023331ec2a7b0086abfc81eca16c84a1692ee653,keras/layers/convolutional.py,Convolution1D,build,#Convolution1D#Any#,127

Before Change

    def build(self, input_shape):
        input_dim = input_shape[2]
        self.W_shape = (self.filter_length, 1, input_dim, self.nb_filter)

        self.W = self.add_weight(self.W_shape,
                                 initializer=self.init,
                                 name="{}_W".format(self.name),
                                 regularizer=self.W_regularizer,
                                 constraint=self.W_constraint)
        if self.bias:
            self.b = self.add_weight((self.nb_filter,),
                                     initializer="zero",
                                     name="{}_b".format(self.name),
                                     regularizer=self.b_regularizer,
                                     constraint=self.b_constraint)
        else:
            self.b = None

        if self.initial_weights is not None:
            self.set_weights(self.initial_weights)
            del self.initial_weights
        self.built = True

    def get_output_shape_for(self, input_shape):
        length = conv_output_length(input_shape[1],

After Change

            **kwargs)
        self.input_spec = [InputSpec(ndim=4)]

    def build(self, input_shape):
        if len(input_shape) != 4:
            raise ValueError("Inputs should have rank " +
                             str(4) +
                             "Received input shape:", str(input_shape))
        if self.data_format == "channels_first":
            channel_axis = 1
        else:
            channel_axis = -1
        if input_shape[channel_axis] is None:
            raise ValueError("The channel dimension of the inputs "
                             "should be defined. Found `None`.")
        input_dim = input_shape[channel_axis]
        kernel_shape = self.kernel_size + (self.filters, input_dim)

        self.kernel = self.add_weight(kernel_shape,
                                      initializer=self.kernel_initializer,
                                      name="kernel",
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        if self.use_bias:
            self.bias = self.add_weight((self.filters,),
                                        initializer=self.bias_initializer,
                                        name="bias",
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)

Instances