Deep_Learning/optimized_cnnmodel.py at master · Punam918/Deep_Learning · GitHub

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# -*- coding: utf-8 -*-
"""Optimized_CNNModel.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1-eqQonO0JtMWvF1V8iCi12fo28Mu1_M6

## Create CNN Model and Optimize it using Keras Tuner
"""

!pip install keras-tuner

import tensorflow as tf
from tensorflow import keras
import numpy as np

print(tf.__version__)

fashion_mnist=keras.datasets.fashion_mnist

(train_images,train_labels),(test_images,test_labels)=fashion_mnist.load_data()

train_images=train_images/255.0
test_images=test_images/255.0

train_images[0].shape

train_images=train_images.reshape(len(train_images),28,28,1)
test_images=test_images.reshape(len(test_images),28,28,1)

def build_model(hp):
  model = keras.Sequential([
    keras.layers.Conv2D(
        filters=hp.Int('conv_1_filter', min_value=32, max_value=128, step=16),
        kernel_size=hp.Choice('conv_1_kernel', values = [3,5]),
        activation='relu',
        input_shape=(28,28,1)
    ),
    keras.layers.Conv2D(
        filters=hp.Int('conv_2_filter', min_value=32, max_value=64, step=16),
        kernel_size=hp.Choice('conv_2_kernel', values = [3,5]),
        activation='relu'
    ),
    keras.layers.Flatten(),
    keras.layers.Dense(
        units=hp.Int('dense_1_units', min_value=32, max_value=128, step=16),
        activation='relu'
    ),
    keras.layers.Dense(10, activation='softmax')
  ])

  model.compile(optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', values=[1e-2, 1e-3])),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

  return model

from kerastuner import RandomSearch
from kerastuner.engine.hyperparameters import HyperParameters

tuner_search=RandomSearch(build_model,
                          objective='val_accuracy',
                          max_trials=5,directory='output',project_name="Mnist Fashion")

tuner_search.search(train_images,train_labels,epochs=3,validation_split=0.1)

model=tuner_search.get_best_models(num_models=1)[0]

model.summary()

model.fit(train_images, train_labels, epochs=10, validation_split=0.1, initial_epoch=3)