from tensorflow.keras import Sequential

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

import matplotlib.pyplot as plt

import numpy as np

 

 

features_train = np.load('/datasets/fashion_mnist/train_features.npy')

target_train = np.load('/datasets/fashion_mnist/train_target.npy')

features_test = np.load('/datasets/fashion_mnist/test_features.npy')

target_test = np.load('/datasets/fashion_mnist/test_target.npy')

 

 

features_train = features_train.reshape(-1, 28, 28, 1) / 255.0

features_test = features_test.reshape(-1, 28, 28, 1) / 255.0

 

model = Sequential()

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

                 input_shape=(28, 28, 1)))

model.add(AvgPool2D(pool_size=(2, 2)))

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

model.add(AvgPool2D(pool_size=(2, 2)))

model.add(Flatten())

model.add(Dense(units=120, activation='tanh'))

model.add(Dense(units=84, activation='tanh'))

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

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

 

model.summary()

model.fit(features_train, target_train, epochs=1, verbose=1,

          steps_per_epoch=1, batch_size=1)