Torch Dataloader
Torch Layers
-
class
nvtabular.framework_utils.torch.layers.embeddings.ConcatenatedEmbeddings(embedding_table_shapes, dropout=0.0, sparse_columns=())[source] Bases:
torch.nn.modules.module.ModuleMap multiple categorical variables to concatenated embeddings.
- Parameters
embedding_table_shapes – A dictionary mapping column names to (cardinality, embedding_size) tuples.
dropout – A float.
sparse_columns – A list of sparse columns
- Inputs:
x: An int64 Tensor with shape [batch_size, num_variables].
- Outputs:
A Float Tensor with shape [batch_size, embedding_size_after_concat].
-
class
nvtabular.framework_utils.torch.layers.embeddings.MultiHotEmbeddings(embedding_table_shapes, dropout=0.0, mode='sum')[source] Bases:
torch.nn.modules.module.ModuleMap multiple categorical variables to concatenated embeddings.
- Parameters
embedding_dict_shapes – A dictionary mapping column names to (cardinality, embedding_size) tuples.
dropout – A float.
- Inputs:
- x: A dictionary with multi-hot column name as keys and a tuple
containing the column values and offsets as values.
- Outputs:
A Float Tensor with shape [batch_size, embedding_size_after_concat].
-
class
nvtabular.framework_utils.torch.models.Model(embedding_table_shapes, num_continuous, emb_dropout, layer_hidden_dims, layer_dropout_rates, max_output=None, bag_mode='sum')[source] Bases:
torch.nn.modules.module.ModuleGeneric Base Pytorch Model, that contains support for Categorical and Continuous values.
- Parameters
embedding_tables_shapes (dict) – A dictionary representing the <column>: <max cardinality of column> for all categorical columns.
num_continuous (int) – Number of continuous columns in data.
emb_dropout (float, 0 - 1) – Sets the embedding dropout rate.
layer_hidden_dims (list) – Hidden layer dimensions.
layer_dropout_rates (list) – A list of the layer dropout rates expressed as floats, 0-1, for each layer
max_output (float) – Signifies the max output.