Created
March 4, 2017 01:24
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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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