from tensorflow.keras.datasets import fashion_mnist

from tensorflow.keras.layers import Conv2D, Flatten, Dense, AvgPool2D

from tensorflow.keras.models import Sequential

import numpy as np

 

 

def load_train(path):

    features_train = np.load(path + 'train_features.npy')

    target_train = np.load(path + 'train_target.npy')

    features_train = features_train.reshape(features_train.shape[0], 28 * 28) / 255.

    return features_train, target_train

 

 

def create_model(input_shape):

    model = Sequential()

    model.add(Conv2D(filters=6, kernel_size=(5, 5), padding='same',

                 activation="relu", input_shape=input_shape))

    model.add(AvgPool2D(pool_size=(2, 2), strides=None, padding='valid'))

    model.add(Conv2D(filters=16, kernel_size=(5, 5), padding='valid',activation="relu"))

    model.add(Flatten())

    model.add(Dense(units=10, activation='softmax'))

    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc'])

 

    return model

 

 

def train_model(model, train_data, test_data, batch_size=32, epochs=100,

               steps_per_epoch=None, validation_steps=None):

 

    features_train, target_train = train_data

    features_test, target_test = test_data

    model.fit(features_train, target_train,

              validation_data=(features_test, target_test),

              batch_size=batch_size, epochs=epochs,

              steps_per_epoch=steps_per_epoch,

              validation_steps=validation_steps,

              verbose=2, shuffle=True)

 

    return model