZ7nDQvyWpJ4+OZ6ptxOj+d5mzzPW5yu98qxh2vZ9OERIvKAiDB6OwngtUw9jDHlROQqEXkR/nyniDzped5BERHP87bGY24kdE5wb5Ikw0/XM+UWPYgxJk1EzhKRhcaYliLyh+d5S4O+iCQkvJYpw0g5tlg9Cn+rJiIXGGMWGmM+M8acE5+pkXDAezP9T3cbY340xrxsjGEr+yQj1a9nyi56jDH5RWS6iPSSY1t3/UVkQFwnRcKC1zI1MMY0F5GtnuctcoZOEpHCItJARO4XkTcN+5AkBXhvep73l4iMEZHKIlJHRDaJyDNxnB7JIn64nim56DHG5JRjF26S53kz5NhFqyQiS40xa0WknIgsNsaUit8sSSjwWqYU54tIy/TrNlVELjHGvC4iG0VkhneMb+XYLhCD0xOcTO5N8Txvi+d5RzzPOyoi40Xk3HjOkYSOX65nyhUnTP8vxFdFZKfneb0C2KwVkXqe522P5dxI1uC1TF2MMY1FpLfnec2NMV1FpIzneQOMMdVE5BMRqeCl2sMphQh0bxpjSnuetyld3yMi9T3PuyFO0yQh4qfrmYrZW+eLSCcRWWaMWZL+t36e570XxzmR8OC19Acvi8jLxpjlIvKviHTmgifhyfTeFJF2xpg6cizBYK2I3BGf6ZEs4pvrmXI7PYQQQgghmZGSMT2EEEIIIS5c9BBCCCHEF3DRQwghhBBfwEUPIYQQQnwBFz2EEEII8QVc9BBCCCHEF2SpTk+xYsW8tLS0KE2FZMbatWtl+/btES/JX7RoUa9ixYoiInLgwAFrDDsAHD16vEWSa5c/f37VuXLlivQUU5JFixZt9zyveKTPG697E78fIiL/+1/2/jtq5cqV1vG+fftUV6hQQXXx4hH/CLNMtO5NPmfjQzTuTb9dyxUrVljHuXPnVh3LzyHYtczSoictLU2+//77yMyKhES9evWict6KFSvKZ599JiIiq1evtsZy5Mih+p9//lG9atUqy+68885TXaVKlWhMM+UwxqyLxnnjdW/u37/fOs6XL1+Wz4G1who3bmyNLViwQHWfPn1U33XXXVl+n0gTrXuTz9n4EI1702/XEn8TRERq1Kih+qWXXorZPIJdy1SsyExC4ODBg7JmzRoREXn66aetsc8//1z11q1bVR86dMiyw52esWPHqm7Xrp1lF6x35IYNG1TnzJlTdalSsWul9e+//6o++eSTY/a+qQD+l1y44MJp2bJlAe3cXSWSeOB/GGU8XzK47LLLIvpeuFhmf9r48e2336r+7rvvrLHu3bvHejonhDE9hBBCCPEFXPQQQgghxBdw0UMIIYQQX8CYHp+SJ08eOeOMM0REZPLkydbYnj17VA8ZMkT1qFGjLDvMrEHfbd26dS27qlWrqp42bZo1du+996rGoLcZM2ZYdgULFgzwLwmNI0eOWMcYrH3SScdvA7cBL2MFgoOfY7gcPnxYNX73XPLmzZvt9yKRZ9euXaoHDRqkul+/flF931BjetDOjQvD7x4Jjzlz5qh2YzEvvfTSWE/nhHCnhxBCCCG+gIseQgghhPgCurcktqmPgbZaI+EmCBf331yoUCHVTz75pGq33sSnn36qeufOnaoHDx4c8Hzjxo2zxrCYXZ48eVS76fHZBbfgRURmz56tet264yUd3OJ4lSpVUu3WZTn11FNV16xZUzXT3rMGfneCpaU3bNgwFtMhWQRrKZUrV0519erVo/q+oRbCxOeb+5yN53M3mcH6bRiy4KaoFy1aNGZzChXu9BBCCCHEF3DRQwghhBBfEHP3Frp3pkyZotqt5Fi4cGHVN954ozWWTL1MsKKxiMiIESNUf/XVV6pbt25t2TVr1kxE/tvvKtbg1jC6c0Rs9xby+uuvh3z+Vq1aqZ46darqcHt5YXXlJ554QvWWLVssuyVLlqj+/fffVbvXC3HdgJUrV1aNVa3djAVmHQVn27ZtIdnt3r1bNbPs4oebYYfV2F977TXVvCapy6OPPqoan59uNf5EhDs9hBBCCPEFXPQQQgghxBdw0UMIIYQQXxDzmJ4///xT9d13360a/fUits/erc67aNEi1ZFIOcyu79mt9vvJJ5+o7tWrlzX2yy+/ZHoOTPsUOV7V2O1UHGswpghjWLICpnBfcskl1thtt92mOtw4nkDv1b9/f9Vueit+v/744w/Vd9xxh2X34Ycfqs6XL581ds0116i++OKLVad6DA+mlYeaNhyMDRs2BBzD7wReWze1PZapx+797jfcyuwYj5iIKcok+6xevdo6njBhguoGDRqoxpIFiQp3egghhBDiC7joIYQQQogviLp7a8eOHdYxpqlj6qObgopg9UeRxKiiiVvc2JRTxK5IHCzlPHfu3Krdar+33nqriNipgZEm49/gbld/8MEHqtEd56Z9B+Kmm26yjrGpqFulFZt9RpqcOXOGZFehQgXVGaUCMkD3Vvny5a0xrFYdCTdPsoD/1kikjp911lkBxw4ePKgaU9vdRpGxfCZkuNaCPbNSDaxaPm/ePGvMDT8gqQF+vwcMGGCNYWmPYcOGqU6GMgX+eVITQgghxNdw0UMIIYQQX5Al38K2bdu0YeTzzz9vjRUpUkQ1VlP+6aefLDuMAg/WXBDBDJ94smrVKtWvvPKK6uHDh1t2WBXYzUjCpon33Xef6qZNm1p2Ga6v0aNHZ2PGwclwU7hZSZhNs3//ftWhNgF1XXW1a9cO6XXosoim2yvY+7pVpx9++GHVbdq0scb85NIKhJvJFM51K168uOr8+fNbY/v27VONz5V4bqNnuE2TYSs/O6B7A7NQu3TpYtlhQ2GSOmAjYAx5EBGpX7++6mSowozwqU0IIYQQX8BFDyGEEEJ8ARc9hBBCCPEFWXLAFylSRNq2bSsidodwEbvjNla4DTVuJxgdOnTI9jnC4f3337eO27dvr9qtII3kyZNHtevvxPT2EiVKZHeK2SIjJuH222+3/o5d7TGmwk1bHDNmTKbnDVZhNxixjONBMLUdqyxndkxsInHN8PPHuB0R+/uH5S+wOnO0cZ9hqR7Lk8H8+fNVY7mKjN+ARMKNNwy1XAUJzKRJk1S7v3dYuT5ez+1w+f/27j3Oxmr/A/hnueZ+yf2uJFRyS6QSKZ2Uozo5FKlUVE6OSqSLTroQDopEktxyyS2X/ChKCSGEo+RWqmEGRTVF9Pz+MPPtu1azpz179v35vF+v8zqfPc+aPcs8e+95etZa38U7PUREROQLvOghIiIiX8jRfam8efPK8sSJEydax/Rwlz7m3oa+8cYbJe/Zs0eyrtoL2FWY3WGVAQMGSK5Zs2bQ/Q+GrsDboUMH65heiq6X6LtLmXV/K1WqZB1LhFvjulK0zi1btrTaBRreipdKtfp8uVWnixUrJllXkI7msAmdpoeD3dfY1KlTJceqRIBfShMcO3bMeqzLNeiyHPozIV4k2hBLvNLDySNGjJDsll5p3Lhx1PoUbv54NxMREZHv8aKHiIiIfIEXPUREROQLIQ+EuuPcl156aZY5WO6YoS57PmHCBOuY3ql94cKFklu1apXjnwvYcz/0HAL9dQDo1q2b5IcfflhysNssJDp3PpJ+rOfxHDhwIGp9cqWnp0t+7rnnJOvzCthzFLi8NX40aNDAeqzP26xZsyRfc801UetTMtPv2wceeMA61rZtW8mNGjWKWp9CkQhzJROBntP61VdfSe7UqZPVrn79+lHrU7jxTg8RERH5Ai96iIiIyBfiZp2fXjYMAOedd55k99ba3r17JeshpyVLlljtgh120jum6yqU7nDZ4MGDJVeoUCGo504m33//vfVY3xrXS8BbtGgRtT659NDnkCFDJPft29dqpyst89Z4/ChTpkzAY6FUMHerKesh63hceh1tevhwxowZ1rG33npL8oIFCyRv2LDBaqfLkOjdt6Pp1KlT1uO8efPGpB+Jxq1k/fjjj0vW5Tv69+8ftT5FGu/0EBERkS/wooeIiIh8IW6Gt1xNmzaVvHjxYuvY1VdfLVlvbvnCCy9Y7d544w3JeggjNTXVaqeHQa644grJ06dPt9r5cUhLO3r0aMBjP/74o+SiRYtGozsA7AqigL3ST9/yvuyyy6x2HNKKT9kNSb/33nuSgx3OcFeZBlu5N7uq4on82tGbQQP2Ktnjx49bx66//vqgnnPdunWS9TmK5hATh7NCs3nzZuvxrl27JOvPzAsuuCBqfYo03ukhIiIiX+BFDxEREfkCL3qIiIjIF+J2To9Wt25d6/GUKVMkt27dWvLbb79ttdu5c6dkPQfAXR6vx7n18nW/z+FxrV27NuAxPXeiZph3vs+OXqIO2PMLSpQoIdl9DVF8cpes62rZKSkpkt3yCdktddeCndOTyPN2XHp+krv0+MiRI5KbNWtmHevevbtkXalXL2UH7Mq9egk059nEJ/16ePHFF61jJ0+elPzggw9KTqb3A+/0EBERkS/wooeIiIh8ISGGt1y64q9eYr5y5Uqr3aOPPip5+/btkvWyPAAYOnSo5Msvvzxc3Uw6+la4q3DhwpKrVq0aje4AAFavXm09DlQlumzZslHrE4WuSpUq1uMLL7xQsq4EvGjRIqvd7bffHtF+JbJt27ZJnjdvnnWsffv2knU1esAuPVG9enXJ7vBWWlqa5GPHjklOhIrXeoNiv1i/fr3kmTNnWsdq1KghuWXLltHqUlTxTg8RERH5Ai96iIiIyBcScnhLr+jQm4K6w1v6Vq6efd67d2+r3X333ZdlO7Jv/+7evTtgOz0sUbp06Yj2SXM3N9VVuA8dOiR5zZo1Vrs2bdpEtmMUEnfFj16dqYe33ErcZNOrVe+55x7JbmXduXPnSs5utdXPP/8c1DH9GRHKBrGRpivHA8Btt90Wo55Elx72f+qppyS7G47qjZn19IBkwjs9RERE5Au86CEiIiJf4EUPERER+UJCzunRu31PnTo1qO+5+OKLJQ8cONA6lghLK2Nly5Ytkg8cOBCwnV6y7u5sHUkVK1a0Hus5Wb/88ovk0aNHW+2uvPLKLL+H4kvt2rWz/Hp2r0WyK5XrSuqDBg2y2gVbNblgwYJBtdNV8Js3bx7U90Safq24pQ30XJdk9tFHH0leunSp5MqVK1vtOnbsGLU+xQrv9BAREZEv8KKHiIiIfCEQDRggAAAgAElEQVQhh7d0RVB9O9WlNxd84oknJJcsWTIyHUtCupqr3ozOddZZZ0kuUKBARPuk6YrcANCzZ0/JevPYFStWWO1GjRolWS9bLVWqlNWOQ1+xdfbZZ0vWw9ANGjSIRXfi1o4dO6zHvXr1kqzfj507dw7p+Rs1aiTZXcqsl4EvWbJEsrscPJLvJV0JGgDGjBkjediwYZLdjWl1iYtly5ZFqHfR5y5F17sT6CG98uXLW+2+++47yfrvZLAb9SYC3ukhIiIiX+BFDxEREflCQt6z0pU+9S3vX3/91WqnVxHpaq7upoZ6aEZXF3UrC+tK0H7h3jYP5KKLLpIczSEhvSkiALz00kuSdTVffXsXAB588EHJs2bNkjxkyBCrXdOmTSW7w3Yc+oq8c845R7L+/W/cuNFqd/XVV0v+4YcfJJ84ccJq9/vvv0suUaKEdUx/riTCudVDGA899JB1TK9w1dXHa9asGdLP0r+btm3bWsf0dIP58+dLHjx4sNXuxhtvlHzuuedKdldQ6XP07bffSnbPuV6R5G6Cqv/9ffr0kXzXXXdZ7dzVS8li4cKF1mN3Y+ZMn376qfW4YcOGkvVnX79+/ax2HTp0yG0XY4Z3eoiIiMgXeNFDREREvsCLHiIiIvKFhJzTo8eU9Ri1O/9Ej+frKszBVmTW81QAYNGiRZKLFy+egx4nFj3G/vnnnwf1PZdcckmkupMjusLsP/7xD8mXX3651U6/BqZMmSK5ffv2Vjs936t+/frWsVtvvVWyXtJ5/vnnW+3cZbIUmLsLtl4CrV+Xw4cPt9rpuRl6h/HZs2db7Q4fPixZV+wG7NdEt27dJEezwnhOjBw5UvI777xjHdOvR/27CvXfouc46d8NYM+n0Z+5AwYMsNo9++yzkvUcLD3/BrDn8Xz99deS9VwfAGjVqpXkZ555xjqmP4902YNkppft69eGS78GFixYYB2bO3euZF3yw50zxTk9RERERHGOFz1ERETkCwk5vKWXrv7nP/+RfMcdd1jt9PLz7Bw/flyyvoW+fv16q924ceMk9+3bN7jOJqCtW7dK/vDDDwO208OC7vLfeKOX3AJ2xVZ9u/7pp5+22unN+dzlnXroRG+42rhxY6vdww8/HEKP/UMPibgbHurfv+YOL+sKxFrv3r0D/tz//e9/1uMmTZpI1p8JnTp1strFsqL7ypUrJT/55JMB291yyy2S3WHZ3NKb9QLAU089JXnVqlWSjxw5EvA5dHmRsmXLWsf0+0f33a2+XqlSJcnxOgQZSe5Sf115Wm8y69LlOtq1a2cd07/7xYsXS05JSQm5n/HGf68UIiIi8iVe9BAREZEvJOTwlnbTTTdJ1pulAUD//v0lu9WaNX1bO7sNNlNTU0PuZyLZsmWL5J9++ilgO71KqXbt2hHtU7jp2+HNmjWTPGfOHKudfg1NnDjROqZ/N3qIxh0S1NVn6bT33ntPsl6VE2glpUsPJ4bKPS96GOvee++V/OKLL1rtMoe93dVEkbB3717rcffu3SXrzzR3o1x3hWo4FSpUyHqsV2llN+Smh2P8OBwVbtOmTbMe62rybkVxPSSrP9PcdhUrVpSsV7x+8MEHVjtdDTzRdirgK4+IiIh8gRc9RERE5Au86CEiIiJfSPg5PXps+IEHHrCO6XHuHj16SHbn92zevFmyrt5Zp04dq12gZbHJxq2+GcjNN98suWDBgpHqTlS58xWef/55ye7y5xUrVkjWr6Fq1apZ7fTrZtSoUWHpZ07o0g0vvPCC5EaNGlnt/v73v+f6Z+lqyHoegPvv1nMC9Ott3bp1Vju9a3fRokUlv/rqq7nuq67eDQCdO3eWrKvRupXeM+f0BFsSI6dOnTolVYr1zuTAn+f4ZHJ3NNeVxCMt2DkdibBzfbzTFcvdeVt6XqE7N04vZz/zzDOD+lktW7aU7M511PM+9ZzYRMA7PUREROQLvOghIiIiX0j44S3NvX2qb1frKs6TJ0+22ukqp4888kiW3w8Ev5w20aSlpVmP3SGGTHpTRwBo06ZNxPoUL/Q5d4cMojmEkBNuFWM97Lt///6A36eXaevNWt2Nd/Vwhh6mAuxl33rzwq5du1rt9JDfE088IVlXys6uf24l2VDs27fPeqw3kNXc5dWZ1W4jNbyVmpoqw4F62NSlhye7dOkSkb4EQy9FD2UIy60sHOg50tPTrcfhKFuQaPTfpz179ljHSpcuLVkPzwJ/3kg5GLoCtjsUrIf2ObxFREREFId40UNERES+wIseIiIi8oWkmtPj0nMP9FyD66+/3mqn57SUKVNGcrLO4XG5JcYPHjyYZTtdAh/485LnZJQo5fL1Mmf3fOptMfRrfePGjVa7jz/+WLLe7bx58+ZWuwsvvFByvnz2R0ig+WBvvvmm9XjKlClZtnMVK1ZM8v333y852Lkj7lYRemf16667zjqmfzf6vLdu3dpql7lL9ezZs4PqQ0799NNPWL16dZbHdL+GDh0qOZbzW/RSaT130j1Hev6XniPibnVTvHjxLH+O+2/U5RFc7hyURKb/nfPmzZPslgooUqRIwGOhqFWrlmS9oz0AvPLKK5L134Vgl8PHUmJ8ohMRERHlEi96iIiIyBeSengrEH0bMKvHfhOoyitgV7WO5bJYyl6ePHmkmrS707WuMl2+fHnJ559/vtWubdu2kvUwla6KDNi3vR9++GHrmH7+6dOnS965c6fV7uuvv87y36GrLgPAyy+/LLl69epZfo/r+++/l+z+LiZMmCDZrcyuX+stWrSQrG/lA38M4USqwnCJEiVk6G358uXWsfvuuy/LPsZSsNXYN2zYIFm/TqpUqRLSz/VLhWc9VKfLhqSmplrtLr30Usl6h/RQ6ekdenoIYFd2zxzuBezKzwBQtmxZyVu3bpU8btw4q51+n5YrVy7EHgeHd3qIiIjIF3jRQ0RERL7gy+EtsmW30kHfutTDGhRfjDEy7OJWXf72228l69VLhw8fttrpYRy9UmbhwoVWO12J2129dccdd2SZ3RU6etjm0KFDkt2VYueddx6y8t1331mPn376acmLFy+W7K7wqVevnmS9YS5gb+55zjnnSI72MEqZMmXkd6eH6gDg3//+t+R43+TXrbRctWpVyfp1E+pKo0RZWRlOJUqUkOyuLr722mslh3vKhq4EDQDjx4+XrHc4WLZsmdVOV2vetGmT5JSUFKtdnz59JHN4i4iIiCgMeNFDREREvsCLHiIiIvIFzukhaywYsJdC6jkEflkimog8z5PKuP369bOOrVy5UvKBAwckX3XVVVa7u+++W7KufqyrIofKXYp+ww035Or5Ro8ebT3W/xY9D61ChQpWOz3XIV6r9ubJk0d+X+6S+0Tifl6EujSd/qDLR7jz1XTJhXBzKy337NlT8uDBgyXrzxcA+OijjyS3a9dOslv+5Oyzzw5LP4PBOz1ERETkC7zoISIiIl8w7rLCbBsbkwbgq8h1h7JQ3fO8sn/dLGd4LmOG5zN58Fwml7CfT57LmAl4LnN00UNERESUqDi8RURERL7Aix4iIiLyhaS96DHG5DXGbDLGLFJf+5cx5gtjzHZjzAvZfT/FB2PMRGNMqjFmm/raU8aYb40xmzP+d212z0HxwRhT1Riz0hizI+M92Dvj6zPVudxnjNkc675ScNzPWZ7LxOWXc5nMdXp6A9gBoDgAGGNaAfg7gPqe5x03xkR2gw8Kl0kARgOY7Hx9hOd5w6LfHcqFkwAe8jzvU2NMMQAbjTHLPc/7Z2YDY8xwAEdj1kPKKetzlucyofniXCblnR5jTBUA7QBMUF++F8Bgz/OOA4DnealZfS/FF8/zVgE4Eut+UO55npfied6nGflHnP6ArZx53JyuZtcRwJux6SHlRIDP2cxjPJcJxE/nMikvegCMBPAIgN/V12oDuMwYs84Y84Ex5qLYdI3CpJcx5rOM4a/IlSKliDDG1ADQEMA69eXLABz0PO/LWPSJciyrz9lMPJeJxTfnMukueowx1wFI9Txvo3MoH4BSAJoB6AtgluG+ColqLICzATQAkAJgeGy7QzlhjCkKYA6Af3ued0wd6owk+a/JZJfN52wmnssE4bdzmYxzeloAaJ8xufUMAMWNMVMBfANgrne6MNEnxpjfAZQBkBa7rlIoPM87mJmNMa8CWJRNc4ojxpj8OH3BM83zvLnq6/kA3Aigcaz6RjmS5ees53ldeC4Tjq/OZdLd6fE871HP86p4nlcDQCcAKzzP6wJgPoDWAGCMqQ2gAIBDMesohcwYU1E9vAHAtkBtKX5k3Fl9DcAOz/P+6xxuA+Bzz/O+iX7PKKey+ZwFeC4Tit/OZTLe6QlkIoCJGUufTwDo5rEcddwzxrwJ4AoAZYwx3wAYCOAKY0wDAB6AfQB6xKyDlBMtAHQFsFUtfx3ged4SnP6wTZpb6D7Hc5k8ku5cchsKIiIi8oWkG94iIiIiygoveoiIiMgXeNFDREREvsCLHiIiIvIFXvQQERGRL/Cih4iIiHwhR3V6ypQp49WoUSNCXaGs7Nu3D4cOHQr7dhk8l7GxcePGQ57nlQ3388bL+UxPT5dcqFAhye6OL4cPH5acJ88f/+1VqlTibKOW7O/N3377TXLevHkl6/OVE0eO/LFvcMGCBSUXKVIkpOcLt0i8N+PlXPpNducyRxc9NWrUwIYNG8LTKwpKkyZNIvK8PJexYYz5KhLPGy/nc9OmTZLr1q0r+YwzzrDaTZo0SbK+OPrnP/8Zuc6FWbK/Nw8elN1eULhwYcnFihUL6flmzJghWV8INGvWLKTnC7dIvDfj5Vz6TXbnksNbRERE5At+2oaCiCJszZo1ksePHy/ZHbYaNmyYZD3U0bx5c6tdtWrVwt1FCtLMmTMl33zzzZJDvdPTqVOnXPeJKLd4p4eIiIh8gRc9RERE5Au86CEiIiJf4JweSlq//PKL5F9//dU6lj9/fslFixaNWp+SXaNGjST36tVLsud5Ab+nTZs2kkuUKBGZjtFf0kvUAWDjxo2SW7RoIblixYpR6xNRuPFODxEREfkCL3qIiIjIFzi8RUklJSVFcqtWrSS7VV+HDBkiWQ+vUO7o4a3bb79d8vz58612FSpUkDxhwgTJ8VKd14/c4a2dO3dKXrduneTGjRtHrU9E4cY7PUREROQLvOghIiIiX0iq4S13hYh7uzaTu2FevnxJ9WtIevo879u3zzp2zTXXSC5ZsqTkRYsWWe3Klg37np8EoECBApJHjBgheffu3Va7Y8eOST7zzDMlZ7fK68SJE5L15pWAPVxGoTl16pT1uHjx4pKnT58uuWPHjla7MmXKRLZjRGHEOz1ERETkC7zoISIiIl/gRQ8RERH5QlJNZhk3bpz1+N57782ynV5KCwCvv/56pLpEYZKeni554MCBkkePHm2169Gjh+RBgwZJDnVnaAqdroKt5+MA9rw6Y4xkXSnbpSts63lAFB7u714/Pnr0qOQ+ffpY7caMGSNZzwMiCkVqaqr1eM2aNZLr1asnuUaNGla77D47NN7pISIiIl/gRQ8RERH5QlINb61cuTLgMX0r7PHHH49Cbyg30tLSrMc9e/aUPHfuXMnVq1e32ulKywULFoxQ7ygY5cqVk+wOR+kl7HqZe3a4GWlkue+X33//XfK2bdskf/HFF1Y7PXQ8fPhwyYUKFQp3FymB6ZII7mtoyZIlksePH28d+/LLL7N8vvLly1uP69evH1Q/eKeHiIiIfIEXPUREROQLCTm8pW+7Dh06VPLixYutdrrq7pQpUySfffbZEexdeLjVUf1my5Yt1uN58+Zl2e66666zHnNIK7qWLl1qPdarfPSqrO3bt1vt9Mou/VrPmzdvuLtIQdLnCwC6du0q+d133w34fWPHjpV8ySWXSO7SpUsYe0e5cfz4ccn6/Va4cOGw/hz9vgaAadOmSR41apRkvZmt2z+X3oRYb3bbrFkzq13p0qUlL1++PODz8U4PERER+QIveoiIiMgXeNFDREREvpAQc3pOnjxpPV69erXkRx99VLK7fPmdd96RXKdOnQj1LjL8Prfh/ffftx4H2n3b77+nWNuzZ4/1+P777w/q++666y7JoZxD9zMhX76E+ChLKDfddJPkDz/8UPIrr7wS8Hs2btwomXN64ocuC+HO3QqGnqsHAOvXr5e8YMECyW+//XbA72vfvr1kXVkZAGbOnCm5Vq1a1jH9t6By5cpB9bd///4Bj/FODxEREfkCL3qIiIjIF+L2nrBe+qY3tAPsisq60vJ7771ntatZs2ZkOkcR595ODeSTTz6xHnP5c3TdcMMN1uN+/fplmd2KzLkd+uC5jTw9JKI/cw8dOmS109MI3NcDxQc9pKVLvvzwww9Wu++++07y9OnTJU+dOtVqt3//fskVKlSQXLduXatdhw4dJOvhUr2UHbBLkowYMcI6FuyQVrB4p4eIiIh8gRc9RERE5Au86CEiIiJfiJs5PXqcEQC6desmedasWdaxpk2bStZL3fT8HkpsTZo0sR7r+QV6rta6deusdt98841kt4QBhZ8ezwfs0hB6XtZjjz1mtQt22ayeo7V3717JibCVTDLRcz9q165tHdPlA/RO1/v27bPa6fIG+ry2bNnSaqff6xQat8THrl27JPfo0UPyxx9/bLX77bffJOu/ye3atbPaDRs2THKrVq0kFytWzGp34sQJyfnz588yA0CJEiUkX3HFFYgk3ukhIiIiX+BFDxEREflCTIe39O2zp59+2jo2e/ZsyS1atLCOLVq0SHLJkiUj1DuKJXf39GrVqkn+4osvAn7f3XffLVm/hvTtUwofd5hK73ys36fPPvus1S7QEMYvv/xiPX7ooYckv/baa5Jffvllq1337t2D7DEFKz09XbJesu4uc969e7fke++9V7Je/gwAq1atkqzPf+/eva12+mcVL148p932DX1+APv3O2nSJOuYrprs7oSu6fdz3759JQ8aNMhqV7BgwaD6eMYZZ0jWf+915W4AyJPnj/svgarvhwvv9BAREZEv8KKHiIiIfCGmw1t6VZZ7+0zf4uratat1jENayc+t4KtXHOjbrq7ly5dL1tWar7rqqjD2jgLRFVj1ENSWLVusdhdddJFkfbvdXSWycuVKyfoW+MGDB3PfWcrW1q1bJX/wwQeS3WGVwoULS9bDFiNHjrTa6Yq8Y8eOlaxXAgH2Kr0ZM2ZI9ksVbr0CFbA38Vy7dm2WXwfs4aPGjRtbx/TK5s8//1xyoUKFrHajRo2SfOedd0oOx+/++++/l7xixQrrmF71WbRo0Vz/rOzwTg8RERH5Ai96iIiIyBd40UNERES+EPE5Pe7yM70T+gMPPCDZrcis/etf/7Iejxs3TrIeu9TjxACQL1/cFJymXLrjjjskr169WvL8+fMDfs8TTzwhuVatWtYxXdWZwqdUqVKS9Xv/o48+stqVL19ecq9evSTrOTwAUKRIEcl61/b+/fvnvrNk0XN4AGD48OGSf/rpJ8nunAs93+6WW26RfN5551nt9HLoiy++WHLbtm2tdtu2bZOsK/q6808SQXp6OjZt2gTgz8u0dZVkPc/GrZKclpYmuVy5cpL17xqw/566n2+vvvqqZP331C0Vc9ddd0nW5+v48eNWOz23SJepcJey67/rer5QSkqK1U6/t4Ot1h4q3ukhIiIiX+BFDxEREflCxMd/3nzzTeux3khUL0F9/fXXrXZ6Q0F3czK9fE4/X/v27a12EyZMkFypUqWcdJti4MiRI5LdTWb18szsKjLr15TejNRdwtmzZ0/J+jbx+eefn4Mek+vLL7+UrIe33NvoAwYMkKw3n3TLUUyZMkWyW6Wbck+XC3jwwQetY++++67ks846S/K0adOsdnqoKtihCb1ptDsUoys86+XxiTi8tX//fhl2cod4Nf1700O/gL2pdps2bSTroWT3OVx6mFBzN/cM9Bz6PQrYGwjr868r5wP2MJaelqIrNQPRfW/zTg8RERH5Ai96iIiIyBciMrz11ltvSXY3AtS3yfRscz1MBQR/m1TPNtebEwL2rPKlS5dKrlevXlDPHU3uRotr1qwBAPz444+x6E5Y6XOuKyYDwOLFiyUvXLhQ8tdff2210ytB9O1Ud+NKvQpC09VAAeD555+X/NJLL0l2b/HrlQ5lypTJ8rnpD4FWYbqbVAbyyCOPWI85pBVe7mpaXWlZr5oC7N/9wIEDJbtDxaGsttHfo1foAfbQR6JXYfY8DydPngTw55VN9evXl6yH2N1VWXrFVqj0MKZe1Rzsc+uq24A99Hno0CHJc+bMsdrp6tJNmjSRfO6551rtKleuHFQ/woF3eoiIiMgXeNFDREREvsCLHiIiIvKFsM3p0dUmdfVcPZYI2LtdP/PMM5JDrcKo5/S4SwLnzZsn+cUXX5T8yiuvhPSzwi01NVXybbfdZh2rXbs2gODnQsTCzz//LFkvVf7vf/9rtdPHdCVPwF6G2rJlS8l6XhhgjwdrCxYssB7rXb6z87e//U2yrkT73HPPWe10yQU9/wgAzjnnnKB+lp/o37/e2Xnfvn0Bv0fPMbj22msj0S3KoKv7AvZ7tWrVqtaxSZMmST7zzDNz/bP1sunMOYsA8O2331rt9DwTdx5Moqldu7bsQuDOzyxbtqxkXWojEipUqCC5ePHikitWrBjS8+l5V1WqVJHcu3fvgN+jq3q7c7WieZ55p4eIiIh8gRc9RERE5AshD2+5yxtvvvlmyfo2lrsU7bXXXpMcjgqb+lZdgwYNrGN6eEtX540mdwmv3kxuxowZkt2luffccw8Ae3PNaNF91lWSt2zZYrW7//77Je/Zs0fyb7/9ZrXTGxTq1wkADBkyRLK+vZ7dZrF6yM+t9Kvp4bLMTf8y6SFOPfzmDq/oY27f/+///k+yW0XVr/R7euTIkZI/++wzq92TTz4p+frrr5d8wQUXRLB3/qSXqb/zzjvWMT206w4p53ZIa//+/dbjYcOGSdYbYLpTIPTrJhGrMGt58uSR5d7usu9o0pXmjx07Jtnd4Pfqq68O68/NXK4P2OdST40A7OktbrXmcOOdHiIiIvIFXvQQERGRL+RoeMvzPLkVeeutt1rH9OoMfatKV7sF/rxCIJzczQq1Xbt2ST58+LB1LLe3cd3bsxs2bJA8ffp065jekG3o0KGSA93GDXVVW064m8lNnjxZ8ogRIyR/9dVXVjt9m1Rr166d9Vi/BvSKHsBe0XHw4MGAfdIVQAcNGiTZXRWkXwP657rDjPo86Nfk8OHDrXZ6+Mwd3tPDqXqoS1da9TO9AbC7QkerU6dONLrjW/o9pjdhBuzVW5dccklQz+dWN9crb/RqSr2pLBB4BZ/72cf3T/jVrVtXsh5m00OOgL26OpS/Pe7nrB7e0uffrW5/9OhRye6UmHCvbOOdHiIiIvIFXvQQERGRL/Cih4iIiHwhR3N6fv75Z3z66acA7KWOgL0sUu+ersf1I61169bW42LFiknW1TDdys2Z1Y8Be/x7x44dVju9g/eHH34o2d0lWM99mT17dsCfFS/Gjx9vPdbVq3v27ClZ71QP2PNY9DkfPHiw1U6/NkaPHm0d0/Oa9Jwed9m7HufVVUS7du1qtbvyyislB7v8WY9d9+rVK+DzNW/e3Dp24MAByXqpu1t1Wlcs9RN9Dnfv3h2wnZ5XUK9ePetY586dJSf6jtuxUqBAAcnz58+3jpUqVSrHz+fOz9KV9fVnQokSJax2+n2mPxPcarz68zM9PV1yLJd8Jzr9t1CXiFiyZInVLiUlRXKlSpWCem5dQkTvMuD+XH3OFy1aZLVbsWKF5Fq1alnHevToITkcpUF4p4eIiIh8gRc9RERE5As5Gt46fPiwtQmdVrNmTcndunWTHM1b0rrqJAB06dJF8tixYyW7m3vqW6h6iZ07bKWrSOql0e4twosuukhydpWFYyktLU1+J3oJOGAvGdVLu/WyQsCueK2XqWf3b+7YsaP1WG+6V7169Sy/DthL3aP5O9VLPXWlWADo3r27ZH3LX98+Bv5c9dQv9LCKOwz58ssvS9YlH3SVb8D+/NBDXRQ8PawUankOXWXf3ZR31apVkt944w3JbnkS/Rlx6NAhye6mynoTaV0Rvk+fPjntNmVBn7+dO3dax/TfUJ3dIXr9esifP79kt4yJPrd6Fwc9jQT481J3TW/6/MILL0h2P2eDxTs9RERE5Au86CEiIiJfyNE4Qd68eWW2v56JnXksU8OGDcPQtdzr16+fZL3SZvv27Va7tLQ0yfqWmbv6QK/k0dV43aGYeB3S0g4cOCCbfboVqt9//33J69evl+wO41188cU5/rnu7PtOnTrl+DliRQ/bAna17eXLl0vevHmz1U7fnvUrdzNgvRmsXvHj3h7Xv1cOb8WO3rBXV5wHgIkTJ0rWG1aOGTPGaqdXBunP1jvvvNNqpys+6yF0vUIU4Gq+UOlhR/czXJ9nvUI5VHplnq7CrKeUAEC5cuUku38/9XCn/twIFe/0EBERkS/wooeIiIh8gRc9RERE5As5mnxSunRpGVd3Kw3fdNNNkqOxM3gw9BLouXPnBmynl8uFe0dXt7KwXt4XSydOnJDKpxdeeKF1TI+xn3vuuZJDmcOTTNzXta4ufdlll0l2q5LqnazptA4dOkjWc3r69u1rtXv22Wej1iey6TIMAwcOlKzn8AB2pXJ97Mknn7Ta6Uq7ixcvzvLrAKTqv/sc4f5s9iv9Oda/f3/r2C233JLl9+zdu9d6rJes6+dzz6W7Y3omdz6WntNz/Phx61jp0qUlh+M1wFcRERER+QIveoiIiMgXcjS8VbhwYVl6umvXLutYIt96jGTf43X5ep06deRWtK7ADPy5EjVlTW8e27t3b8mPPfaY1W7fvn3R6lLC0CUvmjVrJvmpp56y2sXLcLAfuGVIdHXlokWLSm7atKnVTg9HPfTQQ5L1Js/usew2Xm2uwAMAAAU8SURBVN6yZYtkXUE6XqZNJBN3+CnQcJS72XIk6U1KIyFxr1SIiIiIcoAXPUREROQLvOghIiIiXwh5wkkiz+GJpngdhy5SpEhUx2mT3X333SdZ7zoNACVLlpQ8c+bMqPUpnmSWR8g0efJkyZMmTZJcuHDhaHWJHKdOnbIef/LJJ5L1vL/58+db7Z5//nnJeosDdzl0sFvO6LlFX3zxheR4Lf9BiYVXLkREROQLvOghIiIiX4jP9dRECUYPYS1dujRgOz8Nb+mdlDt27Ggd00Mfuuo3xc6JEyesx9u3b5e8cOFCye7Sdl3RfdmyZZIrVaoUUj8+++wzyQUKFJAcr1MFKLHwTg8RERH5Ai96iIiIyBc4vEVEETFmzBjJdevWtY61b98+2t2hv+AOH7Vq1UpyWlqa5AsuuMBqN2fOHMl648hguavGdIXnatWqSdYbQxOFind6iIiIyBd40UNERES+wIseIiIi8gXO6SGisNm1a5fk119/XfLatWutdnnz5o1anyg4hQoVsh6PHTtWcteuXSU3bNjQaqd3YA/F8uXLrce60nLZsmUlHz161GqnjxEFi3d6iIiIyBd40UNERES+YNzqmtk2NiYNwFd/2ZDCqbrneWG/j8tzGTM8n8mD5zK5hP188lzGTMBzmaOLHiIiIqJExeEtIiIi8gVe9BAREZEvJO1FjzEmrzFmkzFmUcbjmcaYzRn/22eM2RzrPlL2jDFVjTErjTE7jDHbjTG9M77Oc5mAjDETjTGpxpht6mtDjTGfG2M+M8bMM8aUzO45KD5k894sbYxZboz5MuP/S8W6r/TXArw3B2W8LzcbY5YZYyrFso/hkrRzeowxDwJoAqC453nXOceGAzjqed7TMekcBcUYUxFARc/zPjXGFAOwEUAHz/P+p9rwXCYIY8zlAH4CMNnzvPMzvnY1gBWe5500xgwBAM/z+sWwmxSEQO9NALcDOOJ53mBjTH8ApXg+41+A92Zxz/OOZeQHANTzPK9nDLsZFkl5p8cYUwVAOwATsjhmAHQE8Ga0+0U543leiud5n2bkHwHsAFA58zjPZWLxPG8VgCPO15Z5nncy4+FaAFWi3jHKsWzem38H8EZGszdw+kKI4lyA9+Yx9bAIgKS4Q5KsFZlHAngEQLEsjl0G4KDneV9Gt0uUG8aYGgAaAlinvsxzmVzuBDAz1p2gnHHem+U9z0sBTl8YGWNyvu06xQ1jzLMAbgNwFECrGHcnLJLuTo8x5joAqZ7nbQzQpDN4ZyChGGOKApgD4N/Of33wXCYJY8xjAE4CmBbrvlDwsnlvUhLwPO8xz/Oq4vT7sles+xMOSXfRA6AFgPbGmH0AZgBobYyZCgDGmHwAbgT/azJhGGPy4/SH6jTP8+aqr/NcJgljTDcA1wG41UvWSYZJKMB782DGfJ/MeT+pseofhdV0ADfFuhPhkHQXPZ7nPep5XhXP82oA6ITTkyS7ZBxuA+Bzz/O+iVkHKWgZc3ZeA7DD87z/Ood5LpOAMeYaAP0AtPc8Lz3W/aHgZPPefBtAt4zcDcCCaPeNwsMYc4562B7A57HqSzgl65yeQDqBwyGJpAWArgC2qmXpAzzPWwKey4RjjHkTwBUAyhhjvgEwEMCjAAoCWH767yjWJsMKER/I8r0JYDCAWcaY7gC+BnBzjPpHORDgvXmtMeZcAL/j9FYaSfG+TNol60RERERa0g1vEREREWWFFz1ERETkC7zoISIiIl/gRQ8RERH5Ai96iIiIyBd40UNERES+wIseIiIi8gVe9BAREZEv/D9I32/aeeiy+gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "for i in range(25):\n", + " plt.subplot(5,5,i+1)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.grid(False)\n", + " plt.imshow(train_data[i], cmap=plt.cm.binary)\n", + " plt.xlabel(train_labels[i])\n", + "train_data = train_data1.reshape((train_data.shape[0], train_data.shape[1], train_data.shape[2], 1))\n", + "test_data = test_data1.reshape((test_data.shape[0], test_data.shape[1], test_data.shape[2], 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_labels))\n", + "test_dataset = tf.data.Dataset.from_tensor_slices((test_data, test_labels))\n", + "\n", + "#outlining number of validation samples (picked 8% of training data)\n", + "num_validation_samples = 0.08 * train_data.shape[0]\n", + "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n", + "\n", + "#isolating test samples\n", + "num_test_samples = test_data.shape[0]\n", + "num_test_samples = tf.cast(num_test_samples, tf.int64)\n", + "\n", + "#setting buffer size and shuffling training data\n", + "BUFFER_SIZE = 10000\n", + "train_dataset = train_dataset.shuffle(BUFFER_SIZE)\n", + "#extracting validation data from grouping into own var\n", + "validation_dataset = train_dataset.take(num_validation_samples)\n", + "#excluding validation data from grouping and defining as training data\n", + "train_dataset = train_dataset.skip(num_validation_samples)\n", + "\n", + "#setting batch size\n", + "BATCH_SIZE = 128\n", + "train_dataset = train_dataset.batch(BATCH_SIZE)\n", + "validation_dataset = validation_dataset.batch(num_validation_samples)\n", + "test_dataset = test_dataset.batch(num_test_samples)\n", + "\n", + "#separating validation data into inputs and targets\n", + "validation_inputs, validation_targets = next(iter(validation_dataset))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 1671 steps, validate on 18589 samples\n", + "Epoch 1/100\n", + "1671/1671 [==============================] - 163s 98ms/step - loss: 0.6321 - accuracy: 0.8310 - val_loss: 0.2672 - val_accuracy: 0.9257\n", + "Epoch 2/100\n", + "1671/1671 [==============================] - 162s 97ms/step - loss: 0.2518 - accuracy: 0.9290 - val_loss: 0.1946 - val_accuracy: 0.9454\n", + "Epoch 3/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1820 - accuracy: 0.9475 - val_loss: 0.1655 - val_accuracy: 0.9529\n", + "Epoch 4/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1433 - accuracy: 0.9578 - val_loss: 0.1397 - val_accuracy: 0.9599\n", + "Epoch 5/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1160 - accuracy: 0.9649 - val_loss: 0.1258 - val_accuracy: 0.9637\n", + "Epoch 6/100\n", + "1671/1671 [==============================] - 167s 100ms/step - loss: 0.0977 - accuracy: 0.9698 - val_loss: 0.1251 - val_accuracy: 0.9636\n", + "Epoch 7/100\n", + "1671/1671 [==============================] - 163s 98ms/step - loss: 0.0840 - accuracy: 0.9736 - val_loss: 0.1134 - val_accuracy: 0.9694\n", + "Epoch 8/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0739 - accuracy: 0.9762 - val_loss: 0.1036 - val_accuracy: 0.9719\n", + "Epoch 9/100\n", + "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0662 - accuracy: 0.9786 - val_loss: 0.1062 - val_accuracy: 0.9706\n", + "Epoch 10/100\n", + "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0590 - accuracy: 0.9806 - val_loss: 0.0992 - val_accuracy: 0.9732\n", + "Epoch 11/100\n", + "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0559 - accuracy: 0.9817 - val_loss: 0.0910 - val_accuracy: 0.9750\n", + "Epoch 12/100\n", + "1671/1671 [==============================] - 178s 107ms/step - loss: 0.0497 - accuracy: 0.9837 - val_loss: 0.0926 - val_accuracy: 0.9739\n", + "Epoch 13/100\n", + "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0479 - accuracy: 0.9842 - val_loss: 0.0907 - val_accuracy: 0.9767\n", + "Epoch 14/100\n", + "1671/1671 [==============================] - 220s 131ms/step - loss: 0.0434 - accuracy: 0.9851 - val_loss: 0.0873 - val_accuracy: 0.9770\n", + "Epoch 15/100\n", + "1671/1671 [==============================] - 212s 127ms/step - loss: 0.0437 - accuracy: 0.9854 - val_loss: 0.0795 - val_accuracy: 0.9779\n", + "Epoch 16/100\n", + "1671/1671 [==============================] - 210s 126ms/step - loss: 0.0381 - accuracy: 0.9870 - val_loss: 0.0751 - val_accuracy: 0.9796\n", + "Epoch 17/100\n", + "1671/1671 [==============================] - 206s 123ms/step - loss: 0.0385 - accuracy: 0.9869 - val_loss: 0.0711 - val_accuracy: 0.9803\n", + "Epoch 18/100\n", + "1671/1671 [==============================] - 196s 117ms/step - loss: 0.0353 - accuracy: 0.9881 - val_loss: 0.0701 - val_accuracy: 0.9817\n", + "Epoch 19/100\n", + "1671/1671 [==============================] - 178s 107ms/step - loss: 0.0348 - accuracy: 0.9883 - val_loss: 0.0684 - val_accuracy: 0.9832\n", + "Epoch 20/100\n", + "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0338 - accuracy: 0.9887 - val_loss: 0.0635 - val_accuracy: 0.9832\n", + "Epoch 21/100\n", + "1671/1671 [==============================] - 169s 101ms/step - loss: 0.0310 - accuracy: 0.9896 - val_loss: 0.0631 - val_accuracy: 0.9836\n", + "Epoch 22/100\n", + "1671/1671 [==============================] - 170s 102ms/step - loss: 0.0312 - accuracy: 0.9894 - val_loss: 0.0626 - val_accuracy: 0.9842\n", + "Epoch 23/100\n", + "1671/1671 [==============================] - 171s 103ms/step - loss: 0.0298 - accuracy: 0.9898 - val_loss: 0.0580 - val_accuracy: 0.9847\n", + "Epoch 24/100\n", + "1671/1671 [==============================] - 186s 111ms/step - loss: 0.0281 - accuracy: 0.9905 - val_loss: 0.0631 - val_accuracy: 0.9835\n", + "Epoch 25/100\n", + "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0280 - accuracy: 0.9904 - val_loss: 0.0582 - val_accuracy: 0.9835\n", + "Epoch 26/100\n", + "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0268 - accuracy: 0.9908 - val_loss: 0.0586 - val_accuracy: 0.9855\n", + "Epoch 27/100\n", + "1671/1671 [==============================] - 124s 74ms/step - loss: 0.0260 - accuracy: 0.9912 - val_loss: 0.0525 - val_accuracy: 0.9864\n", + "Epoch 28/100\n", + "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0251 - accuracy: 0.9916 - val_loss: 0.0531 - val_accuracy: 0.9862\n", + "Epoch 29/100\n", + "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0248 - accuracy: 0.9915 - val_loss: 0.0533 - val_accuracy: 0.9860\n", + "Epoch 30/100\n", + "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0242 - accuracy: 0.9917 - val_loss: 0.0478 - val_accuracy: 0.9876\n", + "Epoch 31/100\n", + "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0234 - accuracy: 0.9919 - val_loss: 0.0474 - val_accuracy: 0.9871\n", + "Epoch 32/100\n", + "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0228 - accuracy: 0.9923 - val_loss: 0.0497 - val_accuracy: 0.9869\n", + "Epoch 33/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0229 - accuracy: 0.9924 - val_loss: 0.0514 - val_accuracy: 0.9869\n", + "Epoch 34/100\n", + "1671/1671 [==============================] - 128s 77ms/step - loss: 0.0216 - accuracy: 0.9929 - val_loss: 0.0472 - val_accuracy: 0.9877\n", + "Epoch 35/100\n", + "1671/1671 [==============================] - 130s 78ms/step - loss: 0.0220 - accuracy: 0.9924 - val_loss: 0.0476 - val_accuracy: 0.9879\n", + "Epoch 36/100\n", + "1671/1671 [==============================] - 131s 78ms/step - loss: 0.0212 - accuracy: 0.9931 - val_loss: 0.0448 - val_accuracy: 0.9877\n", + "Epoch 37/100\n", + "1671/1671 [==============================] - 129s 77ms/step - loss: 0.0210 - accuracy: 0.9930 - val_loss: 0.0384 - val_accuracy: 0.9895\n", + "Epoch 38/100\n", + "1671/1671 [==============================] - 131s 79ms/step - loss: 0.0200 - accuracy: 0.9933 - val_loss: 0.0380 - val_accuracy: 0.9892\n", + "Epoch 39/100\n", + "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0199 - accuracy: 0.9932 - val_loss: 0.0388 - val_accuracy: 0.9885\n", + "Epoch 40/100\n", + "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0205 - accuracy: 0.9932 - val_loss: 0.0357 - val_accuracy: 0.9898\n", + "Epoch 41/100\n", + "1671/1671 [==============================] - 128s 77ms/step - loss: 0.0191 - accuracy: 0.9936 - val_loss: 0.0352 - val_accuracy: 0.9902\n", + "Epoch 42/100\n", + "1671/1671 [==============================] - 146s 87ms/step - loss: 0.0183 - accuracy: 0.9939 - val_loss: 0.0365 - val_accuracy: 0.9898\n", + "Epoch 43/100\n", + "1671/1671 [==============================] - 149s 89ms/step - loss: 0.0201 - accuracy: 0.9933 - val_loss: 0.0338 - val_accuracy: 0.9904\n", + "Epoch 44/100\n", + "1671/1671 [==============================] - 141s 85ms/step - loss: 0.0182 - accuracy: 0.9937 - val_loss: 0.0335 - val_accuracy: 0.9906\n", + "Epoch 45/100\n", + "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0173 - accuracy: 0.9941 - val_loss: 0.0309 - val_accuracy: 0.9911\n", + "Epoch 46/100\n", + "1671/1671 [==============================] - 129s 77ms/step - loss: 0.0190 - accuracy: 0.9935 - val_loss: 0.0329 - val_accuracy: 0.9910\n", + "Epoch 47/100\n", + "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0176 - accuracy: 0.9943 - val_loss: 0.0324 - val_accuracy: 0.9908\n", + "Epoch 48/100\n", + "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0178 - accuracy: 0.9940 - val_loss: 0.0311 - val_accuracy: 0.9909\n", + "Epoch 49/100\n", + "1671/1671 [==============================] - 126s 76ms/step - loss: 0.0176 - accuracy: 0.9942 - val_loss: 0.0323 - val_accuracy: 0.9907\n", + "Epoch 50/100\n", + "1671/1671 [==============================] - 118s 71ms/step - loss: 0.0159 - accuracy: 0.9948 - val_loss: 0.0258 - val_accuracy: 0.9927\n", + "Epoch 51/100\n", + "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0172 - accuracy: 0.9943 - val_loss: 0.0268 - val_accuracy: 0.9925\n", + "Epoch 52/100\n", + "1671/1671 [==============================] - 118s 70ms/step - loss: 0.0166 - accuracy: 0.9943 - val_loss: 0.0255 - val_accuracy: 0.9932\n", + "Epoch 53/100\n", + "1671/1671 [==============================] - 133s 80ms/step - loss: 0.0164 - accuracy: 0.9946 - val_loss: 0.0237 - val_accuracy: 0.9935\n", + "Epoch 54/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0163 - accuracy: 0.9946 - val_loss: 0.0234 - val_accuracy: 0.9930\n", + "Epoch 55/100\n", + "1671/1671 [==============================] - 118s 71ms/step - loss: 0.0157 - accuracy: 0.9947 - val_loss: 0.0242 - val_accuracy: 0.9934\n", + "Epoch 56/100\n", + "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0160 - accuracy: 0.9948 - val_loss: 0.0226 - val_accuracy: 0.9930\n", + "Epoch 57/100\n", + "1671/1671 [==============================] - 121s 72ms/step - loss: 0.0155 - accuracy: 0.9948 - val_loss: 0.0251 - val_accuracy: 0.9926\n", + "Epoch 58/100\n", + "1671/1671 [==============================] - 123s 73ms/step - loss: 0.0162 - accuracy: 0.9946 - val_loss: 0.0238 - val_accuracy: 0.9930\n", + "Epoch 59/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0152 - accuracy: 0.9950 - val_loss: 0.0226 - val_accuracy: 0.9933\n", + "Epoch 60/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0158 - accuracy: 0.9947 - val_loss: 0.0282 - val_accuracy: 0.9920\n", + "Epoch 61/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0151 - accuracy: 0.9950 - val_loss: 0.0232 - val_accuracy: 0.9933\n", + "Epoch 62/100\n", + "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0146 - accuracy: 0.9952 - val_loss: 0.0264 - val_accuracy: 0.9925\n", + "Epoch 63/100\n", + "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0146 - accuracy: 0.9953 - val_loss: 0.0201 - val_accuracy: 0.9938\n", + "Epoch 64/100\n", + "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0146 - accuracy: 0.9953 - val_loss: 0.0248 - val_accuracy: 0.9925\n", + "Epoch 65/100\n", + "1671/1671 [==============================] - 124s 74ms/step - loss: 0.0144 - accuracy: 0.9953 - val_loss: 0.0204 - val_accuracy: 0.9937\n", + "Epoch 66/100\n", + "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0136 - accuracy: 0.9955 - val_loss: 0.0198 - val_accuracy: 0.9940\n", + "Epoch 67/100\n", + "1671/1671 [==============================] - 132s 79ms/step - loss: 0.0138 - accuracy: 0.9953 - val_loss: 0.0227 - val_accuracy: 0.9927\n", + "Epoch 68/100\n", + "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0141 - accuracy: 0.9955 - val_loss: 0.0198 - val_accuracy: 0.9941\n", + "Epoch 69/100\n", + "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0138 - accuracy: 0.9955 - val_loss: 0.0222 - val_accuracy: 0.9937\n", + "Epoch 70/100\n", + "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0139 - accuracy: 0.9956 - val_loss: 0.0169 - val_accuracy: 0.9950\n", + "Epoch 71/100\n", + "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0132 - accuracy: 0.9957 - val_loss: 0.0195 - val_accuracy: 0.9946\n", + "Epoch 72/100\n", + "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0136 - accuracy: 0.9955 - val_loss: 0.0211 - val_accuracy: 0.9939\n", + "Epoch 73/100\n", + "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0130 - accuracy: 0.9958 - val_loss: 0.0219 - val_accuracy: 0.9937\n", + "Epoch 74/100\n", + "1671/1671 [==============================] - 141s 84ms/step - loss: 0.0137 - accuracy: 0.9957 - val_loss: 0.0177 - val_accuracy: 0.9939\n", + "Epoch 75/100\n", + "1671/1671 [==============================] - 175s 105ms/step - loss: 0.0135 - accuracy: 0.9957 - val_loss: 0.0194 - val_accuracy: 0.9945: 0.99\n", + "Epoch 76/100\n", + "1671/1671 [==============================] - 148s 88ms/step - loss: 0.0128 - accuracy: 0.9958 - val_loss: 0.0214 - val_accuracy: 0.9938\n", + "Epoch 77/100\n", + "1671/1671 [==============================] - 151s 90ms/step - loss: 0.0118 - accuracy: 0.9962 - val_loss: 0.0178 - val_accuracy: 0.9944\n", + "Epoch 78/100\n", + "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0132 - accuracy: 0.9958 - val_loss: 0.0202 - val_accuracy: 0.9943\n", + "Epoch 79/100\n", + "1671/1671 [==============================] - 189s 113ms/step - loss: 0.0130 - accuracy: 0.9957 - val_loss: 0.0182 - val_accuracy: 0.9941\n", + "Epoch 80/100\n", + "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0129 - accuracy: 0.9958 - val_loss: 0.0181 - val_accuracy: 0.9945\n", + "Epoch 81/100\n", + "1671/1671 [==============================] - 159s 95ms/step - loss: 0.0127 - accuracy: 0.9959 - val_loss: 0.0157 - val_accuracy: 0.9955\n", + "Epoch 82/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0121 - accuracy: 0.9961 - val_loss: 0.0184 - val_accuracy: 0.9948\n", + "Epoch 83/100\n", + "1671/1671 [==============================] - 163s 98ms/step - loss: 0.0123 - accuracy: 0.9961 - val_loss: 0.0163 - val_accuracy: 0.9954\n", + "Epoch 84/100\n", + "1671/1671 [==============================] - 175s 105ms/step - loss: 0.0125 - accuracy: 0.9960 - val_loss: 0.0188 - val_accuracy: 0.9948\n", + "Epoch 85/100\n", + "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0125 - accuracy: 0.9961 - val_loss: 0.0152 - val_accuracy: 0.9952\n", + "Epoch 86/100\n", + "1671/1671 [==============================] - 160s 96ms/step - loss: 0.0130 - accuracy: 0.9958 - val_loss: 0.0161 - val_accuracy: 0.9953\n", + "Epoch 87/100\n", + "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0120 - accuracy: 0.9961 - val_loss: 0.0168 - val_accuracy: 0.9956\n", + "Epoch 88/100\n", + "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0120 - accuracy: 0.9960 - val_loss: 0.0169 - val_accuracy: 0.9949\n", + "Epoch 89/100\n", + "1671/1671 [==============================] - 133s 80ms/step - loss: 0.0129 - accuracy: 0.9959 - val_loss: 0.0155 - val_accuracy: 0.9955\n", + "Epoch 90/100\n", + "1671/1671 [==============================] - 145s 87ms/step - loss: 0.0117 - accuracy: 0.9962 - val_loss: 0.0147 - val_accuracy: 0.9956\n", + "Epoch 91/100\n", + "1671/1671 [==============================] - 143s 85ms/step - loss: 0.0115 - accuracy: 0.9964 - val_loss: 0.0143 - val_accuracy: 0.9956\n", + "Epoch 92/100\n", + "1671/1671 [==============================] - 142s 85ms/step - loss: 0.0116 - accuracy: 0.9964 - val_loss: 0.0192 - val_accuracy: 0.9943\n", + "Epoch 93/100\n", + "1671/1671 [==============================] - 137s 82ms/step - loss: 0.0117 - accuracy: 0.9963 - val_loss: 0.0156 - val_accuracy: 0.9952\n", + "Epoch 94/100\n", + "1671/1671 [==============================] - 131s 78ms/step - loss: 0.0118 - accuracy: 0.9963 - val_loss: 0.0137 - val_accuracy: 0.9955\n", + "Epoch 95/100\n", + "1671/1671 [==============================] - 142s 85ms/step - loss: 0.0115 - accuracy: 0.9962 - val_loss: 0.0123 - val_accuracy: 0.9961\n", + "Epoch 96/100\n", + "1671/1671 [==============================] - 145s 87ms/step - loss: 0.0115 - accuracy: 0.9964 - val_loss: 0.0134 - val_accuracy: 0.9958\n", + "Epoch 97/100\n", + "1671/1671 [==============================] - 141s 85ms/step - loss: 0.0109 - accuracy: 0.9965 - val_loss: 0.0136 - val_accuracy: 0.9955\n", + "Epoch 98/100\n", + "1671/1671 [==============================] - 143s 86ms/step - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.0133 - val_accuracy: 0.9960\n", + "Epoch 99/100\n", + "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0121 - accuracy: 0.9960 - val_loss: 0.0147 - val_accuracy: 0.9954\n", + "Epoch 100/100\n", + "1671/1671 [==============================] - 139s 83ms/step - loss: 0.0111 - accuracy: 0.9966 - val_loss: 0.0159 - val_accuracy: 0.9952\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_size = 784\n", + "output_size = 49\n", + "standard_hidden_layer_width = 512\n", + "NUM_EPOCHS = 100\n", + "VALIDATION_STEPS = num_validation_samples // BATCH_SIZE\n", + "early_stopping = tf.keras.callbacks.EarlyStopping(patience=2)\n", + "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),\n", + " tf.keras.layers.MaxPooling2D(2, 2),\n", + " tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n", + " tf.keras.layers.MaxPooling2D(2,2),\n", + " tf.keras.layers.Dropout(0.25),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(standard_hidden_layer_width, activation='relu'),\n", + " tf.keras.layers.Dense(output_size, activation='softmax')\n", + " ])\n", + "\n", + "model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "model.fit(train_dataset, epochs=NUM_EPOCHS, callbacks=[reduce_lr], validation_data=(validation_inputs, validation_targets), validation_steps=VALIDATION_STEPS, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From C:\\Users\\victo\\Anaconda3\\envs\\py3-TF2.0\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "If using Keras pass *_constraint arguments to layers.\n", + "INFO:tensorflow:Assets written to: k49-convnet\\assets\n" + ] + } + ], + "source": [ + "model.save('k49-convnet')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "complete_model = tf.keras.models.load_model('k49-convnet')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = complete_model.predict([test_data])" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1000, 1000, 1000, 126, 1000, 1000, 1000, 1000, 767, 1000, 1000, 1000, 1000, 678, 629, 1000, 418, 1000, 1000, 1000, 1000, 1000, 336, 399, 1000, 1000, 836, 1000, 1000, 324, 1000, 498, 280, 552, 1000, 1000, 260, 1000, 1000, 1000, 1000, 1000, 348, 390, 68, 64, 1000, 1000, 574]\n", + "[960, 970, 971, 113, 957, 889, 931, 929, 722, 926, 954, 921, 909, 593, 562, 963, 405, 959, 933, 945, 909, 941, 315, 364, 934, 931, 799, 910, 945, 294, 967, 466, 249, 528, 970, 948, 246, 980, 918, 927, 928, 968, 331, 354, 50, 58, 964, 978, 474]\n", + "0.930142277358246\n" + ] + } + ], + "source": [ + "totals = []\n", + "for cls in range(49):\n", + " total = 0\n", + " for i in test_labels:\n", + " if i == cls:\n", + " total = total + 1\n", + " totals.append(total)\n", + "\n", + "hits = []\n", + "for cls in range(49):\n", + " total_hits = 0\n", + " for i in range(0,test_labels.shape[0]):\n", + " if test_labels[i] == cls == np.argmax(predictions[i]):\n", + " total_hits = total_hits + 1\n", + " hits.append(total_hits)\n", + " \n", + "accuracy_list = []\n", + "for i in range(0,len(hits)):\n", + " accuracy = hits[i]/totals[i]\n", + " accuracy_list.append(accuracy)\n", + "\n", + "print(np.mean(accuracy_list))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:py3-TF2.0]", + "language": "python", + "name": "conda-env-py3-TF2.0-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 2b063db29fc7aa784a60ba69e13f72db892bf435 Mon Sep 17 00:00:00 2001 From: VicHofs <57238498+VicHofs@users.noreply.github.com> Date: Mon, 3 Feb 2020 18:18:25 -0300 Subject: [PATCH 10/12] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index ba08832..64cdc53 100644 --- a/README.md +++ b/README.md @@ -91,6 +91,7 @@ Have more results to add to the table? Feel free to submit an [issue](https://gi |[PCA + 4-kNN](https://github.com/rois-codh/kmnist/issues/10) | 97.76% | 93.98% | 86.80% | [dzisandy](https://github.com/dzisandy) |[Tuned SVM (RBF kernel)](https://github.com/rois-codh/kmnist/issues/3) | 98.57% | 92.82%\* | 85.61%\* | [TomZephire](https://github.com/TomZephire) |[Keras Simple CNN Benchmark](benchmarks/kuzushiji_mnist_cnn.py) |99.06% | 94.63% | 89.36% | +|[TensorFlow Simple CNN](https://github.com/VicHofs/kmnist/blob/master/k49%20cnn%20notebook.ipynb)| | | 93.00% | [Victor Hofstetter](https://github.com/VicHofs) |PreActResNet-18 |99.56% | 97.82%\* |96.64%\*| |PreActResNet-18 + Input Mixup |99.54% | 98.41%\* |97.04%\*| |PreActResNet-18 + Manifold Mixup |99.54% | 98.83%\* | 97.33%\* | From abfd519f46753d77e99ec40339050ede7e87e9bf Mon Sep 17 00:00:00 2001 From: VicHofs <57238498+VicHofs@users.noreply.github.com> Date: Tue, 18 Feb 2020 02:28:28 -0300 Subject: [PATCH 11/12] Delete conv-net tensorflow model --- conv-net tensorflow model | 101 -------------------------------------- 1 file changed, 101 deletions(-) delete mode 100644 conv-net tensorflow model diff --git a/conv-net tensorflow model b/conv-net tensorflow model deleted file mode 100644 index a3f0e01..0000000 --- a/conv-net tensorflow model +++ /dev/null @@ -1,101 +0,0 @@ -########################### -#KMNIST49 CNN model -#by Victor Hofstetter -#02/02/2020 -########################### - - -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt - -def balanced_accuracy(test_labels, predictions, outputs): - totals = [] - for cls in range(outputs): - total = 0 - for i in test_labels: - if i == cls: - total = total + 1 - totals.append(total) - - hits = [] - for cls in range(outputs): - total_hits = 0 - for i in range(0, test_labels.shape[0]): - if test_labels[i] == cls == np.argmax(predictions[i]): - total_hits = total_hits + 1 - hits.append(total_hits) - - accuracy_list = [] - for i in range(0, len(hits)): - accuracy = hits[i] / totals[i] - accuracy_list.append(accuracy) - - print(f'The balanced accuracy is: {np.mean(accuracy_list)}') - -train_data = np.load('k49-train-imgs.npz')['arr_0'] -train_labels = np.load('k49-train-labels.npz')['arr_0'] -test_data = np.load('k49-test-imgs.npz')['arr_0'] -test_labels = np.load('k49-test-labels.npz')['arr_0'] - -train_data1 = tf.keras.utils.normalize(train_data, axis=1) -test_data1 = tf.keras.utils.normalize(test_data, axis=1) - -train_data = train_data1.reshape((train_data.shape[0], train_data.shape[1], train_data.shape[2], 1)) -test_data = test_data1.reshape((test_data.shape[0], test_data.shape[1], test_data.shape[2], 1)) - -train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_labels)) -test_dataset = tf.data.Dataset.from_tensor_slices((test_data, test_labels)) - -#outlining number of validation samples (picked 8% of training data) -num_validation_samples = 0.08 * train_data.shape[0] -num_validation_samples = tf.cast(num_validation_samples, tf.int64) - -#isolating test samples -num_test_samples = test_data.shape[0] -num_test_samples = tf.cast(num_test_samples, tf.int64) - -#setting buffer size and shuffling training data -BUFFER_SIZE = 10000 -train_dataset = train_dataset.shuffle(BUFFER_SIZE) -#extracting validation data from grouping into own var -validation_dataset = train_dataset.take(num_validation_samples) -#excluding validation data from grouping and defining as training data -train_dataset = train_dataset.skip(num_validation_samples) - -#setting batch size -BATCH_SIZE = 128 -train_dataset = train_dataset.batch(BATCH_SIZE) -validation_dataset = validation_dataset.batch(num_validation_samples) -test_dataset = test_dataset.batch(num_test_samples) - -#separating validation data into inputs and targets -validation_inputs, validation_targets = next(iter(validation_dataset)) - -input_size = 784 -output_size = 49 -standard_hidden_layer_width = 512 -NUM_EPOCHS = 100 -VALIDATION_STEPS = num_validation_samples // BATCH_SIZE -early_stopping = tf.keras.callbacks.EarlyStopping(patience=2) -reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001) - -model = tf.keras.Sequential([ - tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)), - tf.keras.layers.MaxPooling2D(2, 2), - tf.keras.layers.Conv2D(64, (3,3), activation='relu'), - tf.keras.layers.MaxPooling2D(2,2), - tf.keras.layers.Dropout(0.25), - tf.keras.layers.Flatten(), - tf.keras.layers.Dense(standard_hidden_layer_width, activation='relu'), - tf.keras.layers.Dense(output_size, activation='softmax') - ]) - -model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) - -model.fit(train_dataset, epochs=NUM_EPOCHS, callbacks=[reduce_lr], validation_data=(validation_inputs, validation_targets), validation_steps=VALIDATION_STEPS, verbose=1) - - -predictions = model.predict([test_data]) - -balanced_accuracy(test_labels, predictions, output_size) From 0f53d3d1d87e3d32d4ba2fdce9be7a442183a336 Mon Sep 17 00:00:00 2001 From: VicHofs <57238498+VicHofs@users.noreply.github.com> Date: Tue, 18 Feb 2020 02:28:38 -0300 Subject: [PATCH 12/12] Delete k49 cnn notebook.ipynb --- k49 cnn notebook.ipynb | 438 ----------------------------------------- 1 file changed, 438 deletions(-) delete mode 100644 k49 cnn notebook.ipynb diff --git a/k49 cnn notebook.ipynb b/k49 cnn notebook.ipynb deleted file mode 100644 index dea16a7..0000000 --- a/k49 cnn notebook.ipynb +++ /dev/null @@ -1,438 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "train_data = np.load('k49-train-imgs.npz')['arr_0']\n", - "train_labels = np.load('k49-train-labels.npz')['arr_0']\n", - "test_data = np.load('k49-test-imgs.npz')['arr_0']\n", - "test_labels = np.load('k49-test-labels.npz')['arr_0']\n", - "\n", - "train_data1 = tf.keras.utils.normalize(train_data, axis=1)\n", - "test_data1 = tf.keras.utils.normalize(test_data, axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(10,10))\n", - "for i in range(25):\n", - " plt.subplot(5,5,i+1)\n", - " plt.xticks([])\n", - " plt.yticks([])\n", - " plt.grid(False)\n", - " plt.imshow(train_data[i], cmap=plt.cm.binary)\n", - " plt.xlabel(train_labels[i])\n", - "train_data = train_data1.reshape((train_data.shape[0], train_data.shape[1], train_data.shape[2], 1))\n", - "test_data = test_data1.reshape((test_data.shape[0], test_data.shape[1], test_data.shape[2], 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataset = tf.data.Dataset.from_tensor_slices((train_data, train_labels))\n", - "test_dataset = tf.data.Dataset.from_tensor_slices((test_data, test_labels))\n", - "\n", - "#outlining number of validation samples (picked 8% of training data)\n", - "num_validation_samples = 0.08 * train_data.shape[0]\n", - "num_validation_samples = tf.cast(num_validation_samples, tf.int64)\n", - "\n", - "#isolating test samples\n", - "num_test_samples = test_data.shape[0]\n", - "num_test_samples = tf.cast(num_test_samples, tf.int64)\n", - "\n", - "#setting buffer size and shuffling training data\n", - "BUFFER_SIZE = 10000\n", - "train_dataset = train_dataset.shuffle(BUFFER_SIZE)\n", - "#extracting validation data from grouping into own var\n", - "validation_dataset = train_dataset.take(num_validation_samples)\n", - "#excluding validation data from grouping and defining as training data\n", - "train_dataset = train_dataset.skip(num_validation_samples)\n", - "\n", - "#setting batch size\n", - "BATCH_SIZE = 128\n", - "train_dataset = train_dataset.batch(BATCH_SIZE)\n", - "validation_dataset = validation_dataset.batch(num_validation_samples)\n", - "test_dataset = test_dataset.batch(num_test_samples)\n", - "\n", - "#separating validation data into inputs and targets\n", - "validation_inputs, validation_targets = next(iter(validation_dataset))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train for 1671 steps, validate on 18589 samples\n", - "Epoch 1/100\n", - "1671/1671 [==============================] - 163s 98ms/step - loss: 0.6321 - accuracy: 0.8310 - val_loss: 0.2672 - val_accuracy: 0.9257\n", - "Epoch 2/100\n", - "1671/1671 [==============================] - 162s 97ms/step - loss: 0.2518 - accuracy: 0.9290 - val_loss: 0.1946 - val_accuracy: 0.9454\n", - "Epoch 3/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1820 - accuracy: 0.9475 - val_loss: 0.1655 - val_accuracy: 0.9529\n", - "Epoch 4/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1433 - accuracy: 0.9578 - val_loss: 0.1397 - val_accuracy: 0.9599\n", - "Epoch 5/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.1160 - accuracy: 0.9649 - val_loss: 0.1258 - val_accuracy: 0.9637\n", - "Epoch 6/100\n", - "1671/1671 [==============================] - 167s 100ms/step - loss: 0.0977 - accuracy: 0.9698 - val_loss: 0.1251 - val_accuracy: 0.9636\n", - "Epoch 7/100\n", - "1671/1671 [==============================] - 163s 98ms/step - loss: 0.0840 - accuracy: 0.9736 - val_loss: 0.1134 - val_accuracy: 0.9694\n", - "Epoch 8/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0739 - accuracy: 0.9762 - val_loss: 0.1036 - val_accuracy: 0.9719\n", - "Epoch 9/100\n", - "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0662 - accuracy: 0.9786 - val_loss: 0.1062 - val_accuracy: 0.9706\n", - "Epoch 10/100\n", - "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0590 - accuracy: 0.9806 - val_loss: 0.0992 - val_accuracy: 0.9732\n", - "Epoch 11/100\n", - "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0559 - accuracy: 0.9817 - val_loss: 0.0910 - val_accuracy: 0.9750\n", - "Epoch 12/100\n", - "1671/1671 [==============================] - 178s 107ms/step - loss: 0.0497 - accuracy: 0.9837 - val_loss: 0.0926 - val_accuracy: 0.9739\n", - "Epoch 13/100\n", - "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0479 - accuracy: 0.9842 - val_loss: 0.0907 - val_accuracy: 0.9767\n", - "Epoch 14/100\n", - "1671/1671 [==============================] - 220s 131ms/step - loss: 0.0434 - accuracy: 0.9851 - val_loss: 0.0873 - val_accuracy: 0.9770\n", - "Epoch 15/100\n", - "1671/1671 [==============================] - 212s 127ms/step - loss: 0.0437 - accuracy: 0.9854 - val_loss: 0.0795 - val_accuracy: 0.9779\n", - "Epoch 16/100\n", - "1671/1671 [==============================] - 210s 126ms/step - loss: 0.0381 - accuracy: 0.9870 - val_loss: 0.0751 - val_accuracy: 0.9796\n", - "Epoch 17/100\n", - "1671/1671 [==============================] - 206s 123ms/step - loss: 0.0385 - accuracy: 0.9869 - val_loss: 0.0711 - val_accuracy: 0.9803\n", - "Epoch 18/100\n", - "1671/1671 [==============================] - 196s 117ms/step - loss: 0.0353 - accuracy: 0.9881 - val_loss: 0.0701 - val_accuracy: 0.9817\n", - "Epoch 19/100\n", - "1671/1671 [==============================] - 178s 107ms/step - loss: 0.0348 - accuracy: 0.9883 - val_loss: 0.0684 - val_accuracy: 0.9832\n", - "Epoch 20/100\n", - "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0338 - accuracy: 0.9887 - val_loss: 0.0635 - val_accuracy: 0.9832\n", - "Epoch 21/100\n", - "1671/1671 [==============================] - 169s 101ms/step - loss: 0.0310 - accuracy: 0.9896 - val_loss: 0.0631 - val_accuracy: 0.9836\n", - "Epoch 22/100\n", - "1671/1671 [==============================] - 170s 102ms/step - loss: 0.0312 - accuracy: 0.9894 - val_loss: 0.0626 - val_accuracy: 0.9842\n", - "Epoch 23/100\n", - "1671/1671 [==============================] - 171s 103ms/step - loss: 0.0298 - accuracy: 0.9898 - val_loss: 0.0580 - val_accuracy: 0.9847\n", - "Epoch 24/100\n", - "1671/1671 [==============================] - 186s 111ms/step - loss: 0.0281 - accuracy: 0.9905 - val_loss: 0.0631 - val_accuracy: 0.9835\n", - "Epoch 25/100\n", - "1671/1671 [==============================] - 180s 108ms/step - loss: 0.0280 - accuracy: 0.9904 - val_loss: 0.0582 - val_accuracy: 0.9835\n", - "Epoch 26/100\n", - "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0268 - accuracy: 0.9908 - val_loss: 0.0586 - val_accuracy: 0.9855\n", - "Epoch 27/100\n", - "1671/1671 [==============================] - 124s 74ms/step - loss: 0.0260 - accuracy: 0.9912 - val_loss: 0.0525 - val_accuracy: 0.9864\n", - "Epoch 28/100\n", - "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0251 - accuracy: 0.9916 - val_loss: 0.0531 - val_accuracy: 0.9862\n", - "Epoch 29/100\n", - "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0248 - accuracy: 0.9915 - val_loss: 0.0533 - val_accuracy: 0.9860\n", - "Epoch 30/100\n", - "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0242 - accuracy: 0.9917 - val_loss: 0.0478 - val_accuracy: 0.9876\n", - "Epoch 31/100\n", - "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0234 - accuracy: 0.9919 - val_loss: 0.0474 - val_accuracy: 0.9871\n", - "Epoch 32/100\n", - "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0228 - accuracy: 0.9923 - val_loss: 0.0497 - val_accuracy: 0.9869\n", - "Epoch 33/100\n", - "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0229 - accuracy: 0.9924 - val_loss: 0.0514 - val_accuracy: 0.9869\n", - "Epoch 34/100\n", - "1671/1671 [==============================] - 128s 77ms/step - loss: 0.0216 - accuracy: 0.9929 - val_loss: 0.0472 - val_accuracy: 0.9877\n", - "Epoch 35/100\n", - "1671/1671 [==============================] - 130s 78ms/step - loss: 0.0220 - accuracy: 0.9924 - val_loss: 0.0476 - val_accuracy: 0.9879\n", - "Epoch 36/100\n", - "1671/1671 [==============================] - 131s 78ms/step - loss: 0.0212 - accuracy: 0.9931 - val_loss: 0.0448 - val_accuracy: 0.9877\n", - "Epoch 37/100\n", - "1671/1671 [==============================] - 129s 77ms/step - loss: 0.0210 - accuracy: 0.9930 - val_loss: 0.0384 - val_accuracy: 0.9895\n", - "Epoch 38/100\n", - "1671/1671 [==============================] - 131s 79ms/step - loss: 0.0200 - accuracy: 0.9933 - val_loss: 0.0380 - val_accuracy: 0.9892\n", - "Epoch 39/100\n", - "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0199 - accuracy: 0.9932 - val_loss: 0.0388 - val_accuracy: 0.9885\n", - "Epoch 40/100\n", - "1671/1671 [==============================] - 135s 81ms/step - loss: 0.0205 - accuracy: 0.9932 - val_loss: 0.0357 - val_accuracy: 0.9898\n", - "Epoch 41/100\n", - "1671/1671 [==============================] - 128s 77ms/step - loss: 0.0191 - accuracy: 0.9936 - val_loss: 0.0352 - val_accuracy: 0.9902\n", - "Epoch 42/100\n", - "1671/1671 [==============================] - 146s 87ms/step - loss: 0.0183 - accuracy: 0.9939 - val_loss: 0.0365 - val_accuracy: 0.9898\n", - "Epoch 43/100\n", - "1671/1671 [==============================] - 149s 89ms/step - loss: 0.0201 - accuracy: 0.9933 - val_loss: 0.0338 - val_accuracy: 0.9904\n", - "Epoch 44/100\n", - "1671/1671 [==============================] - 141s 85ms/step - loss: 0.0182 - accuracy: 0.9937 - val_loss: 0.0335 - val_accuracy: 0.9906\n", - "Epoch 45/100\n", - "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0173 - accuracy: 0.9941 - val_loss: 0.0309 - val_accuracy: 0.9911\n", - "Epoch 46/100\n", - "1671/1671 [==============================] - 129s 77ms/step - loss: 0.0190 - accuracy: 0.9935 - val_loss: 0.0329 - val_accuracy: 0.9910\n", - "Epoch 47/100\n", - "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0176 - accuracy: 0.9943 - val_loss: 0.0324 - val_accuracy: 0.9908\n", - "Epoch 48/100\n", - "1671/1671 [==============================] - 122s 73ms/step - loss: 0.0178 - accuracy: 0.9940 - val_loss: 0.0311 - val_accuracy: 0.9909\n", - "Epoch 49/100\n", - "1671/1671 [==============================] - 126s 76ms/step - loss: 0.0176 - accuracy: 0.9942 - val_loss: 0.0323 - val_accuracy: 0.9907\n", - "Epoch 50/100\n", - "1671/1671 [==============================] - 118s 71ms/step - loss: 0.0159 - accuracy: 0.9948 - val_loss: 0.0258 - val_accuracy: 0.9927\n", - "Epoch 51/100\n", - "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0172 - accuracy: 0.9943 - val_loss: 0.0268 - val_accuracy: 0.9925\n", - "Epoch 52/100\n", - "1671/1671 [==============================] - 118s 70ms/step - loss: 0.0166 - accuracy: 0.9943 - val_loss: 0.0255 - val_accuracy: 0.9932\n", - "Epoch 53/100\n", - "1671/1671 [==============================] - 133s 80ms/step - loss: 0.0164 - accuracy: 0.9946 - val_loss: 0.0237 - val_accuracy: 0.9935\n", - "Epoch 54/100\n", - "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0163 - accuracy: 0.9946 - val_loss: 0.0234 - val_accuracy: 0.9930\n", - "Epoch 55/100\n", - "1671/1671 [==============================] - 118s 71ms/step - loss: 0.0157 - accuracy: 0.9947 - val_loss: 0.0242 - val_accuracy: 0.9934\n", - "Epoch 56/100\n", - "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0160 - accuracy: 0.9948 - val_loss: 0.0226 - val_accuracy: 0.9930\n", - "Epoch 57/100\n", - "1671/1671 [==============================] - 121s 72ms/step - loss: 0.0155 - accuracy: 0.9948 - val_loss: 0.0251 - val_accuracy: 0.9926\n", - "Epoch 58/100\n", - "1671/1671 [==============================] - 123s 73ms/step - loss: 0.0162 - accuracy: 0.9946 - val_loss: 0.0238 - val_accuracy: 0.9930\n", - "Epoch 59/100\n", - "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0152 - accuracy: 0.9950 - val_loss: 0.0226 - val_accuracy: 0.9933\n", - "Epoch 60/100\n", - "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0158 - accuracy: 0.9947 - val_loss: 0.0282 - val_accuracy: 0.9920\n", - "Epoch 61/100\n", - "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0151 - accuracy: 0.9950 - val_loss: 0.0232 - val_accuracy: 0.9933\n", - "Epoch 62/100\n", - "1671/1671 [==============================] - 126s 75ms/step - loss: 0.0146 - accuracy: 0.9952 - val_loss: 0.0264 - val_accuracy: 0.9925\n", - "Epoch 63/100\n", - "1671/1671 [==============================] - 125s 75ms/step - loss: 0.0146 - accuracy: 0.9953 - val_loss: 0.0201 - val_accuracy: 0.9938\n", - "Epoch 64/100\n", - "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0146 - accuracy: 0.9953 - val_loss: 0.0248 - val_accuracy: 0.9925\n", - "Epoch 65/100\n", - "1671/1671 [==============================] - 124s 74ms/step - loss: 0.0144 - accuracy: 0.9953 - val_loss: 0.0204 - val_accuracy: 0.9937\n", - "Epoch 66/100\n", - "1671/1671 [==============================] - 127s 76ms/step - loss: 0.0136 - accuracy: 0.9955 - val_loss: 0.0198 - val_accuracy: 0.9940\n", - "Epoch 67/100\n", - "1671/1671 [==============================] - 132s 79ms/step - loss: 0.0138 - accuracy: 0.9953 - val_loss: 0.0227 - val_accuracy: 0.9927\n", - "Epoch 68/100\n", - "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0141 - accuracy: 0.9955 - val_loss: 0.0198 - val_accuracy: 0.9941\n", - "Epoch 69/100\n", - "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0138 - accuracy: 0.9955 - val_loss: 0.0222 - val_accuracy: 0.9937\n", - "Epoch 70/100\n", - "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0139 - accuracy: 0.9956 - val_loss: 0.0169 - val_accuracy: 0.9950\n", - "Epoch 71/100\n", - "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0132 - accuracy: 0.9957 - val_loss: 0.0195 - val_accuracy: 0.9946\n", - "Epoch 72/100\n", - "1671/1671 [==============================] - 119s 71ms/step - loss: 0.0136 - accuracy: 0.9955 - val_loss: 0.0211 - val_accuracy: 0.9939\n", - "Epoch 73/100\n", - "1671/1671 [==============================] - 120s 72ms/step - loss: 0.0130 - accuracy: 0.9958 - val_loss: 0.0219 - val_accuracy: 0.9937\n", - "Epoch 74/100\n", - "1671/1671 [==============================] - 141s 84ms/step - loss: 0.0137 - accuracy: 0.9957 - val_loss: 0.0177 - val_accuracy: 0.9939\n", - "Epoch 75/100\n", - "1671/1671 [==============================] - 175s 105ms/step - loss: 0.0135 - accuracy: 0.9957 - val_loss: 0.0194 - val_accuracy: 0.9945: 0.99\n", - "Epoch 76/100\n", - "1671/1671 [==============================] - 148s 88ms/step - loss: 0.0128 - accuracy: 0.9958 - val_loss: 0.0214 - val_accuracy: 0.9938\n", - "Epoch 77/100\n", - "1671/1671 [==============================] - 151s 90ms/step - loss: 0.0118 - accuracy: 0.9962 - val_loss: 0.0178 - val_accuracy: 0.9944\n", - "Epoch 78/100\n", - "1671/1671 [==============================] - 165s 99ms/step - loss: 0.0132 - accuracy: 0.9958 - val_loss: 0.0202 - val_accuracy: 0.9943\n", - "Epoch 79/100\n", - "1671/1671 [==============================] - 189s 113ms/step - loss: 0.0130 - accuracy: 0.9957 - val_loss: 0.0182 - val_accuracy: 0.9941\n", - "Epoch 80/100\n", - "1671/1671 [==============================] - 171s 102ms/step - loss: 0.0129 - accuracy: 0.9958 - val_loss: 0.0181 - val_accuracy: 0.9945\n", - "Epoch 81/100\n", - "1671/1671 [==============================] - 159s 95ms/step - loss: 0.0127 - accuracy: 0.9959 - val_loss: 0.0157 - val_accuracy: 0.9955\n", - "Epoch 82/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0121 - accuracy: 0.9961 - val_loss: 0.0184 - val_accuracy: 0.9948\n", - "Epoch 83/100\n", - "1671/1671 [==============================] - 163s 98ms/step - loss: 0.0123 - accuracy: 0.9961 - val_loss: 0.0163 - val_accuracy: 0.9954\n", - "Epoch 84/100\n", - "1671/1671 [==============================] - 175s 105ms/step - loss: 0.0125 - accuracy: 0.9960 - val_loss: 0.0188 - val_accuracy: 0.9948\n", - "Epoch 85/100\n", - "1671/1671 [==============================] - 166s 99ms/step - loss: 0.0125 - accuracy: 0.9961 - val_loss: 0.0152 - val_accuracy: 0.9952\n", - "Epoch 86/100\n", - "1671/1671 [==============================] - 160s 96ms/step - loss: 0.0130 - accuracy: 0.9958 - val_loss: 0.0161 - val_accuracy: 0.9953\n", - "Epoch 87/100\n", - "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0120 - accuracy: 0.9961 - val_loss: 0.0168 - val_accuracy: 0.9956\n", - "Epoch 88/100\n", - "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0120 - accuracy: 0.9960 - val_loss: 0.0169 - val_accuracy: 0.9949\n", - "Epoch 89/100\n", - "1671/1671 [==============================] - 133s 80ms/step - loss: 0.0129 - accuracy: 0.9959 - val_loss: 0.0155 - val_accuracy: 0.9955\n", - "Epoch 90/100\n", - "1671/1671 [==============================] - 145s 87ms/step - loss: 0.0117 - accuracy: 0.9962 - val_loss: 0.0147 - val_accuracy: 0.9956\n", - "Epoch 91/100\n", - "1671/1671 [==============================] - 143s 85ms/step - loss: 0.0115 - accuracy: 0.9964 - val_loss: 0.0143 - val_accuracy: 0.9956\n", - "Epoch 92/100\n", - "1671/1671 [==============================] - 142s 85ms/step - loss: 0.0116 - accuracy: 0.9964 - val_loss: 0.0192 - val_accuracy: 0.9943\n", - "Epoch 93/100\n", - "1671/1671 [==============================] - 137s 82ms/step - loss: 0.0117 - accuracy: 0.9963 - val_loss: 0.0156 - val_accuracy: 0.9952\n", - "Epoch 94/100\n", - "1671/1671 [==============================] - 131s 78ms/step - loss: 0.0118 - accuracy: 0.9963 - val_loss: 0.0137 - val_accuracy: 0.9955\n", - "Epoch 95/100\n", - "1671/1671 [==============================] - 142s 85ms/step - loss: 0.0115 - accuracy: 0.9962 - val_loss: 0.0123 - val_accuracy: 0.9961\n", - "Epoch 96/100\n", - "1671/1671 [==============================] - 145s 87ms/step - loss: 0.0115 - accuracy: 0.9964 - val_loss: 0.0134 - val_accuracy: 0.9958\n", - "Epoch 97/100\n", - "1671/1671 [==============================] - 141s 85ms/step - loss: 0.0109 - accuracy: 0.9965 - val_loss: 0.0136 - val_accuracy: 0.9955\n", - "Epoch 98/100\n", - "1671/1671 [==============================] - 143s 86ms/step - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.0133 - val_accuracy: 0.9960\n", - "Epoch 99/100\n", - "1671/1671 [==============================] - 144s 86ms/step - loss: 0.0121 - accuracy: 0.9960 - val_loss: 0.0147 - val_accuracy: 0.9954\n", - "Epoch 100/100\n", - "1671/1671 [==============================] - 139s 83ms/step - loss: 0.0111 - accuracy: 0.9966 - val_loss: 0.0159 - val_accuracy: 0.9952\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "input_size = 784\n", - "output_size = 49\n", - "standard_hidden_layer_width = 512\n", - "NUM_EPOCHS = 100\n", - "VALIDATION_STEPS = num_validation_samples // BATCH_SIZE\n", - "early_stopping = tf.keras.callbacks.EarlyStopping(patience=2)\n", - "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)\n", - "\n", - "model = tf.keras.Sequential([\n", - " tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),\n", - " tf.keras.layers.MaxPooling2D(2, 2),\n", - " tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n", - " tf.keras.layers.MaxPooling2D(2,2),\n", - " tf.keras.layers.Dropout(0.25),\n", - " tf.keras.layers.Flatten(),\n", - " tf.keras.layers.Dense(standard_hidden_layer_width, activation='relu'),\n", - " tf.keras.layers.Dense(output_size, activation='softmax')\n", - " ])\n", - "\n", - "model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "model.fit(train_dataset, epochs=NUM_EPOCHS, callbacks=[reduce_lr], validation_data=(validation_inputs, validation_targets), validation_steps=VALIDATION_STEPS, verbose=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\victo\\Anaconda3\\envs\\py3-TF2.0\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "If using Keras pass *_constraint arguments to layers.\n", - "INFO:tensorflow:Assets written to: k49-convnet\\assets\n" - ] - } - ], - "source": [ - "model.save('k49-convnet')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "complete_model = tf.keras.models.load_model('k49-convnet')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = complete_model.predict([test_data])" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1000, 1000, 1000, 126, 1000, 1000, 1000, 1000, 767, 1000, 1000, 1000, 1000, 678, 629, 1000, 418, 1000, 1000, 1000, 1000, 1000, 336, 399, 1000, 1000, 836, 1000, 1000, 324, 1000, 498, 280, 552, 1000, 1000, 260, 1000, 1000, 1000, 1000, 1000, 348, 390, 68, 64, 1000, 1000, 574]\n", - "[960, 970, 971, 113, 957, 889, 931, 929, 722, 926, 954, 921, 909, 593, 562, 963, 405, 959, 933, 945, 909, 941, 315, 364, 934, 931, 799, 910, 945, 294, 967, 466, 249, 528, 970, 948, 246, 980, 918, 927, 928, 968, 331, 354, 50, 58, 964, 978, 474]\n", - "0.930142277358246\n" - ] - } - ], - "source": [ - "totals = []\n", - "for cls in range(49):\n", - " total = 0\n", - " for i in test_labels:\n", - " if i == cls:\n", - " total = total + 1\n", - " totals.append(total)\n", - "\n", - "hits = []\n", - "for cls in range(49):\n", - " total_hits = 0\n", - " for i in range(0,test_labels.shape[0]):\n", - " if test_labels[i] == cls == np.argmax(predictions[i]):\n", - " total_hits = total_hits + 1\n", - " hits.append(total_hits)\n", - " \n", - "accuracy_list = []\n", - "for i in range(0,len(hits)):\n", - " accuracy = hits[i]/totals[i]\n", - " accuracy_list.append(accuracy)\n", - "\n", - "print(np.mean(accuracy_list))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:py3-TF2.0]", - "language": "python", - "name": "conda-env-py3-TF2.0-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}