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Sequential是多个网络层的线性堆叠
可以通过向Sequential模型传递一个layer的list来构造该模型：
from keras.models import Sequential
from keras.layers import Dense, Activation model = Sequential([
Dense(32, input_dim=784),
Activation('relu'),
Dense(10),
Activation('softmax'),
])
也可以通过.add()方法一个个的将layer加入模型中：
model = Sequential()
model.add(Dense(32, input_dim=784))
model.add(Activation('relu'))
还可以通过merge将两个Sequential模型通过某种方式合并
Sequential模型的方法：
compile(self, optimizer, loss, metrics=[], sample_weight_mode=None) fit(self, x, y, batch_size=32, nb_epoch=10, verbose=1, callbacks=[], validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None) evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None) #按batch获得输入数据对应的输出，函数的返回值是预测值的numpy array
predict(self, x, batch_size=32, verbose=0) #按batch产生输入数据的类别预测结果,函数的返回值是类别预测结果的numpy array或numpy
predict_classes(self, x, batch_size=32, verbose=1) #本函数按batch产生输入数据属于各个类别的概率，函数的返回值是类别概率的numpy array
predict_proba(self, x, batch_size=32, verbose=1) train_on_batch(self, x, y, class_weight=None, sample_weight=None) test_on_batch(self, x, y, sample_weight=None) predict_on_batch(self, x) fit_generator(self, generator, samples_per_epoch, nb_epoch, verbose=1, callbacks=[], validation_data=None, nb_val_samples=None, class_weight=None, max_q_size=10) evaluate_generator(self, generator, val_samples, max_q_size=10)