tfra.dynamic_embedding.keras.layers.Embedding

Class `Embedding`

A keras style Embedding layer. The Embedding layer acts same like

View aliases

Main aliases

`tfra.dynamic_embedding.keras.layers.embedding.Embedding`

tf.keras.layers.Embedding, except that the Embedding has dynamic embedding space so it does not need to set a static vocabulary size, and there will be no hash conflicts between features.

The embedding layer allow arbirary input shape of feature ids, and get (shape(ids) + embedding_size) lookup result. Normally the first dimension is batch_size.

Example

embedding = dynamic_embedding.keras.layers.Embedding(8) # embedding size 8
ids = tf.constant([[15,2], [4,92], [22,4]], dtype=tf.int64) # (3, 2)
out = embedding(ids) # lookup result, (3, 2, 8)

You could inherit the Embedding class to implement a custom embedding layer with other fixed shape output.

TODO(Lifann) Currently the Embedding only implemented in eager mode API, need to support graph mode also.

`init`

View source

__init__(
    embedding_size,
    key_dtype=tf.int64,
    value_dtype=tf.float32,
    combiner='sum',
    initializer=None,
    devices=None,
    name='DynamicEmbeddingLayer',
    with_unique=True,
    **kwargs
)

Creates a Embedding layer.

Args:

embedding_size: An object convertible to int. Length of embedding vector to every feature id.
key_dtype: Dtype of the embedding keys to weights. Default is int64.
value_dtype: Dtype of the embedding weight values. Default is float32
combiner: A string or a function to combine the lookup result. It's value could be 'sum', 'mean', 'min', 'max', 'prod', 'std', etc. whose are one of tf.math.reduce_xxx.
initializer: Initializer to the embedding values. Default is RandomNormal.
devices: List of devices to place the embedding layer parameter.
name: Name of the embedding layer.
with_unique: : Bool. Whether if the layer does unique on ids. Default is True.
**kwargs: trainable: Bool. Whether if the layer is trainable. Default is True. bp_v2: Bool. If true, the embedding layer will be updated by incremental amount. Otherwise, it will be updated by value directly. Default is False. restrict_policy: A RestrictPolicy class to restrict the size of embedding layer parameter since the dynamic embedding supports nearly infinite embedding space capacity. init_capacity: Integer. Initial number of kv-pairs in an embedding layer. The capacity will growth if the used space exceeded current capacity. partitioner: A function to route the keys to specific devices for distributed embedding parameter. kv_creator: A KVCreator object to create external KV storage as embedding parameter. max_norm: If not None, each values is clipped if its l2-norm is larger distribute_strategy: Used when creating ShadowVariable. keep_distribution: Bool. If true, save and restore python object with devices information. Default is false.

Properties

`activity_regularizer`

Optional regularizer function for the output of this layer.

`compute_dtype`

The dtype of the layer's computations.

This is equivalent to Layer.dtype_policy.compute_dtype. Unless mixed precision is used, this is the same as Layer.dtype, the dtype of the weights.

Layers automatically cast their inputs to the compute dtype, which causes computations and the output to be in the compute dtype as well. This is done by the base Layer class in Layer.__call__, so you do not have to insert these casts if implementing your own layer.

Layers often perform certain internal computations in higher precision when compute_dtype is float16 or bfloat16 for numeric stability. The output will still typically be float16 or bfloat16 in such cases.

Returns:

The layer's compute dtype.

`dtype`

The dtype of the layer weights.

This is equivalent to Layer.dtype_policy.variable_dtype. Unless mixed precision is used, this is the same as Layer.compute_dtype, the dtype of the layer's computations.

`dtype_policy`

The dtype policy associated with this layer.

This is an instance of a tf.keras.mixed_precision.Policy.

`dynamic`

Whether the layer is dynamic (eager-only); set in the constructor.

`input`

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns:

Input tensor or list of input tensors.

Raises:

RuntimeError: If called in Eager mode.
AttributeError: If no inbound nodes are found.

`input_spec`

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

self.input_spec = tf.keras.layers.InputSpec(ndim=4)

Now, if you try to call the layer on an input that isn't rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

ValueError: Input 0 of layer conv2d is incompatible with the layer:
expected ndim=4, found ndim=1. Full shape received: [2]

Input checks that can be specified via input_spec include:

Structure (e.g. a single input, a list of 2 inputs, etc)
Shape
Rank (ndim)
Dtype

For more information, see tf.keras.layers.InputSpec.

Returns:

A tf.keras.layers.InputSpec instance, or nested structure thereof.

`losses`

List of losses added using the add_loss() API.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Examples:

>>> class MyLayer(tf.keras.layers.Layer):
...   def call(self, inputs):
...     self.add_loss(tf.abs(tf.reduce_mean(inputs)))
...     return inputs
>>> l = MyLayer()
>>> l(np.ones((10, 1)))
>>> l.losses
[1.0]

>>> inputs = tf.keras.Input(shape=(10,))
>>> x = tf.keras.layers.Dense(10)(inputs)
>>> outputs = tf.keras.layers.Dense(1)(x)
>>> model = tf.keras.Model(inputs, outputs)
>>> # Activity regularization.
>>> len(model.losses)
0
>>> model.add_loss(tf.abs(tf.reduce_mean(x)))
>>> len(model.losses)
1

>>> inputs = tf.keras.Input(shape=(10,))
>>> d = tf.keras.layers.Dense(10, kernel_initializer='ones')
>>> x = d(inputs)
>>> outputs = tf.keras.layers.Dense(1)(x)
>>> model = tf.keras.Model(inputs, outputs)
>>> # Weight regularization.
>>> model.add_loss(lambda: tf.reduce_mean(d.kernel))
>>> model.losses
[<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]

Returns:

A list of tensors.

`metrics`

List of metrics added using the add_metric() API.

Example:

>>> input = tf.keras.layers.Input(shape=(3,))
>>> d = tf.keras.layers.Dense(2)
>>> output = d(input)
>>> d.add_metric(tf.reduce_max(output), name='max')
>>> d.add_metric(tf.reduce_min(output), name='min')
>>> [m.name for m in d.metrics]
['max', 'min']

Returns:

A list of Metric objects.

`name`

Name of the layer (string), set in the constructor.

`name_scope`

Returns a tf.name_scope instance for this class.

`non_trainable_weights`

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns:

A list of non-trainable variables.

`output`

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns:

Output tensor or list of output tensors.

Raises:

AttributeError: if the layer is connected to more than one incoming layers.
RuntimeError: if called in Eager mode.

`submodules`

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True

Returns:

A sequence of all submodules.

`supports_masking`

Whether this layer supports computing a mask using compute_mask.

`trainable`

`trainable_weights`

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns:

A list of trainable variables.

`variable_dtype`

Alias of Layer.dtype, the dtype of the weights.

`weights`

Returns the list of all layer variables/weights.

Returns:

A list of variables.

Methods

`call`

__call__(
    *args,
    **kwargs
)

Wraps call, applying pre- and post-processing steps.

Args:

*args: Positional arguments to be passed to self.call.
**kwargs: Keyword arguments to be passed to self.call.

Returns:

Output tensor(s).

Note:

The following optional keyword arguments are reserved for specific uses:
- training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference.
- mask: Boolean input mask.
If the layer's call method takes a mask argument (as some Keras layers do), its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support.
If the layer is not built, the method will call build.

Raises:

ValueError: if the layer's call method returns None (an invalid value).
RuntimeError: if super().__init__() was not called in the constructor.

`add_loss`

add_loss(
    losses,
    **kwargs
)

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model's call function, in which case losses should be a Tensor or list of Tensors.

Example:

class MyLayer(tf.keras.layers.Layer):
  def call(self, inputs):
    self.add_loss(tf.abs(tf.reduce_mean(inputs)))
    return inputs

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model's Inputs. These losses become part of the model's topology and are tracked in get_config.

Example:

inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Activity regularization.
model.add_loss(tf.abs(tf.reduce_mean(x)))

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model's layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model's topology since they can't be serialized.

Example:

inputs = tf.keras.Input(shape=(10,))
d = tf.keras.layers.Dense(10)
x = d(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Weight regularization.
model.add_loss(lambda: tf.reduce_mean(d.kernel))

Args:

losses: Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor.
**kwargs: Additional keyword arguments for backward compatibility. Accepted values: inputs - Deprecated, will be automatically inferred.

`add_metric`

add_metric(
    value,
    name=None,
    **kwargs
)

Adds metric tensor to the layer.

This method can be used inside the call() method of a subclassed layer or model.

class MyMetricLayer(tf.keras.layers.Layer):
  def __init__(self):
    super(MyMetricLayer, self).__init__(name='my_metric_layer')
    self.mean = tf.keras.metrics.Mean(name='metric_1')

  def call(self, inputs):
    self.add_metric(self.mean(inputs))
    self.add_metric(tf.reduce_sum(inputs), name='metric_2')
    return inputs

This method can also be called directly on a Functional Model during construction. In this case, any tensor passed to this Model must be symbolic and be able to be traced back to the model's Inputs. These metrics become part of the model's topology and are tracked when you save the model via save().

inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
model.add_metric(math_ops.reduce_sum(x), name='metric_1')

Note: Calling add_metric() with the result of a metric object on a Functional Model, as shown in the example below, is not supported. This is because we cannot trace the metric result tensor back to the model's inputs.

inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
model.add_metric(tf.keras.metrics.Mean()(x), name='metric_1')

Args:

value: Metric tensor.
name: String metric name.
**kwargs: Additional keyword arguments for backward compatibility. Accepted values: aggregation - When the value tensor provided is not the result of calling a keras.Metric instance, it will be aggregated by default using a keras.Metric.Mean.

`build`

build(input_shape)

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Args:

input_shape: Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).

`compute_mask`

compute_mask(
    inputs,
    mask=None
)

Computes an output mask tensor.

Args:

inputs: Tensor or list of tensors.
mask: Tensor or list of tensors.

Returns:

None or a tensor (or list of tensors, one per output tensor of the layer).

`compute_output_shape`

compute_output_shape(input_shape)

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Args:

input_shape: Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.

Returns:

An input shape tuple.

`count_params`

count_params()

Count the total number of scalars composing the weights.

Returns:

An integer count.

Raises:

ValueError: if the layer isn't yet built (in which case its weights aren't yet defined).

`from_config`

@classmethod
from_config(
    cls,
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args:

config: A Python dictionary, typically the output of get_config.

Returns:

A layer instance.

`get_config`

View source

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Note that get_config() does not guarantee to return a fresh copy of dict every time it is called. The callers should make a copy of the returned dict if they want to modify it.

Returns:

Python dictionary.

`get_weights`

get_weights()

Returns the current weights of the layer, as NumPy arrays.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of NumPy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values: the kernel matrix and the bias vector. These can be used to set the weights of another Dense layer:

>>> layer_a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = layer_a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> layer_a.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]
>>> layer_b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = layer_b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> layer_b.get_weights()
[array([[2.],
       [2.],
       [2.]], dtype=float32), array([0.], dtype=float32)]
>>> layer_b.set_weights(layer_a.get_weights())
>>> layer_b.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]

Returns:

Weights values as a list of NumPy arrays.

`set_weights`

set_weights(weights)

Sets the weights of the layer, from NumPy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer's weights must be instantiated before calling this function, by calling the layer.

For example, a Dense layer returns a list of two values: the kernel matrix and the bias vector. These can be used to set the weights of another Dense layer:

>>> layer_a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = layer_a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> layer_a.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]
>>> layer_b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = layer_b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> layer_b.get_weights()
[array([[2.],
       [2.],
       [2.]], dtype=float32), array([0.], dtype=float32)]
>>> layer_b.set_weights(layer_a.get_weights())
>>> layer_b.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]

Args:

weights: a list of NumPy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights).

Raises:

ValueError: If the provided weights list does not match the layer's specifications.

`with_name_scope`

@classmethod
with_name_scope(
    cls,
    method
)

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)

Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Args:

method: The method to wrap.

Returns:

The original method wrapped such that it enters the module's name scope.

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tfra.dynamic_embedding.keras.layers.Embedding

Class Embedding

View aliases

Example

__init__

Args:

Properties

activity_regularizer

compute_dtype

Returns:

dtype

dtype_policy

dynamic

input

Returns:

Raises:

input_spec

Returns:

losses

Examples:

Returns:

metrics

Example:

Returns:

name

name_scope

non_trainable_weights

Returns:

output

Returns:

Raises:

submodules

Returns:

supports_masking

trainable

trainable_weights

Returns:

variable_dtype

weights

Returns:

Methods

__call__

Args:

Returns:

Note:

Raises:

add_loss

Example:

Example:

Example:

Args:

add_metric

Args:

build

Args:

compute_mask

Args:

Returns:

compute_output_shape

Args:

Returns:

count_params

Returns:

Raises:

from_config

Args:

Returns:

get_config

Returns:

get_weights

Returns:

set_weights

Args:

Class `Embedding`

`init`

`activity_regularizer`

`compute_dtype`

`dtype`

`dtype_policy`

`dynamic`

`input`

`input_spec`

`losses`

`metrics`

`name`

`name_scope`

`non_trainable_weights`

`output`

`submodules`

`supports_masking`

`trainable`

`trainable_weights`

`variable_dtype`

`weights`

`call`

`add_loss`

`add_metric`

`build`

`compute_mask`

`compute_output_shape`

`count_params`

`from_config`

`get_config`

`get_weights`

`set_weights`

`with_name_scope`