conv_network.py

from tensorflow.python.keras.layers import Dense, Flatten, Conv2D
from tensorflow.python.keras.models import Sequential
from tensorflow.python import keras
import numpy as np

num_classes = 10
img_rows, img_cols = 28, 28
seed = 7
np.random.seed(seed)


def model(train_x, train_y, test_x, test_y, epoch):
    '''

    :param train_x: train features
    :param train_y: train labels
    :param test_x:  test features
    :param test_y: test labels
    :param epoch: no. of epochs
    :return:
    '''
    conv_model = Sequential()
    # first layer with input shape (img_rows, img_cols, 1) and 12 filters
    conv_model.add(Conv2D(12, kernel_size=(3, 3), activation='relu',
                          input_shape=(img_rows, img_cols, 1)))
    # second layer with 12 filters
    conv_model.add(Conv2D(12, kernel_size=(3, 3), activation='relu'))
    # third layer with 12 filers
    conv_model.add(Conv2D(12, kernel_size=(3, 3), activation='relu'))
    # flatten layer
    conv_model.add(Flatten())
    # adding a Dense layer
    conv_model.add(Dense(100, activation='relu'))
    # adding the final Dense layer with softmax
    conv_model.add(Dense(num_classes, activation='softmax'))

    # compile the model
    conv_model.compile(optimizer=keras.optimizers.Adadelta(),
                       loss='categorical_crossentropy',
                       metrics=['accuracy'])
    print("\n Training the Convolution Neural Network on MNIST data\n")
    # fit the model
    conv_model.fit(train_x, train_y, batch_size=128, epochs=epoch,
                   validation_split=0.1, verbose=2)
    predicted_train_y = conv_model.predict(train_x)
    train_accuracy = (sum(np.argmax(predicted_train_y, axis=1)
                          == np.argmax(train_y, axis=1))/(float(len(train_y))))
    print('Train accuracy : ', train_accuracy)
    predicted_test_y = conv_model.predict(test_x)
    test_accuracy = (sum(np.argmax(predicted_test_y, axis=1)
                         == np.argmax(test_y, axis=1))/(float(len(test_y))))
    print('Test accuracy : ', test_accuracy)
    CNN_accuracy = {'train_accuracy': train_accuracy,
                    'test_accuracy': test_accuracy, 'epoch': epoch}
    return conv_model, CNN_accuracy