396303a7b84fe4f8b304a5878c487ff0b3a16097,spotlight/sequence/representations.py,CNNNet,user_representation,#CNNNet#,297

Before Change

        for i, cnn_layer in enumerate(self.cnn_layers):
            // Pad so that the CNN doesn"t have the future
            // of the sequence in its receptive field.
            x = F.pad(x, (0, 0, self.kernel_width - min(i, 1), 0))
            x = F.relu(cnn_layer(x))

        x = x.squeeze(3)

After Change

                               .unsqueeze(3))

        receptive_field_width = (self.kernel_width +
                                 (self.kernel_width - 1) *
                                 (self.dilation - 1))

        // Pad so that the CNN doesn"t have the future
        // of the sequence in its receptive field.
        x = F.pad(sequence_embeddings,
                  (0, 0, receptive_field_width, 0))
        x = F.tanh(self.cnn_layers[0](x))

        for cnn_layer in self.cnn_layers[1:]:

            x = F.pad(x, (0, 0, receptive_field_width - 1, 0))
            x = F.tanh(cnn_layer(x))

        x = x.squeeze(3)

Instances