ex1

# -*- coding: utf-8 -*-


import tensorflow as tf

import numpy as np
import matplotlib.pyplot as plt

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()

print("x_train shape:", x_train.shape, "y_train shape:", y_train.shape)


plt.imshow(x_train[1])


x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255



model = tf.keras.Sequential()
# Must define the input shape in the first layer of the neural network
model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=2, padding='same', activation='relu', input_shape=(28,28,1)))
model.add(tf.keras.layers.MaxPooling2D(pool_size=2))
model.add(tf.keras.layers.Dropout(0.3))
model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))
model.add(tf.keras.layers.MaxPooling2D(pool_size=2))
model.add(tf.keras.layers.Dropout(0.3))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(256, activation='relu'))
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
# Take a look at the model summary
model.summary()




model.compile(loss='categorical_crossentropy',
             optimizer='adam',
             metrics=['accuracy'])




model.fit(x_train,
         y_train,
         batch_size=64,
         epochs=10,
         validation_data=(x_train, y_train)
         )



score = model.evaluate(x_test, y_test, verbose=0)

print('\n', 'Test accuracy:', score[1])