70e4d7fe60a9658bb27b9f5fb67592a1222b2ec3,spotlight/sequence/representations.py,CNNNet,user_representation,#CNNNet#Any#,114

Before Change

            x = cnn_layer(x)

        user_representations = x.view(batch_size, dim, -1)
        pooled_representations = (user_representations
                                  .max(-1)[0]
                                  .view(batch_size, dim))

        return pooled_representations

    def forward(self, user_representations, targets):

After Change

            x = F.pad(x, (0, 0, self.kernel_width - min(i, 1), 0))
            x = F.relu(cnn_layer(x))

        x = x.squeeze(3)

        return x[:, :, :-1], x[:, :, -1]

    def forward(self, user_representations, targets):

Instances