merlin.models.tf.Encoder#

class merlin.models.tf.Encoder(*args, **kwargs)[source]#

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)[source]#

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: Union[merlin.io.dataset.Dataset, merlin.models.tf.loader.Loader], index: Union[str, merlin.schema.schema.ColumnSchema, merlin.schema.schema.Schema, merlin.schema.tags.Tags], batch_size: int, **kwargs) → merlin.io.dataset.Dataset[source]#

Encodes the given dataset and index.

Parameters

dataset (Union[merlin.io.Dataset, merlin.models.tf.loader.Loader]) – 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: Union[merlin.io.dataset.Dataset, merlin.models.tf.loader.Loader], 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[source]#

Batched prediction using Dask.

Parameters

dataset (Union[merlin.io.Dataset, merlin.models.tf.loader.Loader]) – Dataset or Loader 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)[source]#

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)[source]#

Creates the variables of the layer.

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

compute_output_shape(input_shape)[source]#

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)[source]#

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)[source]#

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[source]#

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)[source]#

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()[source]#

Returns the configuration of the model as a dictionary.

Returns: The configuration of the model.
Return type: dict