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

Before Change

        sequence_embeddings = self.item_embeddings(item_sequences)

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

        // Move embedding dimensions to channels and add a fourth dim.
        sequence_embeddings = (sequence_embeddings

After Change

    def user_representation(self, item_sequences):

        // Make the embedding dimension the channel dimension
        sequence_embeddings = (self.item_embeddings(item_sequences)
                               .permute(0, 2, 1))
        // Add a trailing dimension of 1
        sequence_embeddings = (sequence_embeddings
                               .unsqueeze(3))

        x = sequence_embeddings
        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)

Instances