merlin.models.tf.Encoder

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

Bases: keras.engine.training.Model

Block that can be used for prediction and evaluation but not for training

Parameters

• inputs (Union[Schema, tf.keras.layers.Layer]) – The input block or schema. When a schema is provided, a default input block will be created.

• *blocks (tf.keras.layers.Layer) – The blocks to use for encoding.

• pre (Optional[tf.keras.layers.Layer]) – A block to use before the main blocks

• post (Optional[tf.keras.layers.Layer]) – A block to use after the main blocks

• prep_features (Optional[bool]) – Whether this block should prepare list and scalar features from the dataloader format. By default True.

__init__(inputs: Union[merlin.schema.schema.Schema, keras.engine.base_layer.Layer], *blocks: keras.engine.base_layer.Layer, pre: Optional[keras.engine.base_layer.Layer] = None, post: Optional[keras.engine.base_layer.Layer] = None, prep_features: Optional[bool] = True, **kwargs)

Methods

__init__(inputs, *blocks[, pre, post, ...])
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.
batch_predict(dataset, batch_size[, ...])	Batched prediction using Dask.
build(input_shape)	Creates the variables of the layer.
build_from_config(config)
call(inputs, *[, targets, training, testing])	Calls the model on new inputs and returns the outputs as tensors.
compile([optimizer, loss, metrics, ...])	Configures the model for training.
compile_from_config(config)
compute_loss([x, y, y_pred, sample_weight])	Compute the total loss, validate it, and return it.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_metrics(x, y, y_pred, sample_weight)	Update metric states and collect all metrics to be returned.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
encode(dataset, index, batch_size, **kwargs)	Encodes the given dataset and index.
evaluate([x, y, batch_size, verbose, ...])	Returns the loss value & metrics values for the model in test mode.
evaluate_generator(generator[, steps, ...])	Evaluates the model on a data generator.
export(filepath)	Create a SavedModel artifact for inference (e.g.
finalize_state()	Finalizes the layers state after updating layer weights.
fit(*args, **kwargs)	Fits the model.
fit_generator(generator[, steps_per_epoch, ...])	Fits the model on data yielded batch-by-batch by a Python generator.
from_config(config[, custom_objects])	Creates a new instance of the class by deserializing.
get_build_config()
get_compile_config()
get_config()	Returns the configuration of the model as a 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_layer([name, index])	Retrieves a layer based on either its name (unique) or index.
get_metrics_result()	Returns the model's metrics values as a dict.
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_weight_paths()	Retrieve all the variables and their paths for the model.
get_weights()	Retrieves the weights of the model.
load_weights(filepath[, skip_mismatch, ...])	Loads all layer weights from a saved files.
make_predict_function([force])	Creates a function that executes one step of inference.
make_test_function([force])	Creates a function that executes one step of evaluation.
make_train_function([force])	Creates a function that executes one step of training.
predict(x[, batch_size, verbose, steps, ...])	Generates output predictions for the input samples.
predict_generator(generator[, steps, ...])	Generates predictions for the input samples from a data generator.
predict_on_batch(x)	Returns predictions for a single batch of samples.
predict_step(data)	The logic for one inference step.
reset_metrics()	Resets the state of all the metrics in the model.
reset_states()
save(export_path[, include_optimizer, ...])	Saves the model to export_path as a Tensorflow Saved Model.
save_spec([dynamic_batch])	Returns the tf.TensorSpec of call args as a tuple (args, kwargs).
save_weights(filepath[, overwrite, ...])	Saves all layer weights.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
summary([line_length, positions, print_fn, ...])	Prints a string summary of the network.
test_on_batch(x[, y, sample_weight, ...])	Test the model on a single batch of samples.
test_step(data)	The logic for one evaluation step.
to_json(**kwargs)	Returns a JSON string containing the network configuration.
to_yaml(**kwargs)	Returns a yaml string containing the network configuration.
train_on_batch(x[, y, sample_weight, ...])	Runs a single gradient update on a single batch of data.
train_step(data)	Performs a training step.
with_name_scope(method)	Decorator to automatically enter the module name scope.

Attributes

activity_regularizer	Optional regularizer function for the output of this layer.
compute_dtype	The dtype of the layer's computations.
distribute_reduction_method	The method employed to reduce per-replica values during training.
distribute_strategy	The tf.distribute.Strategy this model was created under.
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.
first	Returns the first block of the model.
has_schema	Returns True as this class does contain a 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.
jit_compile	Specify whether to compile the model with XLA.
last	Returns the last block of the model.
layers
losses	List of losses added using the add_loss() API.
metrics	Return metrics added using compile() or add_metric().
metrics_names	Returns the model's display labels for all outputs.
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.
run_eagerly	Settable attribute indicating whether the model should run eagerly.
schema	Returns the schema of the model.
state_updates	Deprecated, do NOT use!
stateful
submodules	Sequence of all sub-modules.
supports_masking	Whether this layer supports computing a mask using compute_mask.
to_call	Provides the list of blocks to be called during the execution of the model.
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.

encode(dataset: merlin.io.dataset.Dataset, index: Union[str, merlin.schema.schema.ColumnSchema, merlin.schema.schema.Schema, merlin.schema.tags.Tags], batch_size: int, **kwargs) → merlin.io.dataset.Dataset

Encodes the given dataset and index.

Parameters

• dataset (merlin.io.Dataset) – The dataset to encode.

• index (Union[str, ColumnSchema, Schema, Tags]) – The index to use for encoding.

• batch_size (int) – The batch size for encoding.

Returns

The encoded dataset.

Return type

merlin.io.Dataset

batch_predict(dataset: merlin.io.dataset.Dataset, batch_size: int, output_schema: Optional[merlin.schema.schema.Schema] = None, index: Optional[Union[str, merlin.schema.schema.ColumnSchema, merlin.schema.schema.Schema, merlin.schema.tags.Tags]] = None, **kwargs) → merlin.io.dataset.Dataset

Batched prediction using Dask.

Parameters

• dataset (merlin.io.Dataset) – Dataset to predict on.

• batch_size (int) – Batch size to use for prediction.

Return type

merlin.io.Dataset

call(inputs, *, targets=None, training=False, testing=False, **kwargs)

Calls the model on new inputs and returns the outputs as tensors.

Parameters

• inputs (tensor-like or dict/tuple of tensors.) – Tensors or dict/tuple of tensors representing the input batch.

• targets (tensor-like, optional) – Tensors representing the target data.

• training (bool, optional) – Whether the model is in training mode.

• testing (bool, optional) – Whether the model is in testing mode.

Return type

A tensor or dict of tensors corresponding to the result of calling the layer.

build(input_shape)

Creates the variables of the layer.

Parameters

input_shape (Tuple[int]) – The shape of the input data.

compute_output_shape(input_shape)

Computes the output shape of the layer.

Parameters

input_shape (Tuple[int]) – The shape of the input data.

Returns

The output shape of the layer.

Return type

Tuple[int]

train_step(data)

Performs a training step.

Train step method is not implemented and Raises an error as the Encoder block is not meant to be trained by itself and can only be trained as part of a model.

fit(*args, **kwargs)

Fits the model.

Fit method is not implemented and Raises an error as the Encoder block is not meant to be trained by itself and can only be trained as part of a model.

save(export_path: Union[str, os.PathLike], include_optimizer=True, save_traces=True) → None

Saves the model to export_path as a Tensorflow Saved Model. Along with merlin model metadata.

Parameters

• export_path (Union[str, os.PathLike]) – Path where model will be saved to

• include_optimizer (bool, optional) – If False, do not save the optimizer state, by default True

• save_traces (bool, optional) – When enabled, will store the function traces for each layer. This can be disabled, so that only the configs of each layer are stored, by default True

property to_call

Provides the list of blocks to be called during the execution of the model.

property has_schema: bool

Returns True as this class does contain a schema.

property schema: merlin.schema.schema.Schema

Returns the schema of the model.

property first

Returns the first block of the model.

property last

Returns the last block of the model.

classmethod from_config(config, custom_objects=None)

Creates a new instance of the class by deserializing.

Parameters

• config (dict) – A dictionary, typically the output of get_config.

• custom_objects (dict, optional) – A dictionary mapping the names of layers to the corresponding functions and classes.

Return type

A new instance of Encoder.

get_config()

Returns the configuration of the model as a dictionary.

Returns

The configuration of the model.

Return type

dict