merlin.models.tf.TabularBlock

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

Bases: merlin.models.tf.blocks.core.base.Block

Layer that’s specialized for tabular-data by integrating many often used operations.

Note, when extending this class, typically you want to overwrite the compute_call_output_shape method instead of the normal compute_output_shape. This because a Block can contain pre- and post-processing and the output-shapes are handled automatically in compute_output_shape. The output of compute_call_output_shape should be the shape that’s outputted by the call-method.

Parameters

• pre (Union[str, TabularTransformation, List[str], List[TabularTransformation]], optional) – Transformations to apply on the inputs when the module is called (so before call).

• post (Union[str, TabularTransformation, List[str], List[TabularTransformation]], optional) – Transformations to apply on the inputs after the module is called (so after call).

• aggregation (Union[str, TabularAggregation], optional) –

  Aggregation to apply after processing the call-method to output a single Tensor.

  Next to providing a class that extends TabularAggregation, it’s also possible to provide the name that the class is registered in the tabular_aggregation_registry. Out of the box this contains: “concat”, “stack”, “element-wise-sum” & “element-wise-sum-item-multi”.

• schema (Optional[DatasetSchema]) – DatasetSchema containing the columns used in this block.

• name (Optional[str]) – Name of the layer.

__init__(pre: Optional[Union[merlin.models.tf.blocks.core.base.Block, str, Sequence[str]]] = None, post: Optional[Union[merlin.models.tf.blocks.core.base.Block, str, Sequence[str]]] = None, aggregation: Optional[Union[str, merlin.models.tf.blocks.core.tabular.TabularAggregation]] = None, schema: Optional[merlin.schema.schema.Schema] = None, name: Optional[str] = None, is_input: bool = False, **kwargs)

Methods

__init__([pre, post, aggregation, schema, …])
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)
calculate_batch_size_from_input_shapes(…)
call(inputs, **kwargs)
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_shapes)
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)
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_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)
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)
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	returns: :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	returns: :rtype: SequentialTabularTransformations, optional
pre	returns: :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.

property is_input

classmethod from_schema(schema: merlin.schema.schema.Schema, tags=None, allow_none=True, **kwargs) → Optional[merlin.models.tf.blocks.core.tabular.TabularBlock]

Instantiate a TabularLayer instance from a DatasetSchema.

Parameters

• schema –

• tags –

• kwargs –

Returns

Return type

Optional[TabularModule]

classmethod from_features(features: List[str], pre: Optional[Union[merlin.models.tf.blocks.core.base.Block, str, Sequence[str]]] = None, post: Optional[Union[merlin.models.tf.blocks.core.base.Block, str, Sequence[str]]] = None, aggregation: Optional[Union[str, merlin.models.tf.blocks.core.tabular.TabularAggregation]] = None, name=None, **kwargs) → merlin.models.tf.blocks.core.tabular.TabularBlock

Initializes a TabularLayer instance where the contents of features will be filtered out

Parameters

• features (List[str]) – A list of feature-names that will be used as the first pre-processing op to filter out all other features not in this list.

• pre (Union[str, TabularTransformation, List[str], List[TabularTransformation]], optional) – Transformations to apply on the inputs when the module is called (so before call).

• post (Union[str, TabularTransformation, List[str], List[TabularTransformation]], optional) – Transformations to apply on the inputs after the module is called (so after call).

• aggregation (Union[str, TabularAggregation], optional) –

  Aggregation to apply after processing the call-method to output a single Tensor.

  Next to providing a class that extends TabularAggregation, it’s also possible to provide the name that the class is registered in the tabular_aggregation_registry. Out of the box this contains: “concat”, “stack”, “element-wise-sum” & “element-wise-sum-item-multi”.

• schema (Optional[DatasetSchema]) – DatasetSchema containing the columns used in this block.

• name (Optional[str]) – Name of the layer.

Returns

Return type

TabularModule

pre_call(inputs: Dict[str, tensorflow.python.framework.ops.Tensor], transformations: Optional[Union[merlin.models.tf.blocks.core.base.Block, str, Sequence[str]]] = None) → Dict[str, tensorflow.python.framework.ops.Tensor]

Method that’s typically called before the forward method for pre-processing.

Parameters

• inputs (TabularData) – input-data, typically the output of the forward method.

• transformations (TabularTransformationsType, optional) –

Returns

Return type

TabularData

call(inputs: Dict[str, tensorflow.python.framework.ops.Tensor], **kwargs) → Dict[str, tensorflow.python.framework.ops.Tensor]

post_call(inputs: Dict[str, tensorflow.python.framework.ops.Tensor], transformations: Optional[Union[merlin.models.tf.blocks.core.base.Block, str, Sequence[str]]] = None, merge_with: Optional[Union[merlin.models.tf.blocks.core.tabular.TabularBlock, List[merlin.models.tf.blocks.core.tabular.TabularBlock]]] = None, aggregation: Optional[Union[str, merlin.models.tf.blocks.core.tabular.TabularAggregation]] = None) → Union[tensorflow.python.framework.ops.Tensor, Dict[str, tensorflow.python.framework.ops.Tensor]]

Method that’s typically called after the forward method for post-processing.

Parameters

• inputs (TabularData) – input-data, typically the output of the forward method.

• transformations (TabularTransformationType, optional) – Transformations to apply on the input data.

• merge_with (Union[TabularModule, List[TabularModule]], optional) – Other TabularModule’s to call and merge the outputs with.

• aggregation (TabularAggregationType, optional) – Aggregation to aggregate the output to a single Tensor.

Returns

Return type

TensorOrTabularData (Tensor when aggregation is set, else TabularData)

compute_call_output_shape(input_shapes)

compute_output_shape(input_shapes)

build(input_shapes)

get_config()

property is_tabular

classmethod from_config(config)

apply_to_all(inputs, columns_to_filter=None)

set_schema(schema=None)

set_pre(value: Optional[Union[merlin.models.tf.blocks.core.base.Block, str, Sequence[str]]])

property pre

returns: :rtype: SequentialTabularTransformations, optional

property post

returns: :rtype: SequentialTabularTransformations, optional

set_post(value: Optional[Union[merlin.models.tf.blocks.core.base.Block, str, Sequence[str]]])

property aggregation

returns: :rtype: TabularAggregation, optional

set_aggregation(value: Optional[Union[str, merlin.models.tf.blocks.core.tabular.TabularAggregation]])

Parameters

value –

repr_ignore()

repr_extra()

repr_add()

static calculate_batch_size_from_input_shapes(input_shapes)

super()