fcf346e85356a73472a10c564a0a40011139f6be,spotlight/sequence/representations.py,CNNNet,user_representation,#CNNNet#Any#,114

Before Change

                                                        1,
                                                        seq_len,
                                                        dim))
        x = self.cnn_layers[](sequence_embeddings)

        user_representations = x.view(batch_size, dim, -1)
        pooled_representations = (user_representations

After Change

        (batch_size, seq_len, dim) = sequence_embeddings.size()

        // Move embedding dimensions to channels and add a fourth dim.
        sequence_embeddings = (sequence_embeddings
                               .permute(0, 2, 1)
                               .contiguous()
                               .view(batch_size, dim, seq_len, 1))

        x = sequence_embeddings
        for cnn_layer in self.cnn_layers:
            x = cnn_layer(x)

Instances