Keras：错误的训练时期

Question

I'm trying to build a class to quickly initialize and train an autoencoder for rapid prototyping. One thing I'd like to be able to do is quickly adjust the number of epochs I train for. However, it seems like no matter what I do, the model trains each layer for 100 epochs! I'm using the tensorflow backend.

Here is the code from the two offending methods.

    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

Any suggestions on how to fix the problem would be appreciated. My original suspicion was that it was something to do with tensorflow, so I tried running with the theano backend but encountered the same problem.

Here is a link to the full program.

Answer 1

Following the Keras doc , the fit method uses a default of 100 training epochs ( 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)

I'm sure how you are running these methods, but following the "Typical usage" from the original code , you should be able to run something like (adjusting the variable num_epoch as required):

#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)