DCNN
DCNN(args, *kwargs)
The DCNN class corresponds to the Neural Convolution Network algorithm for Natural Language Processing.
Parameters
vocab_size: Vocabulary size of the algorithm input text.
-
emb_dim
: intEmbedding size.
-
nb_filters
: intFilter size for each layer Conv1D.
-
FFN_units
: intUnits for dense layer.
-
nb_classes
: intNumbers of final categories.
-
dropout_rate
: floatDropout parameter.
-
training
: boolTrainning process activated.
-
name
: strCustom Model Name.
weights_path: str Path load weight model.
Attributes
-
embedding
: tf.keras.layers.EmbeddingEmbedding layer for input vocabulary.
-
bigram
: tf.keras.layers.Conv1D1D convolution layer, for two letters in a row.
-
trigram
: tf.keras.layers.Conv1D1D convolution layer, for three letters in a row.
-
fourgram
: tf.keras.layers.Conv1D1D convolution layer, for four letters in a row.
-
pool
: tf.keras.layers.GlobalMaxPool1DMax pooling operation for 1D temporal data.
-
dense_1
: tf.keras.layers.DenseRegular densely-connected NN layer, concatenate 1D Convolutions.
-
last_dense
: tf.keras.layers.DenseRegular densely-connected NN layer, final decision.
-
dropout
: tf.keras.layers.DropoutApplies Dropout to dense_1.
Examples:
VOCAB_SIZE = tokenizer.vocab_size # 65540
EMB_DIM = 200
NB_FILTERS = 100
FFN_UNITS = 256
NB_CLASSES = 2#len(set(train_labels))
DROPOUT_RATE = 0.2
BATCH_SIZE = 32
NB_EPOCHS = 5
Dcnn = DCNN(vocab_size=VOCAB_SIZE,
emb_dim=EMB_DIM,
nb_filters=NB_FILTERS,
FFN_units=FFN_UNITS,
nb_classes=NB_CLASSES,
dropout_rate=DROPOUT_RATE)
if NB_CLASSES == 2:
Dcnn.compile(loss="binary_crossentropy",
optimizer="adam",
metrics=["accuracy"])
else:
Dcnn.compile(loss="sparse_categorical_crossentropy",
optimizer="adam",
metrics=["sparse_categorical_accuracy"])
# Entrenamiento
Dcnn.fit(train_inputs,
train_labels,
batch_size=BATCH_SIZE,
epochs=NB_EPOCHS)
# Evaluation
results = Dcnn.evaluate(test_inputs, test_labels, batch_size=BATCH_SIZE)
print(results)
Methods
add_loss(losses, inputs=None)
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(inputs, self):
self.add_loss(tf.abs(tf.reduce_mean(inputs)), inputs=True)
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
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,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Weight regularization.
model.add_loss(lambda: tf.reduce_mean(x.kernel))
The get_losses_for
method allows to retrieve the losses relevant to a
specific set of inputs.
Arguments:
losses: Loss tensor, or list/tuple of tensors. Rather than tensors, losses
may also be zero-argument callables which create a loss tensor.
inputs: Ignored when executing eagerly. If anything other than None is
passed, it signals the losses are conditional on some of the layer's
inputs, and thus they should only be run where these inputs are
available. This is the case for activity regularization losses, for
instance. If None
is passed, the losses are assumed
to be unconditional, and will apply across all dataflows of the layer
(e.g. weight regularization losses).
add_metric(value, aggregation=None, name=None)
Adds metric tensor to the layer.
Args:
value: Metric tensor.
aggregation: Sample-wise metric reduction function. If aggregation=None
,
it indicates that the metric tensor provided has been aggregated
already. eg, bin_acc = BinaryAccuracy(name='acc')
followed by
model.add_metric(bin_acc(y_true, y_pred))
. If aggregation='mean', the
given metric tensor will be sample-wise reduced using mean
function.
eg, model.add_metric(tf.reduce_sum(outputs), name='output_mean',
aggregation='mean')
.
name: String metric name.
Raises:
ValueError: If aggregation
is anything other than None or mean
.
add_update(updates, inputs=None)
Add update op(s), potentially dependent on layer inputs. (deprecated arguments)
Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs)
. They will be removed in a future version.
Instructions for updating:
inputs
is now automatically inferred
Weight updates (for instance, the updates of the moving mean and variance
in a BatchNormalization layer) 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.updates
may be
dependent on a
and some on b
. This method automatically keeps track
of dependencies.
The get_updates_for
method allows to retrieve the updates relevant to a
specific set of inputs.
This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).
Arguments:
updates: Update op, or list/tuple of update ops, or zero-arg callable
that returns an update op. A zero-arg callable should be passed in
order to disable running the updates by setting trainable=False
on this Layer, when executing in Eager mode.
inputs: Deprecated, will be automatically inferred.
add_variable(args, *kwargs)
Deprecated, do NOT use! Alias for add_weight
. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Please use layer.add_weight
method instead.
add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=
Adds a new variable to the layer.
Arguments:
name: Variable name.
shape: Variable shape. Defaults to scalar if unspecified.
dtype: The type of the variable. Defaults to self.dtype
or float32
.
initializer: Initializer instance (callable).
regularizer: Regularizer instance (callable).
trainable: Boolean, whether the variable should be part of the layer's
"trainable_variables" (e.g. variables, biases)
or "non_trainable_variables" (e.g. BatchNorm mean and variance).
Note that trainable
cannot be True
if synchronization
is set to ON_READ
.
constraint: Constraint instance (callable).
partitioner: Partitioner to be passed to the Trackable
API.
use_resource: Whether to use ResourceVariable
.
synchronization: Indicates when a distributed a variable will be
aggregated. Accepted values are constants defined in the class
tf.VariableSynchronization
. By default the synchronization is set to
AUTO
and the current DistributionStrategy
chooses
when to synchronize. If synchronization
is set to ON_READ
,
trainable
must not be set to True
.
aggregation: Indicates how a distributed variable will be aggregated.
Accepted values are constants defined in the class
tf.VariableAggregation
.
**kwargs: Additional keyword arguments. Accepted values are getter
,
collections
, experimental_autocast
and caching_device
.
Returns:
The created variable. Usually either a Variable
or ResourceVariable
instance. If partitioner
is not None
, a PartitionedVariable
instance is returned.
Raises:
RuntimeError: If called with partitioned variable regularization and
eager execution is enabled.
ValueError: When giving unsupported dtype and no initializer or when
trainable has been set to True with synchronization set as ON_READ
.
apply(inputs, args, *kwargs)
Deprecated, do NOT use! (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Please use layer.__call__
method instead.
This is an alias of self.__call__
.
Arguments:
inputs: Input tensor(s).
args: additional positional arguments to be passed to self.call
.
*kwargs: additional keyword arguments to be passed to self.call
.
Returns: Output tensor(s).
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, or list of shapes, where shapes are tuples, integers, or TensorShapes.
Raises: ValueError: 1. In case of invalid user-provided data (not of type tuple, list, or TensorShape). 2. If the model requires call arguments that are agnostic to the input shapes (positional or kwarg in call signature). 3. If not all layers were properly built. 4. If float type inputs are not supported within the layers.
In each of these cases, the user should build their model by calling it on real tensor data.
call(inputs, training)
Calling the build function of the model.
Parameters
inputs: Tensor. Input Tensor.
-
Training
: boolTrainning process activated.
Returns: output: Tensor. Output Tensor.
compile(optimizer='rmsprop', loss=None, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, kwargs)
Configures the model for training.
Arguments:
optimizer: String (name of optimizer) or optimizer instance.
See tf.keras.optimizers
.
loss: String (name of objective function), objective function or
tf.keras.losses.Loss
instance. See tf.keras.losses
.
An objective function is any callable with the signature
loss = fn(y_true, y_pred)
, where
y_true = ground truth values with shape = [batch_size, d0, .. dN]
,
except sparse loss functions such as sparse categorical crossentropy
where shape = [batch_size, d0, .. dN-1]
.
y_pred = predicted values with shape = [batch_size, d0, .. dN]
.
It returns a weighted loss float tensor.
If a custom Loss
instance is used and reduction is set to NONE,
return value has the shape [batch_size, d0, .. dN-1] ie. per-sample
or per-timestep 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.
metrics: List of metrics to be evaluated by the model during training
and testing.
Each of this can be a string (name of a built-in 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
multi-output model, you could also pass a dictionary, such as
metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}
.
You can also pass a list (len = len(outputs)) of lists of metrics
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.
sample_weight_mode: If you need to do timestep-wise
sample weighting (2D weights), set this to "temporal"
.
None
defaults to sample-wise weights (1D).
If the model has multiple outputs, you can use a different
sample_weight_mode
on each output by passing a
dictionary or a list of modes.
weighted_metrics: List of metrics to be evaluated and weighted
by sample_weight or class_weight during training and testing.
**kwargs: Any additional arguments. For eager execution, pass
run_eagerly=True
.
Raises:
ValueError: In case of invalid arguments for
optimizer
, loss
, metrics
or sample_weight_mode
.
compute_mask(inputs, mask)
Computes an output mask tensor.
Arguments: 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(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.
Arguments: 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.
compute_output_signature(input_signature)
Compute the output tensor signature of the layer based on the inputs.
Unlike a TensorShape object, a TensorSpec object contains both shape
and dtype information for a tensor. This method allows layers to provide
output dtype information if it is different from the input dtype.
For any layer that doesn't implement this function,
the framework will fall back to use compute_output_shape
, and will
assume that the output dtype matches the input dtype.
Args: input_signature: Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer.
Returns: Single TensorSpec or nested structure of TensorSpec objects, describing how the layer would transform the provided input.
Raises: TypeError: If input_signature contains a non-TensorSpec object.
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(x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, return_dict=False)
Returns the loss value & metrics values for the model in test mode.
Computation is done in batches.
Arguments:
x: Input data. It could be: - A Numpy array (or array-like), or a list
of arrays (in case the model has multiple inputs). - A TensorFlow
tensor, or a list of tensors (in case the model has multiple inputs).
- A dict mapping input names to the corresponding array/tensors, if
the model has named inputs. - A tf.data
dataset. - A generator or
keras.utils.Sequence
instance. A more detailed description of
unpacking behavior for iterator types (Dataset, generator, Sequence)
is given in the Unpacking behavior for iterator-like inputs
section
of Model.fit
.
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 gradient update. 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: 0 or 1. Verbosity mode. 0 = silent, 1 = progress bar.
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. In this case you should make sure to specify
sample_weight_mode="temporal"
in compile()
. 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 process-based
threading. If unspecified, workers
will default to 1. If 0, will
execute the generator on the main thread.
use_multiprocessing: Boolean. Used for generator or
keras.utils.Sequence
input only. If True
, use process-based
threading. If unspecified, use_multiprocessing
will default to
False
. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable 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.
See the discussion of Unpacking behavior for iterator-like 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: ValueError: in case of invalid arguments.
evaluate_generator(generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)
Evaluates the model on a data generator. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use Model.evaluate, which supports generators.
DEPRECATED:
Model.evaluate
now supports generators, so there is no longer any need
to use this endpoint.
fit(x=None, y=None, batch_size=None, epochs=1, verbose=1, 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).
Arguments:
x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).
- A TensorFlow tensor, or a list of tensors
(in case the model has multiple inputs).
- A dict mapping input names to the corresponding array/tensors,
if the model has named inputs.
- A tf.data
dataset. Should return a tuple
of either (inputs, targets)
or
(inputs, targets, sample_weights)
.
- A generator or keras.utils.Sequence
returning (inputs, targets)
or (inputs, targets, sample_weights)
.
A more detailed description of unpacking behavior for iterator types
(Dataset, generator, Sequence) is given below.
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.
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: 0, 1, or 2. Verbosity mode.
0 = silent, 1 = progress bar, 2 = one line per epoch.
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
.
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.
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.
validation_data
will override validation_split
.
validation_data
could be:
- tuple (x_val, y_val)
of Numpy arrays or tensors
- tuple (x_val, y_val, val_sample_weights)
of Numpy arrays
- dataset
For the first two cases, batch_size
must be provided.
For the last case, validation_steps
could be provided.
Note that validation_data
does not support all the data types that
are supported in x
, eg, dict, generator or keras.utils.Sequence
.
shuffle: Boolean (whether to shuffle the training data
before each epoch) or str (for 'batch'). This argument is ignored
when x
is a generator. 'batch' is a special option for dealing
with the limitations of HDF5 data; it shuffles in batch-sized
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 under-represented 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.
In this case you should make sure to specify
sample_weight_mode="temporal"
in compile()
. 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. This argument is not supported with
array inputs.
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 process-based threading. If unspecified, workers
will default to 1. If 0, will execute the generator on the main
thread.
use_multiprocessing: Boolean. Used for generator or
keras.utils.Sequence
input only. If True
, use process-based
threading. If unspecified, use_multiprocessing
will default to
False
. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can't be passed easily to children processes.
Unpacking behavior for iterator-like 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 iterator-likes 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 top-level 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: If the model was never compiled. ValueError: In case of mismatch between the provided input data and what the model expects.
fit_generator(generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, validation_freq=1, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0)
Fits the model on data yielded batch-by-batch by a Python generator. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use Model.fit, which supports generators.
DEPRECATED:
Model.fit
now supports generators, so there is no longer any need to use
this endpoint.
from_config(config, custom_objects=None)
Instantiates a Model from its config (output of get_config()
).
Arguments: config: Model config dictionary. custom_objects: Optional dictionary mapping names (strings) to custom classes or functions to be considered during deserialization.
Returns: A model instance.
Raises: ValueError: In case of improperly formatted config dict.
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).
Returns: Python dictionary.
get_input_at(node_index)
Retrieves the input tensor(s) of a layer at a given node.
Arguments:
node_index: Integer, index of the node
from which to retrieve the attribute.
E.g. node_index=0
will correspond to the
first time the layer was called.
Returns: A tensor (or list of tensors if the layer has multiple inputs).
Raises: RuntimeError: If called in Eager mode.
get_input_mask_at(node_index)
Retrieves the input mask tensor(s) of a layer at a given node.
Arguments:
node_index: Integer, index of the node
from which to retrieve the attribute.
E.g. node_index=0
will correspond to the
first time the layer was called.
Returns: A mask tensor (or list of tensors if the layer has multiple inputs).
get_input_shape_at(node_index)
Retrieves the input shape(s) of a layer at a given node.
Arguments:
node_index: Integer, index of the node
from which to retrieve the attribute.
E.g. node_index=0
will correspond to the
first time the layer was called.
Returns: A shape tuple (or list of shape tuples if the layer has multiple inputs).
Raises: RuntimeError: If called in Eager mode.
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 (bottom-up).
Arguments: name: String, name of layer. index: Integer, index of layer.
Returns: A layer instance.
Raises: ValueError: In case of invalid layer name or index.
get_losses_for(inputs)
Retrieves losses relevant to a specific set of inputs.
Arguments: inputs: Input tensor or list/tuple of input tensors.
Returns:
List of loss tensors of the layer that depend on inputs
.
get_output_at(node_index)
Retrieves the output tensor(s) of a layer at a given node.
Arguments:
node_index: Integer, index of the node
from which to retrieve the attribute.
E.g. node_index=0
will correspond to the
first time the layer was called.
Returns: A tensor (or list of tensors if the layer has multiple outputs).
Raises: RuntimeError: If called in Eager mode.
get_output_mask_at(node_index)
Retrieves the output mask tensor(s) of a layer at a given node.
Arguments:
node_index: Integer, index of the node
from which to retrieve the attribute.
E.g. node_index=0
will correspond to the
first time the layer was called.
Returns: A mask tensor (or list of tensors if the layer has multiple outputs).
get_output_shape_at(node_index)
Retrieves the output shape(s) of a layer at a given node.
Arguments:
node_index: Integer, index of the node
from which to retrieve the attribute.
E.g. node_index=0
will correspond to the
first time the layer was called.
Returns: A shape tuple (or list of shape tuples if the layer has multiple outputs).
Raises: RuntimeError: If called in Eager mode.
get_updates_for(inputs)
Retrieves updates relevant to a specific set of inputs.
Arguments: inputs: Input tensor or list/tuple of input tensors.
Returns:
List of update ops of the layer that depend on inputs
.
get_weights()
Retrieves the weights of the model.
Returns: A flat list of Numpy arrays.
load_weights(filepath, by_name=False, skip_mismatch=False)
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 fine-tuning or transfer-learning 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 user-defined classes inheriting from
tf.keras.Model
: HDF5 loads based on a flattened list of weights, while the
TensorFlow format loads based on the object-local names of attributes to
which layers are assigned in the Model
's constructor.
Arguments:
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
).
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
).
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 user-defined classes inheriting from Model
, immediately if it is
already built).
When loading weights in HDF5 format, returns None
.
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()
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.
Returns:
Function. The function created by this method should accept a
tf.data.Iterator
, and return the outputs of the Model
.
make_test_function()
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.
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()
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.
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(x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)
Generates output predictions for the input samples.
Computation is done in batches. This method is designed for performance in
large scale inputs. For small amount of inputs that fit in one batch,
directly using __call__
is recommended for faster execution, e.g.,
model(x)
, or model(x, training=False)
if you have layers such as
tf.keras.layers.BatchNormalization
that behaves differently during
inference.
Arguments:
x: Input samples. It could be:
- A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).
- A TensorFlow tensor, or a list of tensors
(in case the model has multiple inputs).
- A tf.data
dataset.
- A generator or keras.utils.Sequence
instance.
A more detailed description of unpacking behavior for iterator types
(Dataset, generator, Sequence) is given in the Unpacking behavior
for iterator-like inputs
section of Model.fit
.
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: Verbosity mode, 0 or 1.
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
process-based threading. If unspecified, workers
will default
to 1. If 0, will execute the generator on the main thread.
use_multiprocessing: Boolean. Used for generator or
keras.utils.Sequence
input only. If True
, use process-based
threading. If unspecified, use_multiprocessing
will default to
False
. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can't be passed easily to children processes.
See the discussion of Unpacking behavior for iterator-like 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: 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_generator(generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)
Generates predictions for the input samples from a data generator. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use Model.predict, which supports generators.
DEPRECATED:
Model.predict
now supports generators, so there is no longer any need
to use this endpoint.
predict_on_batch(x)
Returns predictions for a single batch of samples.
Arguments: x: Input data. It could be: - A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). - A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
Returns: Numpy array(s) of predictions.
Raises: ValueError: In case of mismatch between given number of inputs and expectations of the model.
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 mathemetical 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.
Arguments:
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()
Resets the state of metrics.
reset_states()
None
save(filepath, overwrite=True, include_optimizer=True, save_format=None, signatures=None, options=None)
Saves the model to Tensorflow SavedModel or a single HDF5 file.
The savefile includes: - The model architecture, allowing to re-instantiate the model. - The model weights. - The state of the optimizer, allowing to resume training exactly where you left off.
This allows you to save the entirety of the state of a model in a single file.
Saved models can be reinstantiated via keras.models.load_model
.
The model returned by load_model
is a compiled model ready to be used
(unless the saved model was never compiled in the first place).
Models built with the Sequential and Functional API can be saved to both the HDF5 and SavedModel formats. Subclassed models can only be saved with the SavedModel format.
Note that the model weights may have different scoped names after being
loaded. Scoped names include the model/layer names, such as
"dense_1/kernel:0". It is recommended that you use the layer properties to
access specific variables, e.g.
model.get_layer("dense_1").kernel`.
Arguments:
filepath: String, 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: Optional tf.saved_model.SaveOptions
object that specifies
options for saving to SavedModel.
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_weights(filepath, overwrite=True, save_format=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
named layer.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.
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 user-defined 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.
Arguments:
filepath: String, 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'.
Raises: ImportError: If h5py is not available when attempting to save in HDF5 format. ValueError: For invalid/unknown format arguments.
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-- per-output weights and the bias value. These can be used to set the weights of another Dense layer:
>>> a = tf.keras.layers.Dense(1,
... kernel_initializer=tf.constant_initializer(1.)) >>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]])) >>> a.get_weights() [array([[1.], [1.], [1.]], dtype=float32), array([0.], dtype=float32)] >>> b = tf.keras.layers.Dense(1, ... kernel_initializer=tf.constant_initializer(2.)) >>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]])) >>> b.get_weights() [array([[2.], [2.], [2.]], dtype=float32), array([0.], dtype=float32)] >>> b.set_weights(a.get_weights()) >>> b.get_weights() [array([[1.], [1.], [1.]], dtype=float32), array([0.], dtype=float32)]
Arguments:
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(line_length=None, positions=None, print_fn=None)
Prints a string summary of the network.
Arguments:
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.
Raises:
ValueError: if summary()
is called before the model is built.
test_on_batch(x, y=None, sample_weight=None, reset_metrics=True, return_dict=False)
Test the model on a single batch of samples.
Arguments:
x: Input data. It could be: - A Numpy array (or array-like), or a list
of arrays (in case the model has multiple inputs). - A TensorFlow
tensor, or a list of tensors (in case the model has multiple inputs).
- A dict mapping input names to the corresponding array/tensors, if
the model has named inputs.
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. In this case you should make sure to specify
sample_weight_mode="temporal" in compile().
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: ValueError: In case of invalid user-provided arguments.
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 mathemetical 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.
Arguments:
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(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={})
.
Arguments:
**kwargs: Additional keyword arguments
to be passed to json.dumps()
.
Returns: A JSON string.
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.
Arguments:
**kwargs: Additional keyword arguments
to be passed to yaml.dump()
.
Returns: A YAML string.
Raises: ImportError: if yaml module is not found.
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.
Arguments:
x: Input data. It could be:
- A Numpy array (or array-like), or a list of arrays
(in case the model has multiple inputs).
- A TensorFlow tensor, or a list of tensors
(in case the model has multiple inputs).
- A dict mapping input names to the corresponding array/tensors,
if the model has named inputs.
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. In this case you should make sure to specify
sample_weight_mode="temporal" in compile().
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 under-represented 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: ValueError: In case of invalid user-provided arguments.
train_step(data)
The logic for one training step.
This method can be overridden to support custom training logic.
This method is called by Model.make_train_function
.
This method should contain the mathemetical 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.
Arguments:
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(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]))
Args: method: The method to wrap.
Returns: The original method wrapped such that it enters the module's name scope.
Properties
activity_regularizer
Optional regularizer function for the output of this layer.
distribute_strategy
The tf.distribute.Strategy
this model was created under.
dtype
Dtype used by the weights of the layer, set in the constructor.
dynamic
Whether the layer is dynamic (eager-only); set in the constructor.
inbound_nodes
Deprecated, do NOT use! Only for compatibility with external Keras.
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_mask
Retrieves the input mask tensor(s) of a layer.
Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.
Returns: Input mask tensor (potentially None) or list of input mask tensors.
Raises: AttributeError: if the layer is connected to more than one incoming layers.
input_shape
Retrieves the input shape(s) of a layer.
Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.
Returns: Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).
Raises: AttributeError: if the layer has no defined input_shape. RuntimeError: if called in Eager mode.
input_spec
Gets the network's input specs.
Returns:
A list of InputSpec
instances (one per input to the model)
or a single instance if the model has only one input.
layers
None
losses
Losses which are associated with this Layer
.
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.
Returns: A list of tensors.
metrics
Returns the model's metrics added using compile
, add_metric
APIs.
Note: metrics
are available only after a keras.Model
has been
trained/evaluated on actual data.
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"])
>>> [m.name for m in model.metrics]
[]
>>> x = np.random.random((2, 3))
>>> y = np.random.randint(0, 2, (2, 2))
>>> _ = model.fit(x, y, verbose=0)
>>> [m.name for m in model.metrics]
['loss', 'mae']
>>> inputs = tf.keras.layers.Input(shape=(3,))
>>> d = tf.keras.layers.Dense(2, name='out')
>>> output_1 = d(inputs)
>>> output_2 = d(inputs)
>>> model = tf.keras.models.Model(
... inputs=inputs, outputs=[output_1, output_2]) >>> model.add_metric( ... tf.reduce_sum(output_2), name='mean', aggregation='mean') >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"]) >>> _ = model.fit(x, (y, y), verbose=0) >>> [m.name for m in model.metrics] ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae', 'out_1_acc', 'mean']
metrics_names
Returns the model's display labels for all outputs.
Note: metrics_names
are available only after a keras.Model
has been
trained/evaluated on actual data.
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"])
>>> model.metrics_names
[]
>>> x = np.random.random((2, 3))
>>> y = np.random.randint(0, 2, (2, 2))
>>> _ = model.fit(x, y, verbose=0)
>>> model.metrics_names
['loss', 'mae']
>>> inputs = tf.keras.layers.Input(shape=(3,))
>>> d = tf.keras.layers.Dense(2, name='out')
>>> output_1 = d(inputs)
>>> output_2 = d(inputs)
>>> model = tf.keras.models.Model(
... inputs=inputs, outputs=[output_1, output_2]) >>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"]) >>> _ = model.fit(x, (y, y), verbose=0) >>> model.metrics_names ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae', 'out_1_acc']
name
Name of the layer (string), set in the constructor.
name_scope
Returns a tf.name_scope
instance for this class.
non_trainable_variables
None
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.
outbound_nodes
Deprecated, do NOT use! Only for compatibility with external Keras.
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.
output_mask
Retrieves the output mask tensor(s) of a layer.
Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.
Returns: Output mask tensor (potentially None) or list of output mask tensors.
Raises: AttributeError: if the layer is connected to more than one incoming layers.
output_shape
Retrieves the output shape(s) of a layer.
Only applicable if the layer has one output, or if all outputs have the same shape.
Returns: Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).
Raises: AttributeError: if the layer has no defined output shape. RuntimeError: if called in Eager mode.
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.
Returns: Boolean, whether the model should run eagerly.
state_updates
Returns the updates
from all layers that are stateful.
This is useful for separating training updates and state updates, e.g. when we need to update a layer's internal state during prediction.
Returns: A list of update ops.
stateful
None
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.
trainable
None
trainable_variables
Sequence of trainable variables owned by this module and its submodules.
Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.
Returns: A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).
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.
updates
None
variables
Returns the list of all layer variables/weights.
Alias of self.weights
.
Returns: A list of variables.
weights
Returns the list of all layer variables/weights.
Returns: A list of variables.