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Wraps a SavedModel (or a legacy TF1 Hub format) as a Keras Layer.
hub.KerasLayer(
handle,
trainable=False,
arguments=None,
_sentinel=None,
tags=None,
signature=None,
signature_outputs_as_dict=None,
output_key=None,
output_shape=None,
load_options=None,
**kwargs
)
Used in the notebooks
Used in the guide  Used in the tutorials 

This layer wraps a callable object for use as a Keras layer. The callable
object can be passed directly, or be specified by a Python string with a
handle that gets passed to hub.load()
.
This is the preferred API to load a TF2style SavedModel from TF Hub into a Keras model. Calling this function requires TF 1.15 or newer. It can be called both in eager and graph mode.
The callable object is expected to follow the conventions detailed below. (These are met by TF2compatible modules loaded from TensorFlow Hub.)
The callable is invoked with a single positional argument set to one tensor
or a nest of tensors containing the inputs to the layer. If the callable
accepts a training
argument, a Python boolean is passed for it. It is True
if this layer is marked trainable and called for training, analogous to
tf.keras.layers.BatchNormalization. (By contrast, tf.keras.layers.Dropout
ignores the trainable state and applies the training argument verbatim.)
If present, the following attributes of callable are understood to have
special meanings:
variables: a list of all tf.Variable objects that the callable depends on.
trainable_variables: those elements of variables
that are reported
as trainable variables of this Keras Layer when the layer is trainable.
regularization_losses: a list of callables to be added as losses of this
Keras Layer when the layer is trainable. Each one must accept zero
arguments and return a scalar tensor.
hub.KerasLayer(
"/tmp/text_embedding_model",
output_shape=[20], # Outputs a tensor with shape [batch_size, 20].
input_shape=[], # Expects a tensor of shape [batch_size] as input.
dtype=tf.string) # Expects a tf.string input tensor.
Attributes  

handle

A callable object (subject to the conventions above), or a Python string to load a saved model via hub.load(). A string is required to save the Keras config of this Layer. 
trainable

Optional. A boolean controlling whether this layer is trainable. Must not be set to True when using a signature (raises ValueError), including the use of legacy TF1 Hub format. 
arguments

Optional. A dict with additional keyword arguments passed to the callable. These must be JSONserializable to save the Keras config of this layer, and are not tracked as checkpointing dependencies of this layer. 
_sentinel

Used to prevent further positional arguments. 
tags

Optional. If set indicates which graph variant to use. For legacy models in TF1 Hub format leaving unset means to use the empty tags set. 
signature

Optional. If set, KerasLayer will use the requested signature.
For legacy models in TF1 Hub format leaving unset means to use the
default signature. When using a signature, either
signature_outputs_as_dict or output_key have to set.

signature_outputs_as_dict

If set to True, the call to this layer returns a dict of all the signature outputs. Can only be used if a signature is specified (or default signature is used for legacy models in TF1 Hub format). 
output_key

Name of the output item to return if the layer returns a dict.
For legacy models in TF1 Hub format leaving unset means to return the
default output.

output_shape

A tuple or a nest of tuples with the (possibly partial) output shapes of the callable without leading batch size. This must have the same nesting structure as the output of the callable object and cover all output tensors. 
load_options

Optional, tf.saved_model.LoadOptions object that specifies
options for loading when a Python string is provided as handle . This
argument can only be used from TensorFlow 2.3 onwards.

**kwargs

Forwarded to Keras' base Layer constructor. 
activity_regularizer

Optional regularizer function for the output of this layer. 
compute_dtype

The dtype of the layer's computations.
This is equivalent to 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 Layers often perform certain internal computations in higher precision
when 
dtype

The dtype of the layer weights.
This is equivalent to 
dtype_policy

The dtype policy associated with this layer.
This is an instance of a 
dynamic

Whether the layer is dynamic (eageronly); 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. 
input_spec

InputSpec instance(s) describing the input format for this layer.
When you create a layer subclass, you can set
Now, if you try to call the layer on an input that isn't rank 4
(for instance, an input of shape
Input checks that can be specified via
For more information, see 
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

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_weights

List of all nontrainable weights tracked by this layer.
Nontrainable weights are not updated during training. They are
expected to be updated manually in 
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. 
resolved_object

Returns the callable object to which handle resolved in __init__ .

submodules

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

supports_masking

Whether this layer supports computing a mask using compute_mask .

trainable_weights

List of all trainable weights tracked by this layer.
Trainable weights are updated via gradient descent during training. 
variable_dtype

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

weights

Returns the list of all layer variables/weights. 
Methods
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 Input
s. 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 zeroargument 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 zeroargument callables which create a loss tensor. 
**kwargs

Used for backwards compatibility only. 
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 Input
s. 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')
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 statecreation step inbetween
layer instantiation and layer call. It is invoked automatically before
the first execution of call()
.
This is typically used to create the weights of Layer
subclasses
(at the discretion of the subclass implementer).
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.
This relies on the output_shape
provided during initialization, if any,
else falls back to the default behavior from tf.keras.layers.Layer
.
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( 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
get_config()
Returns a serializable dict of keras layer configuration parameters.
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 nontrainable 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( 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.Variable
s and tf.Tensor
s 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. 
__call__
__call__(
*args, **kwargs
)
Wraps call
, applying pre and postprocessing 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  


Raises  

ValueError

if the layer's call method returns None (an invalid
value).

RuntimeError

if super().__init__() was not called in the
constructor.
