merlin.models.tf.SequentialBlock

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

Bases: merlin.models.tf.blocks.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.blocks.core.tabular.Filter]] = None, pre_aggregation: Optional[Union[str, merlin.models.tf.blocks.core.tabular.TabularAggregation]] = None, block_name: Optional[str] = None, copy_layers: bool = False, **kwargs)

Create a 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 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])
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)
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_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.
filter_features
first
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
is_tabular
last
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
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
trainable_variables
trainable_weights
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)

compute_output_signature(input_signature)

build(input_shape=None)

set_schema(schema=None)

property inputs

property first

property last

property filter_features

property trainable_weights

property non_trainable_weights

property trainable

property losses

property regularizers

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

compute_loss(inputs, targets, **kwargs)

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

get_config()

property is_tabular

classmethod from_config(config, custom_objects=None)