network.py 文件源码

def __init__(self, output_dim, hidden_sizes, hidden_nonlinearity,
                 output_nonlinearity, hidden_W_init=LI.GlorotUniform(), hidden_b_init=LI.Constant(0.),
                 output_W_init=LI.GlorotUniform(), output_b_init=LI.Constant(0.),
                 name=None, input_var=None, input_layer=None, input_shape=None, batch_norm=False):

        Serializable.quick_init(self, locals())

        if name is None:
            prefix = ""
        else:
            prefix = name + "_"

        if input_layer is None:
            l_in = L.InputLayer(shape=(None,) + input_shape, input_var=input_var)
        else:
            l_in = input_layer
        self._layers = [l_in]
        l_hid = l_in
        for idx, hidden_size in enumerate(hidden_sizes):
            l_hid = L.DenseLayer(
                l_hid,
                num_units=hidden_size,
                nonlinearity=hidden_nonlinearity,
                name="%shidden_%d" % (prefix, idx),
                W=hidden_W_init,
                b=hidden_b_init,
            )
            if batch_norm:
                l_hid = L.batch_norm(l_hid)
            self._layers.append(l_hid)

        l_out = L.DenseLayer(
            l_hid,
            num_units=output_dim,
            nonlinearity=output_nonlinearity,
            name="%soutput" % (prefix,),
            W=output_W_init,
            b=output_b_init,
        )
        self._layers.append(l_out)
        self._l_in = l_in
        self._l_out = l_out
        # self._input_var = l_in.input_var
        self._output = L.get_output(l_out)
        LasagnePowered.__init__(self, [l_out])