Tiny MLP on MNIST with Keras - stripped down from Keras examples, functional model API

tiny_keras_mnist_mlp.py

from keras.datasets import mnist
from keras.models import Model
from keras.layers import Dense, Input
from keras.utils import to_categorical

num_classes = 10

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(60000, 784).astype('float32') / 255
y_train = to_categorical(y_train, num_classes)

image = Input(shape=(784,))
x = Dense(512, activation='relu')(image)
digit = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=image, outputs=digit)

model.compile(optimizer='sgd', loss='categorical_crossentropy')

history = model.fit(x_train, y_train, batch_size=128, epochs=1, verbose=1)