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from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.datasets import mnist
from keras.utils import np_utils
from keras.optimizers import SGD
model = Sequential()
(X_train, Y_train), (X_test, Y_test) = mnist.load_data()
X_train = X_train.reshape(-1, 28*28).astype('float32')/255
Y_train = np_utils.to_categorical(Y_train, 10)
X_test = X_test.reshape(-1, 28*28).astype('float32')/255
Y_test = np_utils.to_categorical(Y_test, 10)
model.add(Dense(output_dim=100, input_dim=784))
model.add(Activation("sigmoid"))
model.add(Dense(output_dim=10, input_dim=100))
model.add(Activation("softmax"))
model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.01), metrics=['accuracy'])
model.fit(X_train, Y_train, nb_epoch=5, batch_size=100)
loss_and_metrics = model.evaluate(X_test, Y_test, batch_size=32)
print(loss_and_metrics)
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