merlin.models.tf.SequencePredictNext

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

Bases: merlin.models.tf.transforms.sequence.SequenceTransform

Prepares sequential inputs and targets for next-item prediction. The target is extracted from the shifted sequence of item ids and the sequential input features are truncated in the last position.

Parameters

• schema (Schema) – The schema with the sequential columns to be truncated

• target (Union[str, Tags, ColumnSchema]) – The sequential input column that will be used to extract the target

• pre (Optional[BlockType], optional) – A block that is called before this method call(). If not set, the ListToRagged() block is applied to convert the tuple representation of sequential features to RaggedTensors, so that the tensors sequences can be shifted/truncated

__init__(schema: merlin.schema.schema.Schema, target: Union[str, merlin.schema.tags.Tags, merlin.schema.schema.ColumnSchema], pre: Optional[Union[merlin.models.tf.core.base.Block, str, Sequence[str]]] = None, **kwargs)

Methods

__init__(schema, target[, pre])
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_to_all(inputs[, columns_to_filter])
as_tabular([name])
build(input_shapes)
build_from_config(config)
calculate_batch_size_from_input_shapes(...)
call(inputs[, targets, training, testing])
call_outputs(outputs[, training])
check_schema([schema])
compute_call_output_shape(input_shapes)
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 layer from its config.
from_features(features[, pre, post, ...])	Initializes a TabularLayer instance where the contents of features will be filtered out
from_layer(layer)
from_schema(schema[, tags, allow_none])	Instantiate a TabularLayer instance from a DatasetSchema.
get_build_config()
get_config()	Returns the config of the layer as a Python dictionary.
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)
post_call(inputs[, transformations, ...])	Method that's typically called after the forward method for post-processing.
pre_call(inputs[, transformations])	Method that's typically called before the forward method for pre-processing.
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.
repr_add()
repr_extra()
repr_ignore()
select_by_name(name)
select_by_tag(tags)
set_aggregation(value)	param value
set_post(value)
set_pre(value)
set_schema([schema])
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
super()
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.
aggregation	rtype: TabularAggregation, optional
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.
is_input
is_tabular
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.
post	rtype: SequentialTabularTransformations, optional
pre	rtype: SequentialTabularTransformations, optional
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.

call(inputs: Dict[str, tensorflow.python.framework.ops.Tensor], targets=None, training=False, testing=False, **kwargs) → Tuple

compute_output_shape(input_shape)