merlin.models.tf.CausalLanguageModeling

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

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)[source]

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, …)

as_tabular([name])

build(input_shapes)

call(inputs[, feature_context, training])

call_outputs(outputs[, training])

check_schema([schema])

compute_feature_mask(item_ids[, training])

compute_mask(inputs[, mask])

Computes an output mask tensor.

compute_output_shape(input_shape)

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.

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_feature_mask(item_ids: tensorflow.python.framework.ops.Tensor, training: bool = False)tensorflow.python.framework.ops.Tensor[source]
apply_mask_to_inputs(inputs: tensorflow.python.framework.ops.Tensor, input_mask_to_replace: tensorflow.python.framework.ops.Tensor)tensorflow.python.framework.ops.Tensor[source]