from tensorflow import keras

from keras.datasets import fashion_mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop

batch_size = 128
epochs = 5
dropout = 0.2
lr = 0.001
lr_decay = 0.0

(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(dropout))
model.add(Dense(512, activation='relu'))
model.add(Dropout(dropout))
model.add(Dense(10, activation='softmax'))

model.compile(
    loss='categorical_crossentropy',
    optimizer=RMSprop(lr=lr, decay=lr_decay),
    metrics=['accuracy'],
)

model.fit(
    x_train,
    y_train,
    batch_size=batch_size,
    epochs=epochs,
    verbose=1,
    validation_data=(x_test, y_test),
    callbacks=[keras.callbacks.TensorBoard()],
)
