20180127_mnist_mlp.py

# -*- coding: utf-8 -*-
# https://rakuishi.com/archives/getting-started-with-keras/

from keras.datasets import mnist
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Activation

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

x_train = x_train.reshape(-1, 784) / 255
x_test = x_test.reshape(-1, 784) /255
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

model = Sequential() # モデルを作成
model.add(Dense(units=256, input_shape=(784,))) # 784 -> 256 に線形変換
model.add(Activation('relu')) # ReLU 関数で活性化
model.add(Dense(units=100))
model.add(Activation('relu'))
model.add(Dense(units=10)) # 最終的に 0 ~ 9 にする
model.add(Activation('softmax'))

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

model.fit(
  x_train, y_train,
  batch_size=100, epochs=10,
  validation_data=(x_test, y_test)
)