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Class that wraps a given Keras
model to include graph regularization.
nsl.keras.GraphRegularization(
base_model, graph_reg_config=None
)
Used in the notebooks
Used in the tutorials 

Graph regularization is configured by an instance of
nsl.configs.GraphRegConfig
and the resulting loss is added as a
regularization term to the model's training objective. The graphregularized
model reuses the layers and variables from the base model. So, training this
model will also update the variables in the base model.
Example usage:
# Create a base model using the sequential, functional, or subclass API.
base_model = tf.keras.Sequential(...)
# Wrap the base model to include graph regularization using up to 1 neighbor
# per sample.
graph_config = nsl.configs.make_graph_reg_config(max_neighbors=1)
graph_model = nsl.keras.GraphRegularization(base_model, graph_config)
# Compile, train, and evaluate the graphregularized model as usual.
graph_model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
graph_model.fit(train_dataset, epochs=5)
graph_model.evaluate(test_dataset)
Args  

base_model

Unregularized model to which the loss term resulting from graph regularization will be added. 
graph_reg_config

Instance of nsl.configs.GraphRegConfig that contains
configuration for graph regularization. Use
nsl.configs.make_graph_reg_config to construct one.

Attributes  

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

distribute_strategy

The tf.distribute.Strategy this model was created under.

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 
layers


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

Returns the model's metrics added using compile() , add_metric() APIs.

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_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. 
run_eagerly

Settable attribute indicating whether the model should run eagerly.
Running eagerly means that your model will be run step by step, like Python code. Your model might run slower, but it should become easier for you to debug it by stepping into individual layer calls. By default, we will attempt to compile your model to a static graph to deliver the best execution performance. 
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


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
)
Builds the model based on input shapes received.
This is to be used for subclassed models, which do not know at instantiation time what their inputs look like.
This method only exists for users who want to call model.build()
in a
standalone way (as a substitute for calling the model on real data to
build it). It will never be called by the framework (and thus it will
never throw unexpected errors in an unrelated workflow).
Args  

input_shape

Single tuple, TensorShape instance, or list/dict of shapes,
where shapes are tuples, integers, or TensorShape instances.

Raises  

ValueError

In each of these cases, the user should build their model by calling it on real tensor data. 
call
call(
inputs, training=False, **kwargs
)
Incorporates graph regularization into the loss of base_model
.
Graph regularization is done on the logits layer and only during training.
Args  

inputs

Dictionary containing sample features, neighbor features, and
neighbor weights in the same format as described in
utils.unpack_neighbor_features .

training

Boolean tensor that indicates if we are in training mode. 
**kwargs

Additional keyword arguments to be passed to self.base_model .

Returns  

The output tensors for the wrapped graphregularized model. 
compile
compile(
*args, **kwargs
)
Configures the model for training.
Example:
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e3),
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=[tf.keras.metrics.BinaryAccuracy(),
tf.keras.metrics.FalseNegatives()])
Args  

optimizer

String (name of optimizer) or optimizer instance. See
tf.keras.optimizers .

loss

Loss function. May be a string (name of loss function), or
a tf.keras.losses.Loss instance. See tf.keras.losses . A loss
function is any callable with the signature loss = fn(y_true,
y_pred) , where y_true are the ground truth values, and
y_pred are the model's predictions.
y_true should have shape
(batch_size, d0, .. dN) (except in the case of
sparse loss functions such as
sparse categorical crossentropy which expects integer arrays of shape
(batch_size, d0, .. dN1) ).
y_pred should have shape (batch_size, d0, .. dN) .
The loss function should return a float tensor.
If a custom Loss instance is
used and reduction is set to None , return value has shape
(batch_size, d0, .. dN1) i.e. persample or pertimestep loss
values; otherwise, it is a scalar. If the model has multiple outputs,
you can use a different loss on each output by passing a dictionary
or a list of losses. The loss value that will be minimized by the
model will then be the sum of all individual losses, unless
loss_weights is specified.

metrics

List of metrics to be evaluated by the model during training
and testing. Each of this can be a string (name of a builtin
function), function or a tf.keras.metrics.Metric instance. See
tf.keras.metrics . Typically you will use metrics=['accuracy'] . A
function is any callable with the signature result = fn(y_true,
y_pred) . To specify different metrics for different outputs of a
multioutput model, you could also pass a dictionary, such as
metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']} .
You can also pass a list to specify a metric or a list of metrics
for each output, such as metrics=[['accuracy'], ['accuracy', 'mse']]
or metrics=['accuracy', ['accuracy', 'mse']] . When you pass the
strings 'accuracy' or 'acc', we convert this to one of
tf.keras.metrics.BinaryAccuracy ,
tf.keras.metrics.CategoricalAccuracy ,
tf.keras.metrics.SparseCategoricalAccuracy based on the loss
function used and the model output shape. We do a similar
conversion for the strings 'crossentropy' and 'ce' as well.

loss_weights

Optional list or dictionary specifying scalar coefficients
(Python floats) to weight the loss contributions of different model
outputs. The loss value that will be minimized by the model will then
be the weighted sum of all individual losses, weighted by the
loss_weights coefficients.
If a list, it is expected to have a 1:1 mapping to the model's
outputs. If a dict, it is expected to map output names (strings)
to scalar coefficients.

weighted_metrics

List of metrics to be evaluated and weighted by
sample_weight or class_weight during training and testing.

run_eagerly

Bool. Defaults to False . If True , this Model 's
logic will not be wrapped in a tf.function . Recommended to leave
this as None unless your Model cannot be run inside a
tf.function . run_eagerly=True is not supported when using
tf.distribute.experimental.ParameterServerStrategy .

steps_per_execution

Int. Defaults to 1. The number of batches to run
during each tf.function call. Running multiple batches inside a
single tf.function call can greatly improve performance on TPUs or
small models with a large Python overhead. At most, one full epoch
will be run each execution. If a number larger than the size of the
epoch is passed, the execution will be truncated to the size of the
epoch. Note that if steps_per_execution is set to N ,
Callback.on_batch_begin and Callback.on_batch_end methods will
only be called every N batches (i.e. before/after each tf.function
execution).

jit_compile

If True , compile the model training step with XLA.
XLA is an optimizing compiler for
machine learning.
jit_compile is not enabled for by default.
This option cannot be enabled with run_eagerly=True .
Note that jit_compile=True is
may not necessarily work for all models.
For more information on supported operations please refer to the
XLA documentation.
Also refer to
known XLA issues for
more details.

**kwargs

Arguments supported for backwards compatibility only. 
compute_loss
compute_loss(
x=None, y=None, y_pred=None, sample_weight=None
)
Compute the total loss, validate it, and return it.
Subclasses can optionally override this method to provide custom loss computation logic.
Example:
class MyModel(tf.keras.Model):
def __init__(self, *args, **kwargs):
super(MyModel, self).__init__(*args, **kwargs)
self.loss_tracker = tf.keras.metrics.Mean(name='loss')
def compute_loss(self, x, y, y_pred, sample_weight):
loss = tf.reduce_mean(tf.math.squared_difference(y_pred, y))
loss += tf.add_n(self.losses)
self.loss_tracker.update_state(loss)
return loss
def reset_metrics(self):
self.loss_tracker.reset_states()
@property
def metrics(self):
return [self.loss_tracker]
tensors = tf.random.uniform((10, 10)), tf.random.uniform((10,))
dataset = tf.data.Dataset.from_tensor_slices(tensors).repeat().batch(1)
inputs = tf.keras.layers.Input(shape=(10,), name='my_input')
outputs = tf.keras.layers.Dense(10)(inputs)
model = MyModel(inputs, outputs)
model.add_loss(tf.reduce_sum(outputs))
optimizer = tf.keras.optimizers.SGD()
model.compile(optimizer, loss='mse', steps_per_execution=10)
model.fit(dataset, epochs=2, steps_per_epoch=10)
print('My custom loss: ', model.loss_tracker.result().numpy())
Args  

x

Input data. 
y

Target data. 
y_pred

Predictions returned by the model (output of model(x) )

sample_weight

Sample weights for weighting the loss function. 
Returns  

The total loss as a tf.Tensor , or None if no loss results (which is
the case when called by Model.test_step ).

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_metrics
compute_metrics(
x, y, y_pred, sample_weight
)
Update metric states and collect all metrics to be returned.
Subclasses can optionally override this method to provide custom metric updating and collection logic.
Example:
class MyModel(tf.keras.Sequential):
def compute_metrics(self, x, y, y_pred, sample_weight):
# This super call updates `self.compiled_metrics` and returns results
# for all metrics listed in `self.metrics`.
metric_results = super(MyModel, self).compute_metrics(
x, y, y_pred, sample_weight)
# Note that `self.custom_metric` is not listed in `self.metrics`.
self.custom_metric.update_state(x, y, y_pred, sample_weight)
metric_results['custom_metric_name'] = self.custom_metric.result()
return metric_results
Args  

x

Input data. 
y

Target data. 
y_pred

Predictions returned by the model (output of model.call(x) )

sample_weight

Sample weights for weighting the loss function. 
Returns  

A dict containing values that will be passed to
tf.keras.callbacks.CallbackList.on_train_batch_end() . Typically, the
values of the metrics listed in self.metrics are returned. Example:
{'loss': 0.2, 'accuracy': 0.7} .

compute_output_shape
compute_output_shape(
input_shape
)
Computes the output shape of the layer.
This method will cause the layer's state to be built, if that has not happened before. This requires 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). 
evaluate
evaluate(
*args, **kwargs
)
Returns the loss value & metrics values for the model in test mode.
Computation is done in batches (see the batch_size
arg.)
Args  

x

Input data. It could be:

y

Target data. Like the input data x , it could be either Numpy
array(s) or TensorFlow tensor(s). It should be consistent with x
(you cannot have Numpy inputs and tensor targets, or inversely). If
x is a dataset, generator or keras.utils.Sequence instance, y
should not be specified (since targets will be obtained from the
iterator/dataset).

batch_size

Integer or None . Number of samples per batch of
computation. If unspecified, batch_size will default to 32. Do not
specify the batch_size if your data is in the form of a dataset,
generators, or keras.utils.Sequence instances (since they generate
batches).

verbose

"auto" , 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = single line.
"auto" defaults to 1 for most cases, and to 2 when used with
ParameterServerStrategy . Note that the progress bar is not
particularly useful when logged to a file, so verbose=2 is
recommended when not running interactively (e.g. in a production
environment).

sample_weight

Optional Numpy array of weights for the test samples,
used for weighting the loss function. You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples), or in the case of
temporal data, you can pass a 2D array with shape (samples,
sequence_length) , to apply a different weight to every timestep
of every sample. This argument is not supported when x is a
dataset, instead pass sample weights as the third element of x .

steps

Integer or None . Total number of steps (batches of samples)
before declaring the evaluation round finished. Ignored with the
default value of None . If x is a tf.data dataset and steps is
None, 'evaluate' will run until the dataset is exhausted. This
argument is not supported with array inputs.

callbacks

List of keras.callbacks.Callback instances. List of
callbacks to apply during evaluation. See
callbacks.

max_queue_size

Integer. Used for generator or keras.utils.Sequence
input only. Maximum size for the generator queue. If unspecified,
max_queue_size will default to 10.

workers

Integer. Used for generator or keras.utils.Sequence input
only. Maximum number of processes to spin up when using processbased
threading. If unspecified, workers will default to 1.

use_multiprocessing

Boolean. Used for generator or
keras.utils.Sequence input only. If True , use processbased
threading. If unspecified, use_multiprocessing will default to
False . Note that because this implementation relies on
multiprocessing, you should not pass nonpicklable arguments to the
generator as they can't be passed easily to children processes.

return_dict

If True , loss and metric results are returned as a dict,
with each key being the name of the metric. If False , they are
returned as a list.

**kwargs

Unused at this time. 
See the discussion of Unpacking behavior for iteratorlike inputs
for
Model.fit
.
Returns  

Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.

Raises  

RuntimeError

If model.evaluate is wrapped in a tf.function .

fit
fit(
x=None,
y=None,
batch_size=None,
epochs=1,
verbose='auto',
callbacks=None,
validation_split=0.0,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
validation_batch_size=None,
validation_freq=1,
max_queue_size=10,
workers=1,
use_multiprocessing=False
)
Trains the model for a fixed number of epochs (iterations on a dataset).
Args  

x

Input data. It could be:

y

Target data. Like the input data x ,
it could be either Numpy array(s) or TensorFlow tensor(s).
It should be consistent with x (you cannot have Numpy inputs and
tensor targets, or inversely). If x is a dataset, generator,
or keras.utils.Sequence instance, y should
not be specified (since targets will be obtained from x ).

batch_size

Integer or None .
Number of samples per gradient update.
If unspecified, batch_size will default to 32.
Do not specify the batch_size if your data is in the
form of datasets, generators, or keras.utils.Sequence instances
(since they generate batches).

epochs

Integer. Number of epochs to train the model.
An epoch is an iteration over the entire x and y
data provided
(unless the steps_per_epoch flag is set to
something other than None).
Note that in conjunction with initial_epoch ,
epochs is to be understood as "final epoch".
The model is not trained for a number of iterations
given by epochs , but merely until the epoch
of index epochs is reached.

verbose

'auto', 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = one line per epoch.
'auto' defaults to 1 for most cases, but 2 when used with
ParameterServerStrategy . Note that the progress bar is not
particularly useful when logged to a file, so verbose=2 is
recommended when not running interactively (eg, in a production
environment).

callbacks

List of keras.callbacks.Callback instances.
List of callbacks to apply during training.
See tf.keras.callbacks . Note tf.keras.callbacks.ProgbarLogger
and tf.keras.callbacks.History callbacks are created automatically
and need not be passed into model.fit .
tf.keras.callbacks.ProgbarLogger is created or not based on
verbose argument to model.fit .
Callbacks with batchlevel calls are currently unsupported with
tf.distribute.experimental.ParameterServerStrategy , and users are
advised to implement epochlevel calls instead with an appropriate
steps_per_epoch value.

validation_split

Float between 0 and 1.
Fraction of the training data to be used as validation data.
The model will set apart this fraction of the training data,
will not train on it, and will evaluate
the loss and any model metrics
on this data at the end of each epoch.
The validation data is selected from the last samples
in the x and y data provided, before shuffling. This argument is
not supported when x is a dataset, generator or
keras.utils.Sequence instance.
If both validation_data and validation_split are provided,
validation_data will override validation_split .
validation_split is not yet supported with
tf.distribute.experimental.ParameterServerStrategy .

validation_data

Data on which to evaluate
the loss and any model metrics at the end of each epoch.
The model will not be trained on this data. Thus, note the fact
that the validation loss of data provided using validation_split
or validation_data is not affected by regularization layers like
noise and dropout.
validation_data will override validation_split .
validation_data could be:

shuffle

Boolean (whether to shuffle the training data
before each epoch) or str (for 'batch'). This argument is ignored
when x is a generator or an object of tf.data.Dataset.
'batch' is a special option for dealing
with the limitations of HDF5 data; it shuffles in batchsized
chunks. Has no effect when steps_per_epoch is not None .

class_weight

Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an underrepresented class. 
sample_weight

Optional Numpy array of weights for
the training samples, used for weighting the loss function
(during training only). You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples),
or in the case of temporal data,
you can pass a 2D array with shape
(samples, sequence_length) ,
to apply a different weight to every timestep of every sample. This
argument is not supported when x is a dataset, generator, or
keras.utils.Sequence instance, instead provide the sample_weights
as the third element of x .

initial_epoch

Integer. Epoch at which to start training (useful for resuming a previous training run). 
steps_per_epoch

Integer or None .
Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch. When training with input tensors such as
TensorFlow data tensors, the default None is equal to
the number of samples in your dataset divided by
the batch size, or 1 if that cannot be determined. If x is a
tf.data dataset, and 'steps_per_epoch'
is None, the epoch will run until the input dataset is exhausted.
When passing an infinitely repeating dataset, you must specify the
steps_per_epoch argument. If steps_per_epoch=1 the training
will run indefinitely with an infinitely repeating dataset.
This argument is not supported with array inputs.
When using tf.distribute.experimental.ParameterServerStrategy :
steps_per_epoch=None is not supported.

validation_steps

Only relevant if validation_data is provided and
is a tf.data dataset. Total number of steps (batches of
samples) to draw before stopping when performing validation
at the end of every epoch. If 'validation_steps' is None, validation
will run until the validation_data dataset is exhausted. In the
case of an infinitely repeated dataset, it will run into an
infinite loop. If 'validation_steps' is specified and only part of
the dataset will be consumed, the evaluation will start from the
beginning of the dataset at each epoch. This ensures that the same
validation samples are used every time.

validation_batch_size

Integer or None .
Number of samples per validation batch.
If unspecified, will default to batch_size .
Do not specify the validation_batch_size if your data is in the
form of datasets, generators, or keras.utils.Sequence instances
(since they generate batches).

validation_freq

Only relevant if validation data is provided. Integer
or collections.abc.Container instance (e.g. list, tuple, etc.).
If an integer, specifies how many training epochs to run before a
new validation run is performed, e.g. validation_freq=2 runs
validation every 2 epochs. If a Container, specifies the epochs on
which to run validation, e.g. validation_freq=[1, 2, 10] runs
validation at the end of the 1st, 2nd, and 10th epochs.

max_queue_size

Integer. Used for generator or keras.utils.Sequence
input only. Maximum size for the generator queue.
If unspecified, max_queue_size will default to 10.

workers

Integer. Used for generator or keras.utils.Sequence input
only. Maximum number of processes to spin up
when using processbased threading. If unspecified, workers
will default to 1.

use_multiprocessing

Boolean. Used for generator or
keras.utils.Sequence input only. If True , use processbased
threading. If unspecified, use_multiprocessing will default to
False . Note that because this implementation relies on
multiprocessing, you should not pass nonpicklable arguments to
the generator as they can't be passed easily to children processes.

Unpacking behavior for iteratorlike inputs:
A common pattern is to pass a tf.data.Dataset, generator, or
tf.keras.utils.Sequence to the x
argument of fit, which will in fact
yield not only features (x) but optionally targets (y) and sample weights.
Keras requires that the output of such iteratorlikes be unambiguous. The
iterator should return a tuple of length 1, 2, or 3, where the optional
second and third elements will be used for y and sample_weight
respectively. Any other type provided will be wrapped in a length one
tuple, effectively treating everything as 'x'. When yielding dicts, they
should still adhere to the toplevel tuple structure.
e.g. ({"x0": x0, "x1": x1}, y)
. Keras will not attempt to separate
features, targets, and weights from the keys of a single dict.
A notable unsupported data type is the namedtuple. The reason is that
it behaves like both an ordered datatype (tuple) and a mapping
datatype (dict). So given a namedtuple of the form:
namedtuple("example_tuple", ["y", "x"])
it is ambiguous whether to reverse the order of the elements when
interpreting the value. Even worse is a tuple of the form:
namedtuple("other_tuple", ["x", "y", "z"])
where it is unclear if the tuple was intended to be unpacked into x, y,
and sample_weight or passed through as a single element to x
. As a
result the data processing code will simply raise a ValueError if it
encounters a namedtuple. (Along with instructions to remedy the issue.)
Returns  

A History object. Its History.history attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).

Raises  

RuntimeError


ValueError

In case of mismatch between the provided input data and what the model expects or when the input data is empty. 
from_config
@classmethod
from_config( config, custom_objects=None )
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 the config of the Model
.
Config is a Python dictionary (serializable) containing the configuration of
an object, which in this case is a Model
. This allows the Model
to be
be reinstantiated later (without its trained weights) from this
configuration.
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.
Developers of subclassed Model
are advised to override this method, and
continue to update the dict from super(MyModel, self).get_config()
to provide the proper configuration of this Model
. The default config
is an empty dict. Optionally, raise NotImplementedError
to allow Keras to
attempt a default serialization.
Returns  

Python dictionary containing the configuration of this Model .

get_layer
get_layer(
name=None, index=None
)
Retrieves a layer based on either its name (unique) or index.
If name
and index
are both provided, index
will take precedence.
Indices are based on order of horizontal graph traversal (bottomup).
Args  

name

String, name of layer. 
index

Integer, index of layer. 
Returns  

A layer instance. 
get_weights
get_weights()
Retrieves the weights of the model.
Returns  

A flat list of Numpy arrays. 
load_weights
load_weights(
filepath, by_name=False, skip_mismatch=False, options=None
)
Loads all layer weights, either from a TensorFlow or an HDF5 weight file.
If by_name
is False weights are loaded based on the network's
topology. This means the architecture should be the same as when the weights
were saved. Note that layers that don't have weights are not taken into
account in the topological ordering, so adding or removing layers is fine as
long as they don't have weights.
If by_name
is True, weights are loaded into layers only if they share the
same name. This is useful for finetuning or transferlearning models where
some of the layers have changed.
Only topological loading (by_name=False
) is supported when loading weights
from the TensorFlow format. Note that topological loading differs slightly
between TensorFlow and HDF5 formats for userdefined classes inheriting from
tf.keras.Model
: HDF5 loads based on a flattened list of weights, while the
TensorFlow format loads based on the objectlocal names of attributes to
which layers are assigned in the Model
's constructor.
Args  

filepath

String, path to the weights file to load. For weight files in
TensorFlow format, this is the file prefix (the same as was passed
to save_weights ). This can also be a path to a SavedModel
saved from model.save .

by_name

Boolean, whether to load weights by name or by topological order. Only topological loading is supported for weight files in TensorFlow format. 
skip_mismatch

Boolean, whether to skip loading of layers where there is
a mismatch in the number of weights, or a mismatch in the shape of
the weight (only valid when by_name=True ).

options

Optional tf.train.CheckpointOptions object that specifies
options for loading weights.

Returns  

When loading a weight file in TensorFlow format, returns the same status
object as tf.train.Checkpoint.restore . When graph building, restore
ops are run automatically as soon as the network is built (on first call
for userdefined classes inheriting from Model , immediately if it is
already built).
When loading weights in HDF5 format, returns 
Raises  

ImportError

If h5py is not available and the weight file is in HDF5
format.

ValueError

If skip_mismatch is set to True when by_name is
False .

make_predict_function
make_predict_function(
force=False
)
Creates a function that executes one step of inference.
This method can be overridden to support custom inference logic.
This method is called by Model.predict
and Model.predict_on_batch
.
Typically, this method directly controls tf.function
and
tf.distribute.Strategy
settings, and delegates the actual evaluation
logic to Model.predict_step
.
This function is cached the first time Model.predict
or
Model.predict_on_batch
is called. The cache is cleared whenever
Model.compile
is called. You can skip the cache and generate again the
function with force=True
.
Args  

force

Whether to regenerate the predict function and skip the cached function if available. 
Returns  

Function. The function created by this method should accept a
tf.data.Iterator , and return the outputs of the Model .

make_test_function
make_test_function(
force=False
)
Creates a function that executes one step of evaluation.
This method can be overridden to support custom evaluation logic.
This method is called by Model.evaluate
and Model.test_on_batch
.
Typically, this method directly controls tf.function
and
tf.distribute.Strategy
settings, and delegates the actual evaluation
logic to Model.test_step
.
This function is cached the first time Model.evaluate
or
Model.test_on_batch
is called. The cache is cleared whenever
Model.compile
is called. You can skip the cache and generate again the
function with force=True
.
Args  

force

Whether to regenerate the test function and skip the cached function if available. 
Returns  

Function. The function created by this method should accept a
tf.data.Iterator , and return a dict containing values that will
be passed to tf.keras.Callbacks.on_test_batch_end .

make_train_function
make_train_function(
force=False
)
Creates a function that executes one step of training.
This method can be overridden to support custom training logic.
This method is called by Model.fit
and Model.train_on_batch
.
Typically, this method directly controls tf.function
and
tf.distribute.Strategy
settings, and delegates the actual training
logic to Model.train_step
.
This function is cached the first time Model.fit
or
Model.train_on_batch
is called. The cache is cleared whenever
Model.compile
is called. You can skip the cache and generate again the
function with force=True
.
Args  

force

Whether to regenerate the train function and skip the cached function if available. 
Returns  

Function. The function created by this method should accept a
tf.data.Iterator , and return a dict containing values that will
be passed to tf.keras.Callbacks.on_train_batch_end , such as
{'loss': 0.2, 'accuracy': 0.7} .

predict
predict(
*args, **kwargs
)
Generates output predictions for the input samples.
Computation is done in batches. This method is designed for batch processing of large numbers of inputs. It is not intended for use inside of loops that iterate over your data and process small numbers of inputs at a time.
For small numbers of inputs that fit in one batch,
directly use __call__()
for faster execution, e.g.,
model(x)
, or model(x, training=False)
if you have layers such as
tf.keras.layers.BatchNormalization
that behave differently during
inference. You may pair the individual model call with a tf.function
for additional performance inside your inner loop.
If you need access to numpy array values instead of tensors after your
model call, you can use tensor.numpy()
to get the numpy array value of
an eager tensor.
Also, note the fact that test loss is not affected by regularization layers like noise and dropout.
Args  

x

Input samples. It could be:

batch_size

Integer or None .
Number of samples per batch.
If unspecified, batch_size will default to 32.
Do not specify the batch_size if your data is in the
form of dataset, generators, or keras.utils.Sequence instances
(since they generate batches).

verbose

"auto" , 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = single line.
"auto" defaults to 1 for most cases, and to 2 when used with
ParameterServerStrategy . Note that the progress bar is not
particularly useful when logged to a file, so verbose=2 is
recommended when not running interactively (e.g. in a production
environment).

steps

Total number of steps (batches of samples)
before declaring the prediction round finished.
Ignored with the default value of None . If x is a tf.data
dataset and steps is None, predict() will
run until the input dataset is exhausted.

callbacks

List of keras.callbacks.Callback instances.
List of callbacks to apply during prediction.
See callbacks.

max_queue_size

Integer. Used for generator or keras.utils.Sequence
input only. Maximum size for the generator queue.
If unspecified, max_queue_size will default to 10.

workers

Integer. Used for generator or keras.utils.Sequence input
only. Maximum number of processes to spin up when using
processbased threading. If unspecified, workers will default
to 1.

use_multiprocessing

Boolean. Used for generator or
keras.utils.Sequence input only. If True , use processbased
threading. If unspecified, use_multiprocessing will default to
False . Note that because this implementation relies on
multiprocessing, you should not pass nonpicklable arguments to
the generator as they can't be passed easily to children processes.

See the discussion of Unpacking behavior for iteratorlike inputs
for
Model.fit
. Note that Model.predict uses the same interpretation rules as
Model.fit
and Model.evaluate
, so inputs must be unambiguous for all
three methods.
Returns  

Numpy array(s) of predictions. 
Raises  

RuntimeError

If model.predict is wrapped in a tf.function .

ValueError

In case of mismatch between the provided input data and the model's expectations, or in case a stateful model receives a number of samples that is not a multiple of the batch size. 
predict_on_batch
predict_on_batch(
x
)
Returns predictions for a single batch of samples.
Args  

x

Input data. It could be:

Returns  

Numpy array(s) of predictions. 
Raises  

RuntimeError

If model.predict_on_batch is wrapped in a tf.function .

predict_step
predict_step(
data
)
The logic for one inference step.
This method can be overridden to support custom inference logic.
This method is called by Model.make_predict_function
.
This method should contain the mathematical logic for one step of inference. This typically includes the forward pass.
Configuration details for how this logic is run (e.g. tf.function
and
tf.distribute.Strategy
settings), should be left to
Model.make_predict_function
, which can also be overridden.
Args  

data

A nested structure of Tensor s.

Returns  

The result of one inference step, typically the output of calling the
Model on data.

reset_metrics
reset_metrics()
Resets the state of all the metrics in the model.
Examples:
inputs = tf.keras.layers.Input(shape=(3,))
outputs = tf.keras.layers.Dense(2)(inputs)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
x = np.random.random((2, 3))
y = np.random.randint(0, 2, (2, 2))
_ = model.fit(x, y, verbose=0)
assert all(float(m.result()) for m in model.metrics)
model.reset_metrics()
assert all(float(m.result()) == 0 for m in model.metrics)
reset_states
reset_states()
save
save(
filepath,
overwrite=True,
include_optimizer=True,
save_format=None,
signatures=None,
options=None,
save_traces=True
)
Saves the model to Tensorflow SavedModel or a single HDF5 file.
Please see tf.keras.models.save_model
or the
Serialization and Saving guide
for details.
Args  

filepath

String, PathLike, path to SavedModel or H5 file to save the model. 
overwrite

Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt. 
include_optimizer

If True, save optimizer's state together. 
save_format

Either 'tf' or 'h5' , indicating whether to save the
model to Tensorflow SavedModel or HDF5. Defaults to 'tf' in TF 2.X,
and 'h5' in TF 1.X.

signatures

Signatures to save with the SavedModel. Applicable to the
'tf' format only. Please see the signatures argument in
tf.saved_model.save for details.

options

(only applies to SavedModel format)
tf.saved_model.SaveOptions object that specifies options for
saving to SavedModel.

save_traces

(only applies to SavedModel format) When enabled, the
SavedModel will store the function traces for each layer. This
can be disabled, so that only the configs of each layer are stored.
Defaults to True . Disabling this will decrease serialization time
and reduce file size, but it requires that all custom layers/models
implement a get_config() method.

Example:
from keras.models import load_model
model.save('my_model.h5') # creates a HDF5 file 'my_model.h5'
del model # deletes the existing model
# returns a compiled model
# identical to the previous one
model = load_model('my_model.h5')
save_spec
save_spec(
dynamic_batch=True
)
Returns the tf.TensorSpec
of call inputs as a tuple (args, kwargs)
.
This value is automatically defined after calling the model for the first time. Afterwards, you can use it when exporting the model for serving:
model = tf.keras.Model(...)
@tf.function
def serve(*args, **kwargs):
outputs = model(*args, **kwargs)
# Apply postprocessing steps, or add additional outputs.
...
return outputs
# arg_specs is `[tf.TensorSpec(...), ...]`. kwarg_specs, in this example, is
# an empty dict since functional models do not use keyword arguments.
arg_specs, kwarg_specs = model.save_spec()
model.save(path, signatures={
'serving_default': serve.get_concrete_function(*arg_specs, **kwarg_specs)
})
Args  

dynamic_batch

Whether to set the batch sizes of all the returned
tf.TensorSpec to None . (Note that when defining functional or
Sequential models with tf.keras.Input([...], batch_size=X) , the
batch size will always be preserved). Defaults to True .

Returns  

If the model inputs are defined, returns a tuple (args, kwargs) . All
elements in args and kwargs are tf.TensorSpec .
If the model inputs are not defined, returns None .
The model inputs are automatically set when calling the model,
model.fit , model.evaluate or model.predict .

save_weights
save_weights(
filepath, overwrite=True, save_format=None, options=None
)
Saves all layer weights.
Either saves in HDF5 or in TensorFlow format based on the save_format
argument.
When saving in HDF5 format, the weight file has:
layer_names
(attribute), a list of strings (ordered names of model layers). For every layer, a
group
namedlayer.name
 For every such layer group, a group attribute
weight_names
, a list of strings (ordered names of weights tensor of the layer).  For every weight in the layer, a dataset storing the weight value, named after the weight tensor.
 For every such layer group, a group attribute
When saving in TensorFlow format, all objects referenced by the network are
saved in the same format as tf.train.Checkpoint
, including any Layer
instances or Optimizer
instances assigned to object attributes. For
networks constructed from inputs and outputs using tf.keras.Model(inputs,
outputs)
, Layer
instances used by the network are tracked/saved
automatically. For userdefined classes which inherit from tf.keras.Model
,
Layer
instances must be assigned to object attributes, typically in the
constructor. See the documentation of tf.train.Checkpoint
and
tf.keras.Model
for details.
While the formats are the same, do not mix save_weights
and
tf.train.Checkpoint
. Checkpoints saved by Model.save_weights
should be
loaded using Model.load_weights
. Checkpoints saved using
tf.train.Checkpoint.save
should be restored using the corresponding
tf.train.Checkpoint.restore
. Prefer tf.train.Checkpoint
over
save_weights
for training checkpoints.
The TensorFlow format matches objects and variables by starting at a root
object, self
for save_weights
, and greedily matching attribute
names. For Model.save
this is the Model
, and for Checkpoint.save
this
is the Checkpoint
even if the Checkpoint
has a model attached. This
means saving a tf.keras.Model
using save_weights
and loading into a
tf.train.Checkpoint
with a Model
attached (or vice versa) will not match
the Model
's variables. See the
guide to training checkpoints
for details on the TensorFlow format.
Args  

filepath

String or PathLike, path to the file to save the weights to. When saving in TensorFlow format, this is the prefix used for checkpoint files (multiple files are generated). Note that the '.h5' suffix causes weights to be saved in HDF5 format. 
overwrite

Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt. 
save_format

Either 'tf' or 'h5'. A filepath ending in '.h5' or
'.keras' will default to HDF5 if save_format is None . Otherwise
None defaults to 'tf'.

options

Optional tf.train.CheckpointOptions object that specifies
options for saving weights.

Raises  

ImportError

If h5py is not available when attempting to save in HDF5
format.

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. 
summary
summary(
line_length=None,
positions=None,
print_fn=None,
expand_nested=False,
show_trainable=False
)
Prints a string summary of the network.
Args  

line_length

Total length of printed lines (e.g. set this to adapt the display to different terminal window sizes). 
positions

Relative or absolute positions of log elements
in each line. If not provided,
defaults to [.33, .55, .67, 1.] .

print_fn

Print function to use. Defaults to print .
It will be called on each line of the summary.
You can set it to a custom function
in order to capture the string summary.

expand_nested

Whether to expand the nested models.
If not provided, defaults to False .

show_trainable

Whether to show if a layer is trainable.
If not provided, defaults to False .

Raises  

ValueError

if summary() is called before the model is built.

test_on_batch
test_on_batch(
x, y=None, sample_weight=None, reset_metrics=True, return_dict=False
)
Test the model on a single batch of samples.
Args  

x

Input data. It could be:

y

Target data. Like the input data x , it could be either Numpy
array(s) or TensorFlow tensor(s). It should be consistent with x
(you cannot have Numpy inputs and tensor targets, or inversely).

sample_weight

Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. 
reset_metrics

If True , the metrics returned will be only for this
batch. If False , the metrics will be statefully accumulated across
batches.

return_dict

If True , loss and metric results are returned as a dict,
with each key being the name of the metric. If False , they are
returned as a list.

Returns  

Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.

Raises  

RuntimeError

If model.test_on_batch is wrapped in a tf.function .

test_step
test_step(
data
)
The logic for one evaluation step.
This method can be overridden to support custom evaluation logic.
This method is called by Model.make_test_function
.
This function should contain the mathematical logic for one step of evaluation. This typically includes the forward pass, loss calculation, and metrics updates.
Configuration details for how this logic is run (e.g. tf.function
and
tf.distribute.Strategy
settings), should be left to
Model.make_test_function
, which can also be overridden.
Args  

data

A nested structure of Tensor s.

Returns  

A dict containing values that will be passed to
tf.keras.callbacks.CallbackList.on_train_batch_end . Typically, the
values of the Model 's metrics are returned.

to_json
to_json(
**kwargs
)
Returns a JSON string containing the network configuration.
To load a network from a JSON save file, use
keras.models.model_from_json(json_string, custom_objects={})
.
Args  

**kwargs

Additional keyword arguments
to be passed to json.dumps() .

Returns  

A JSON string. 
to_yaml
to_yaml(
**kwargs
)
Returns a yaml string containing the network configuration.
To load a network from a yaml save file, use
keras.models.model_from_yaml(yaml_string, custom_objects={})
.
custom_objects
should be a dictionary mapping
the names of custom losses / layers / etc to the corresponding
functions / classes.
Args  

**kwargs

Additional keyword arguments
to be passed to yaml.dump() .

Returns  

A YAML string. 
Raises  

RuntimeError

announces that the method poses a security risk 
train_on_batch
train_on_batch(
x,
y=None,
sample_weight=None,
class_weight=None,
reset_metrics=True,
return_dict=False
)
Runs a single gradient update on a single batch of data.
Args  

x

Input data. It could be:

y

Target data. Like the input data x , it could be either Numpy
array(s) or TensorFlow tensor(s).

sample_weight

Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. 
class_weight

Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an underrepresented class. 
reset_metrics

If True , the metrics returned will be only for this
batch. If False , the metrics will be statefully accumulated across
batches.

return_dict

If True , loss and metric results are returned as a dict,
with each key being the name of the metric. If False , they are
returned as a list.

Returns  

Scalar training loss
(if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.

Raises  

RuntimeError

If model.train_on_batch is wrapped in a tf.function .

train_step
train_step(
data
)
The logic for one training step.
This method can be overridden to support custom training logic.
For concrete examples of how to override this method see
Customizing what happends in fit.
This method is called by Model.make_train_function
.
This method should contain the mathematical logic for one step of training. This typically includes the forward pass, loss calculation, backpropagation, and metric updates.
Configuration details for how this logic is run (e.g. tf.function
and
tf.distribute.Strategy
settings), should be left to
Model.make_train_function
, which can also be overridden.
Args  

data

A nested structure of Tensor s.

Returns  

A dict containing values that will be passed to
tf.keras.callbacks.CallbackList.on_train_batch_end . Typically, the
values of the Model 's metrics are returned. Example:
{'loss': 0.2, 'accuracy': 0.7} .

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
)