d4aa09c5b5b762b0b809d349cb7a8f50212a7107,theanolm/layers/softmaxlayer.py,SoftmaxLayer,__init__,#SoftmaxLayer#,17

Before Change

        // Create the parameters. Weight matrix and bias for each input.
        output_size = self.output_size
        for input_index, input_layer in enumerate(self.input_layers):
            input_size = input_layer.output_size
            param_name = "input" + str(input_index) + "/W"
            self._init_random_weight(param_name,
                                     (input_size, output_size),
                                     scale=0.01)
            param_name = "input" + str(input_index) + "/b"
            self._init_bias(param_name, output_size)

    def create_structure(self):
        Creates the symbolic graph of this layer.

        The input is always 3-dimensional: the first dimension is the time step,

After Change

        super().__init__(*args, **kwargs)

        // Create the parameters. Weight matrix and bias for each input.
        input_size = sum(x.output_size for x in self.input_layers)
        output_size = self.output_size
        self._init_random_weight("input/W",
                                 (input_size, output_size),
                                 scale=0.01)

Instances