mnist-example.py

# from https://weblabo.oscasierra.net/python/keras-mnist-sample.html

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

# read MNIST
(x_train, y_train), (x_test, y_test) = mnist.load_data()

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

# build models
model = Sequential() 
model.add(InputLayer(input_shape=(784,)))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

# train
epochs = 20
batch_size = 120
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))

# proof
score = model.evaluate(x_test, y_test, verbose=1)
print()
print(f"Test loss: {score[0]}")
print(f"Test accuracy: {score[1]}")