model.add(Convolution2D(nb_filter=32, nb_row=3, nb_col=3, dim_ordering="th", border_mode="same", bias=False, init="uniform"))

model.add(Convolution2D(nb_filter=32, nb_row=3, nb_col=3, dim_ordering="th", border_mode="same", bias=False, init="uniform"))

model.add(AveragePooling2D(pool_size=(2, 2), dim_ordering="th"))

model.add(Flatten())

model.add(Dense(output_dim=1000, activation="sigmoid"))

model.add(Dense(output_dim=1000, activation="sigmoid"))

model.add(Dense(output_dim=10, activation="sigmoid"))

with open("digits_model.json", "w") as f:

f.write(model.to_json())

plot(model, to_file="digits_model.png", show_shapes=True)

model.compile(loss="mse", optimizer="rmsprop", metrics=["accuracy"])

model.fit(train_digits, train_labels_one_hot, batch_size=32, nb_epoch=10, callbacks=[history])

model.save_weights("digits_model_weights.hdf5")

predict_labels = model.predict_classes(test_digits)

print(classification_report(test_labels, predict_labels))

print(accuracy_score(test_labels, predict_labels))

print(confusion_matrix(test_labels, predict_labels))

五、参考书目

［1］《最优化导论》（美）Edwin K. P. Chong（美）Stanislaw H. Zak

［2］《神经网络设计》（美）Martin T.Hagan（美）Howard B.Demuth（美）Mark Beale

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