merlin.models.tf.EmbeddingEncoder#
- class merlin.models.tf.EmbeddingEncoder(*args, **kwargs)[source]#
Bases:
merlin.models.tf.core.encoder.Encoder
- __init__(schema: Union[merlin.schema.schema.ColumnSchema, merlin.schema.schema.Schema], dim: int, embeddings_initializer='uniform', embeddings_regularizer=None, activity_regularizer=None, embeddings_constraint=None, mask_zero=False, input_length=None, sequence_combiner: Optional[Union[str, keras.engine.base_layer.Layer]] = None, trainable=True, name=None, dtype=None, dynamic=False, post: Optional[keras.engine.base_layer.Layer] = None, embeddings_l2_batch_regularization: Optional[Union[float, Dict[str, float]]] = 0.0)[source]#
Methods
__init__
(schema, dim[, ...])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)build_from_config
(config)call
(inputs[, training, testing, targets])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)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)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)Fit 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])get_build_config
()get_compile_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_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_dataset
([gpu])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)Train 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
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.
jit_compile
Specify whether to compile the model with XLA.
last
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
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
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.
- to_dataset(gpu=None) merlin.io.dataset.Dataset [source]#