View source on GitHub
|
Creates a NAS-FPN model.
tfm.vision.decoders.NASFPN(
input_specs: Mapping[str, tf.TensorShape],
min_level: int = 3,
max_level: int = 7,
block_specs: Optional[List[BlockSpec]] = None,
num_filters: int = 256,
num_repeats: int = 5,
use_separable_conv: bool = False,
activation: str = 'relu',
use_sync_bn: bool = False,
norm_momentum: float = 0.99,
norm_epsilon: float = 0.001,
kernel_initializer: str = 'VarianceScaling',
kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
**kwargs
)
This implements the paper: Golnaz Ghiasi, Tsung-Yi Lin, Ruoming Pang, Quoc V. Le. NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection. (https://arxiv.org/abs/1904.07392)
Args | |
|---|---|
input_specs
|
A dict of input specifications. A dictionary consists of
{level: TensorShape} from a backbone.
|
min_level
|
An int of minimum level in FPN output feature maps.
|
max_level
|
An int of maximum level in FPN output feature maps.
|
block_specs
|
a list of BlockSpec objects that specifies the NAS-FPN network topology. By default, the previously discovered architecture is used. |
num_filters
|
An int number of filters in FPN layers.
|
num_repeats
|
number of repeats for feature pyramid network. |
use_separable_conv
|
A bool. If True use separable convolution for
convolution in FPN layers.
|
activation
|
A str name of the activation function.
|
use_sync_bn
|
A bool. If True, use synchronized batch normalization.
|
norm_momentum
|
A float of normalization momentum for the moving average.
|
norm_epsilon
|
A float added to variance to avoid dividing by zero.
|
kernel_initializer
|
A str name of kernel_initializer for convolutional
layers.
|
kernel_regularizer
|
A tf.keras.regularizers.Regularizer object for
Conv2D. Default is None.
|
bias_regularizer
|
A tf.keras.regularizers.Regularizer object for Conv2D.
|
**kwargs
|
Additional keyword arguments to be passed. |
Attributes | ||
|---|---|---|
activity_regularizer
|
Optional regularizer function for the output of this layer. | |
autotune_steps_per_execution
|
Settable property to enable tuning for steps_per_execution | |
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_reduction_method
|
The method employed to reduce per-replica values during training.
Unless specified, the value "auto" will be assumed, indicating that
the reduction strategy should be chosen based on the current
running environment.
See |
|
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 (eager-only); set in the constructor. | |
input
|
Retrieves the input tensor(s) of a layer.
Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer. |
|
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 |
|
jit_compile
|
Specify whether to compile the model with XLA.
XLA is an optimizing compiler
for machine learning. For more information on supported operations please refer to the XLA documentation. Also refer to known XLA issues for more details. |
|
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
|
Return metrics added using compile() or add_metric().
|
|
metrics_names
|
Returns the model's display labels for all outputs.
|
|
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 |
|
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. |
|
output_specs
|
A dict of {level: TensorShape} pairs for the model output. | |
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. |
|
steps_per_execution
|
Settable steps_per_execution variable. Requires a compiled model.
</td>
</tr><tr>
<td>supports_masking<a id="supports_masking"></a>
</td>
<td>
Whether this layer supports computing a mask usingcompute_mask.
</td>
</tr><tr>
<td>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
The same code works in distributed training: the input to add_loss()
is treated like a regularization loss and averaged across replicas
by the training loop (both built-in Model.fit() and compliant custom
training loops).
The add_loss method can also be called directly on a Functional Model
during construction. In this case, any loss Tensors passed to this Model
must be symbolic and be able to be traced back to the model's Inputs.
These losses become part of the model's topology and are tracked in
get_config.
Example:
inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Activity regularization.
model.add_loss(tf.abs(tf.reduce_mean(x)))
If this is not the case for your loss (if, for example, your loss
references a Variable of one of the model's layers), you can wrap your
loss in a zero-argument lambda. These losses are not tracked as part of
the model's topology since they can't be serialized.
Example:
inputs = tf.keras.Input(shape=(10,))
d = tf.keras.layers.Dense(10)
x = d(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Weight regularization.
model.add_loss(lambda: tf.reduce_mean(d.kernel))
| Args | |
|---|---|
losses
|
Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. |
**kwargs
|
Used for backwards compatibility only. |
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. |
build_from_config
build_from_config(
config
)
Builds the layer's states with the supplied config dict.
By default, this method calls the build(config["input_shape"]) method,
which creates weights based on the layer's input shape in the supplied
config. If your config contains other information needed to load the
layer's state, you should override this method.
| Args | |
|---|---|
config
|
Dict containing the input shape associated with this layer. |
call
call(
inputs, training=None, mask=None
)
Calls the model on new inputs and returns the outputs as tensors.
In this case call() just reapplies
all ops in the graph to the new inputs
(e.g. build a new computational graph from the provided inputs).
| Args | |
|---|---|
inputs
|
Input tensor, or dict/list/tuple of input tensors. |
training
|
Boolean or boolean scalar tensor, indicating whether to
run the Network in training mode or inference mode.
|
mask
|
A mask or list of masks. A mask can be either a boolean tensor or None (no mask). For more details, check the guide here. |
| Returns | |
|---|---|
| A tensor if there is a single output, or a list of tensors if there are more than one outputs. |
compile
compile(
optimizer='rmsprop',
loss=None,
metrics=None,
loss_weights=None,
weighted_metrics=None,
run_eagerly=None,
steps_per_execution=None,
jit_compile=None,
pss_evaluation_shards=0,
**kwargs
)
Configures the model for training.
Example:
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
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, .. dN-1)).
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, .. dN-1) i.e. 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, 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
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 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 shapes
of the targets and of the model output. We do a similar
conversion for the strings 'crossentropy' and 'ce' as well.
The metrics passed here are evaluated without sample weighting; if
you would like sample weighting to apply, you can specify your
metrics via the weighted_metrics argument instead.
|
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. 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. Defaults to
False.
|
steps_per_execution
|
Int or 'auto'. The number of batches to
run during each tf.function call. If set to "auto", keras will
automatically tune steps_per_execution during runtime. Running
multiple batches inside a single tf.function call can greatly
improve performance on TPUs, when used with distributed strategies
such as ParameterServerStrategy, or with 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). Defaults to 1.
|
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.
Note that jit_compile=True
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.
|
pss_evaluation_shards
|
Integer or 'auto'. Used for
tf.distribute.ParameterServerStrategy training only. This arg
sets the number of shards to split the dataset into, to enable an
exact visitation guarantee for evaluation, meaning the model will
be applied to each dataset element exactly once, even if workers
fail. The dataset must be sharded to ensure separate workers do
not process the same data. The number of shards should be at least
the number of workers for good performance. A value of 'auto'
turns on exact evaluation and uses a heuristic for the number of
shards based on the number of workers. 0, meaning no
visitation guarantee is provided. NOTE: Custom implementations of
Model.test_step will be ignored when doing exact evaluation.
Defaults to 0.
|
**kwargs
|
Arguments supported for backwards compatibility only. |
compile_from_config
compile_from_config(
config
)
Compiles the model with the information given in config.
This method uses the information in the config (optimizer, loss, metrics, etc.) to compile the model.
| Args | |
|---|---|
config
|
Dict containing information for compiling the model. |
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 tf.TensorShape,
or structure of shape tuples / tf.TensorShape instances
(one per output tensor of the layer).
Shape tuples can include None for free dimensions,
instead of an integer.
|
| Returns | |
|---|---|
A tf.TensorShape instance
or structure of tf.TensorShape instances.
|
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(
x=None,
y=None,
batch_size=None,
verbose='auto',
sample_weight=None,
steps=None,
callbacks=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
return_dict=False,
**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" becomes 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). Defaults to 'auto'.
|
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
process-based threading. If unspecified, workers will default to
1.
|
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-pickleable 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 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 | |
|---|---|
RuntimeError
|
If model.evaluate is wrapped in a tf.function.
|
export
export(
filepath
)
Create a SavedModel artifact for inference (e.g. via TF-Serving).
This method lets you export a model to a lightweight SavedModel artifact
that contains the model's forward pass only (its call() method)
and can be served via e.g. TF-Serving. The forward pass is registered
under the name serve() (see example below).
The original code of the model (including any custom layers you may have used) is no longer necessary to reload the artifact -- it is entirely standalone.
| Args | |
|---|---|
filepath
|
str or pathlib.Path object. Path where to save
the artifact.
|
Example:
# Create the artifact
model.export("path/to/location")
# Later, in a different process / environment...
reloaded_artifact = tf.saved_model.load("path/to/location")
predictions = reloaded_artifact.serve(input_data)
If you would like to customize your serving endpoints, you can
use the lower-level keras.export.ExportArchive class. The export()
method relies on ExportArchive internally.
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 (dataset iterations).
| 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' becomes 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). Defaults to 'auto'.
|
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 batch-level calls are currently unsupported with
tf.distribute.experimental.ParameterServerStrategy, and users
are advised to implement epoch-level 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 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. When class_weight is specified
and targets have a rank of 2 or greater, either y must be
one-hot encoded, or an explicit final dimension of 1 must
be included for sparse class labels.
|
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.
Note that sample weighting does not apply to metrics specified
via the metrics argument in compile(). To apply sample
weighting to your metrics, you can specify them via the
weighted_metrics in compile() instead.
|
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 process-based threading. If unspecified, workers
will default to 1.
|
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-pickleable 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
|
|
ValueError
|
In case of mismatch between the provided input data and what the model expects or when the input data is empty. |
from_config
@classmethodfrom_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_build_config
get_build_config()
Returns a dictionary with the layer's input shape.
This method returns a config dict that can be used by
build_from_config(config) to create all states (e.g. Variables and
Lookup tables) needed by the layer.
By default, the config only contains the input shape that the layer was built with. If you're writing a custom layer that creates state in an unusual way, you should override this method to make sure this state is already created when Keras attempts to load its value upon model loading.
| Returns | |
|---|---|
| A dict containing the input shape associated with the layer. |
get_compile_config
get_compile_config()
Returns a serialized config with information for compiling the model.
This method returns a config dictionary containing all the information (optimizer, loss, metrics, etc.) with which the model was compiled.
| Returns | |
|---|---|
| A dict containing information for compiling the model. |
get_config
get_config() -> Mapping[str, Any]
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
will return config dict for init parameters if they are basic types.
Raises NotImplementedError when in cases where a custom
get_config() implementation is required for the subclassed model.
| 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 (bottom-up).
| Args | |
|---|---|
name
|
String, name of layer. |
index
|
Integer, index of layer. |
| Returns | |
|---|---|
| A layer instance. |
get_metrics_result
get_metrics_result()
Returns the model's metrics values as a dict.
If any of the metric result is a dict (containing multiple metrics), each of them gets added to the top level returned dict of this method.
| Returns | |
|---|---|
A dict containing values of the metrics listed in self.metrics.
|
|
Example
|
{'loss': 0.2, 'accuracy': 0.7}.
|
get_weight_paths
get_weight_paths()
Retrieve all the variables and their paths for the model.
The variable path (string) is a stable key to identify a tf.Variable
instance owned by the model. It can be used to specify variable-specific
configurations (e.g. DTensor, quantization) from a global view.
This method returns a dict with weight object paths as keys
and the corresponding tf.Variable instances as values.
Note that if the model is a subclassed model and the weights haven't been initialized, an empty dict will be returned.
| Returns | |
|---|---|
A dict where keys are variable paths and values are tf.Variable
instances.
|
Example:
class SubclassModel(tf.keras.Model):
def __init__(self, name=None):
super().__init__(name=name)
self.d1 = tf.keras.layers.Dense(10)
self.d2 = tf.keras.layers.Dense(20)
def call(self, inputs):
x = self.d1(inputs)
return self.d2(x)
model = SubclassModel()
model(tf.zeros((10, 10)))
weight_paths = model.get_weight_paths()
# weight_paths:
# {
# 'd1.kernel': model.d1.kernel,
# 'd1.bias': model.d1.bias,
# 'd2.kernel': model.d2.kernel,
# 'd2.bias': model.d2.bias,
# }
# Functional model
inputs = tf.keras.Input((10,), batch_size=10)
x = tf.keras.layers.Dense(20, name='d1')(inputs)
output = tf.keras.layers.Dense(30, name='d2')(x)
model = tf.keras.Model(inputs, output)
d1 = model.layers[1]
d2 = model.layers[2]
weight_paths = model.get_weight_paths()
# weight_paths:
# {
# 'd1.kernel': d1.kernel,
# 'd1.bias': d1.bias,
# 'd2.kernel': d2.kernel,
# 'd2.bias': d2.bias,
# }
get_weights
get_weights()
Retrieves the weights of the model.
| Returns | |
|---|---|
| A flat list of Numpy arrays. |
load_own_variables
load_own_variables(
store
)
Loads the state of the layer.
You can override this method to take full control of how the state of
the layer is loaded upon calling keras.models.load_model().
| Args | |
|---|---|
store
|
Dict from which the state of the model will be loaded. |
load_weights
load_weights(
filepath, skip_mismatch=False, by_name=False, options=None
)
Loads all layer weights from a saved files.
The saved file could be a SavedModel file, a .keras file (v3 saving
format), or a file created via model.save_weights().
By default, 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.
Partial weight loading
If you have modified your model, for instance by adding a new layer
(with weights) or by changing the shape of the weights of a layer,
you can choose to ignore errors and continue loading
by setting skip_mismatch=True. In this case any layer with
mismatching weights will be skipped. A warning will be displayed
for each skipped layer.
Weight loading by name
If your weights are saved as a .h5 file created
via model.save_weights(), you can use the argument by_name=True.
In this case, 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.
Note that only topological loading (by_name=False) is supported when
loading weights from the .keras v3 format or from the TensorFlow
SavedModel format.
| 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 or a .keras file (v3 saving format) saved
via model.save().
|
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 weights. |
by_name
|
Boolean, whether to load weights by name or by topological
order. Only topological loading is supported for weight files in
the .keras v3 format or in the TensorFlow SavedModel format.
|
options
|
Optional tf.train.CheckpointOptions object that specifies
options for loading weights (only valid for a SavedModel file).
|
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(
x,
batch_size=None,
verbose='auto',
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 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" becomes 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). Defaults to 'auto'.
|
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.
|
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-pickleable 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 | |
|---|---|
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 Tensors.
|
| 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, save_format=None, **kwargs
)
Saves a model as a TensorFlow SavedModel or HDF5 file.
See the Serialization and Saving guide for details.
| Args | |
|---|---|
model
|
Keras model instance to be saved. |
filepath
|
str or pathlib.Path object. Path where to save the
model.
|
overwrite
|
Whether we should overwrite any existing model at the target location, or instead ask the user via an interactive prompt. |
save_format
|
Either "keras", "tf", "h5",
indicating whether to save the model
in the native Keras format (.keras),
in the TensorFlow SavedModel format
(referred to as "SavedModel" below),
or in the legacy HDF5 format (.h5).
Defaults to "tf" in TF 2.X, and "h5" in TF 1.X.
|
SavedModel format arguments:
include_optimizer: Only applied to SavedModel and legacy HDF5
formats. If False, do not save the optimizer state.
Defaults to True.
signatures: Only applies to SavedModel format. Signatures to save
with the SavedModel. See the signatures argument in
tf.saved_model.save for details.
options: Only applies to SavedModel format.
tf.saved_model.SaveOptions object that specifies SavedModel
saving options.
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:
model = tf.keras.Sequential([
tf.keras.layers.Dense(5, input_shape=(3,)),
tf.keras.layers.Softmax()])
model.save("model.keras")
loaded_model = tf.keras.models.load_model("model.keras")
x = tf.random.uniform((10, 3))
assert np.allclose(model.predict(x), loaded_model.predict(x))
Note that model.save() is an alias for tf.keras.models.save_model().
save_own_variables
save_own_variables(
store
)
Saves the state of the layer.
You can override this method to take full control of how the state of
the layer is saved upon calling model.save().
| Args | |
|---|---|
store
|
Dict where the state of the model will be saved. |
save_spec
save_spec(
dynamic_batch=True
)
Returns the tf.TensorSpec of call args 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
groupnamedlayer.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 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.
| 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 becomes 'tf'. Defaults to None.
|
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,
layer_range=None
)
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, becomes
[0.3, 0.6, 0.70, 1.]. Defaults to None.
|
print_fn
|
Print function to use. By default, prints to stdout.
If stdout doesn't work in your environment, change 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.
Defaults to False.
|
show_trainable
|
Whether to show if a layer is trainable.
Defaults to False.
|
layer_range
|
a list or tuple of 2 strings,
which is the starting layer name and ending layer name
(both inclusive) indicating the range of layers to be printed
in summary. It also accepts regex patterns instead of exact
name. In such case, start predicate will be the first element
it matches to layer_range[0] and the end predicate will be
the last element it matches to layer_range[1].
By default None which considers all layers of model.
|
| 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 Tensors.
|
| 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 under-represented
class. When class_weight is specified and targets have a rank of
2 or greater, either y must be one-hot encoded, or an explicit
final dimension of 1 must be included for sparse class labels.
|
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 happens 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 Tensors.
|
| 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}.
|
__call__
__call__(
*args, **kwargs
)