merlin.models.tf.CausalLanguageModeling

class merlin.models.tf.CausalLanguageModeling(*args, **kwargs)

Bases: merlin.models.tf.blocks.core.masking.MaskingBlock

In Causal Language Modeling (clm) you predict the next item based on past positions of the sequence. Future positions are masked. :param padding_idx: Index of padding item, used for masking and for getting batch of sequences

with the same length. Defaults to 0

Parameters

• eval_on_last_item_seq_only (bool) – When set to True, predict only the last non-padded item during evaluation Defaults to True

• item_id_feature_name (str) – Name of the column containing the item ids Defaults to item_id

• train_on_last_item_seq_only (Optional[bool]) – predict only the last item during training. Defaults to True.

__init__(padding_idx: int = 0, eval_on_last_item_seq_only: bool = True, train_on_last_item_seq_only: bool = True, **kwargs)

Methods

__init__([padding_idx, …])
add_features_to_context(feature_shapes)
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, …])	Adds a new variable to the layer.
apply_mask_to_inputs(inputs, mask_schema)
as_tabular([name])
build(input_shapes)
call(inputs[, training])
call_outputs(outputs[, training])
check_schema([schema])
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_mask_schema(items[, training])
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
connect(*block[, block_name, context])	Connect the block to other blocks sequentially.
connect_branch(*branches[, add_rest, post, …])	Connect the block to one or multiple branches.
connect_debug_block([append])	Connect the block to a debug block.
connect_with_residual(block[, activation])	Connect the block to other blocks sequentially with a residual connection.
connect_with_shortcut(block[, …])	Connect the block to other blocks sequentially with a shortcut connection.
copy()
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Creates a layer from its config.
from_layer(layer)
get_config()
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_item_ids_from_inputs(inputs)
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_padding_mask_from_item_id(inputs[, …])
get_weights()	Returns the current weights of the layer, as NumPy arrays.
parse(*block)
parse_block(input)
prepare([block, post, aggregation])	Transform the inputs of this block.
register_features(feature_shapes)
repeat([num])	Repeat the block num times.
repeat_in_parallel([num, prefix, names, …])	Repeat the block num times in parallel.
select_by_name(name)
set_schema([schema])
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
to_model(schema[, input_block, prediction_tasks])	Wrap the block between inputs & outputs to create a model.
with_name_scope(method)	Decorator to automatically enter the module name scope.

Attributes

REQUIRES_SCHEMA
activity_regularizer	Optional regularizer function for the output of this layer.
compute_dtype	The dtype of the layer’s computations.
context
dtype	The dtype of the layer weights.
dtype_policy	The dtype policy associated with this layer.
dynamic	Whether the layer is dynamic (eager-only); set in the constructor.
has_schema
inbound_nodes	Return Functional API nodes upstream of this layer.
input	Retrieves the input tensor(s) of a layer.
input_mask	Retrieves the input mask tensor(s) of a layer.
input_shape	Retrieves the input shape(s) of a layer.
input_spec	InputSpec instance(s) describing the input format for this layer.
losses	List of losses added using the add_loss() API.
metrics	List of metrics added using the add_metric() API.
name	Name of the layer (string), set in the constructor.
name_scope	Returns a tf.name_scope instance for this class.
non_trainable_variables
non_trainable_weights	List of all non-trainable weights tracked by this layer.
outbound_nodes	Return Functional API nodes downstream of this layer.
output	Retrieves the output tensor(s) of a layer.
output_mask	Retrieves the output mask tensor(s) of a layer.
output_shape	Retrieves the output shape(s) of a layer.
registry
schema
stateful
submodules	Sequence of all sub-modules.
supports_masking	Whether this layer supports computing a mask using compute_mask.
trainable
trainable_variables
trainable_weights	List of all trainable weights tracked by this layer.
updates
variable_dtype	Alias of Layer.dtype, the dtype of the weights.
variables	Returns the list of all layer variables/weights.
weights	Returns the list of all layer variables/weights.

compute_mask_schema(items: tensorflow.python.framework.ops.Tensor, training: bool = False) → tensorflow.python.framework.ops.Tensor

apply_mask_to_inputs(inputs: tensorflow.python.framework.ops.Tensor, mask_schema: tensorflow.python.framework.ops.Tensor) → tensorflow.python.framework.ops.Tensor