Keras：错误的训练时期数

Question

我正在尝试构建一个 class 来快速初始化和训练自动编码器以进行快速原型制作。我希望能够做的一件事是快速调整我训练的时期数。但是，似乎无论我做什么，模型都会对每一层进行 100 个 epoch 的训练！我正在使用张量流后端。

这是两种违规方法的代码。

    def pretrain(self, X_train, nb_epoch = 10):
    data = X_train
    for ae in self.pretrains:            
        ae.fit(data, data, nb_epoch = nb_epoch)
        ae.layers[0].output_reconstruction = False
        ae.compile(optimizer='sgd', loss='mse')
        data = ae.predict(data)

.........

    def fine_train(self, X_train, nb_epoch):
    weights = [ae.layers[0].get_weights() for ae in self.pretrains]

    dims = self.dims
    encoder = containers.Sequential()
    decoder = containers.Sequential()

    ## add special input encoder
    encoder.add(Dense(output_dim = dims[1], input_dim = dims[0], 
        weights = weights[0][0:2], activation = 'linear'))

    ## add the rest of the encoders
    for i in range(1, len(dims) - 1):
        encoder.add(Dense(output_dim = dims[i+1],
            weights = weights[i][0:2], activation = self.act))

    ## add the decoders from the end

    decoder.add(Dense(output_dim = dims[len(dims) - 2], input_dim = dims[len(dims) - 1],
        weights = weights[len(dims) - 2][2:4], activation = self.act))

    for i in range(len(dims) - 2, 1, -1):
        decoder.add(Dense(output_dim = dims[i - 1],
            weights = weights[i-1][2:4], activation = self.act))

    ## add the output layer decoder
    decoder.add(Dense(output_dim = dims[0], 
        weights = weights[0][2:4], activation = 'linear'))



    masterAE = AutoEncoder(encoder = encoder, decoder = decoder)
    masterModel = models.Sequential()
    masterModel.add(masterAE)
    masterModel.compile(optimizer = 'sgd', loss = 'mse')
    masterModel.fit(X_train, X_train, nb_epoch = nb_epoch)
    self.model = masterModel

如有任何关于如何解决问题的建议，我们将不胜感激。最初怀疑是tensorflow的问题，于是尝试了运行theano后端也遇到了同样的问题。

Here 是完整程序的 link。

Answer 1

继 Keras doc 之后，fit 方法使用默认的 100 个训练周期 (nb_epoch=100)：

fit(X, y, batch_size=128, nb_epoch=100, verbose=1, callbacks=[], validation_split=0.0, validation_data=None, shuffle=True, show_accuracy=False, class_weight=None, sample_weight=None)

我确定你是如何运行使用这些方法的，但是按照 original code 中的 "Typical usage"，你应该能够运行类似（根据需要调整变量 num_epoch):

#Typical usage:
num_epoch = 10
ae = JPAutoEncoder(dims)
ae.pretrain(X_train, nb_epoch = num_epoch)
ae.train(X_train, nb_epoch = num_epoch)
ae.predict(X_val)

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