merlin.models.tf.SequentialBlock

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

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

The SequentialLayer represents a sequence of Keras layers. It is a Keras Layer that can be used instead of tf.keras.layers.Sequential, which is actually a Keras Model. In contrast to keras Sequential, this layer can be used as a pure Layer in tf.functions and when exporting SavedModels, without having to pre-declare input and output shapes. In turn, this layer is usable as a preprocessing layer for TF Agents Networks, and can be exported via PolicySaver. Usage:

c = SequentialLayer([layer1, layer2, layer3])
output = c(inputs)    # Equivalent to: output = layer3(layer2(layer1(inputs)))

__init__(*layers, filter: Optional[Union[merlin.schema.schema.Schema, merlin.schema.tags.Tags, List[str], merlin.models.tf.core.tabular.Filter]] = None, pre_aggregation: Optional[Union[str, merlin.models.tf.core.tabular.TabularAggregation]] = None, block_name: Optional[str] = None, copy_layers: bool = False, **kwargs)

Create a sequential composition.

Parameters

• layers – A list or tuple of layers to compose.

• **kwargs – Arguments to pass to Keras layer initializer, including name.

Raises

TypeError: – If any of the layers are not instances of keras Layer.

Methods

__init__(*layers[, filter, pre_aggregation, …])	Create a sequential composition.
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.
as_tabular([name])
build([input_shape])	Builds the sequential block
build_from_config(config)
call(inputs[, training])
call_outputs(outputs[, training])
check_schema([schema])
compute_loss(inputs, targets, **kwargs)
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape based on the input shape
compute_output_signature(input_signature)
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[, custom_objects])
from_layer(layer)
get_build_config()
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)
select_by_tag(tags)
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.
filter_features
first	Returns the first block in the SequenceBlock
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.
inputs	Returns the InputBlock, if it is the first block within SequenceBlock
is_tabular
last	Returns the last block in the SequenceBlock
losses
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	Returns non-trainable weights of all layers of this block
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
regularizers
schema
stateful
submodules	Sequence of all sub-modules.
supports_masking	Whether this layer supports computing a mask using compute_mask.
trainable	Returns whether all layer within SequentialBlock are trainable
trainable_variables
trainable_weights	Returns trainable weights of all layers of this block
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_output_shape(input_shape)

Computes the output shape based on the input shape

Parameters

input_shape (tf.TensorShape) – The input shape

Returns

The output shape

Return type

tf.TensorShape

compute_output_signature(input_signature)

build(input_shape=None)

Builds the sequential block

Parameters

input_shape (tf.TensorShape, optional) – The input shape, by default None

set_schema(schema=None)

property inputs

Returns the InputBlock, if it is the first block within SequenceBlock

Returns

The input block

Return type

InputBlock

property first

Returns the first block in the SequenceBlock

Returns

The first block of SequenceBlock

Return type

Block

property last

Returns the last block in the SequenceBlock

Returns

The last block of SequenceBlock

Return type

Block

property filter_features

property trainable_weights

Returns trainable weights of all layers of this block

Returns

List with trainable weights

Return type

List

property non_trainable_weights

Returns non-trainable weights of all layers of this block

Returns

List with non-trainable weights

Return type

List

property trainable

Returns whether all layer within SequentialBlock are trainable

Returns

True if any layer within SequentialBlock are trainable, otherwise False

Return type

bool

property losses

property regularizers

call(inputs, training=False, **kwargs)

compute_loss(inputs, targets, **kwargs)

call_outputs(outputs: merlin.models.tf.core.base.PredictionOutput, training=False, **kwargs) → merlin.models.tf.core.base.PredictionOutput

get_config()

property is_tabular

classmethod from_config(config, custom_objects=None)