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x = data.iloc[:,:36].as_matrix() #36个属性
y = data.iloc[:,36].as_matrix() #最后一列是标签,0或者1
x_train,x_test,y_train,y_test=model_selection.train_test_split(x,y,test_size=0.2)
x_train=preprocessing.normalize(x_train)
x_test=preprocessing.normalize(x_test)
model = Sequential()
model.add(Dense(36, input_dim=36, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model.fit(x_train, y_train,shuffle=True,
epochs=5,
batch_size=20)
score = model.evaluate(x_test, y_test, batch_size=20)
y_predict= model.predict_classes(x_test,batch_size=5)
print(y_predict)