recurrent_lasagne_power.py 文件源码

def build_model(input_var=None):

    layers = [1, 5, 10, 1]

    l_in = InputLayer((None, None, layers[0]),
                      input_var=input_var)

    l_lstm1 = LSTMLayer(l_in, layers[1])
    l_lstm1_dropout = DropoutLayer(l_lstm1, p=0.2)

    l_lstm2 = LSTMLayer(l_lstm1_dropout, layers[2])
    l_lstm2_dropout = DropoutLayer(l_lstm2, p=0.2)

    # The objective of this task depends only on the final value produced by
    # the network.  So, we'll use SliceLayers to extract the LSTM layer's
    # output after processing the entire input sequence.  For the forward
    # layer, this corresponds to the last value of the second (sequence length)
    # dimension.
    l_slice = SliceLayer(l_lstm2_dropout, -1, 1)

    l_out = DenseLayer(l_slice, 1, nonlinearity=lasagne.nonlinearities.linear)

    return l_out