from tensorflow.keras.datasets import fashion_mnist

from tensorflow.keras.layers import Dense

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(Dense(10, input_shape=input_shape, activation='softmax'))

    model.add(Dense(50, input_shape=input_shape, activation='softmax'))

    model.add(Dense(100, input_shape=input_shape, activation='softmax'))

    model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy',

                  metrics=['acc'])

 

    return model

 

def train_model(model, train_data, test_data, batch_size=32, epochs=5,

               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