View source on GitHub
|
Sequential groups a linear stack of layers into a Model.
Inherits From: Model, Layer, Operation
tf.keras.Sequential(
layers=None, trainable=True, name=None
)
Used in the notebooks
| Used in the guide | Used in the tutorials |
|---|---|
Examples:
model = keras.Sequential()
model.add(keras.Input(shape=(16,)))
model.add(keras.layers.Dense(8))
# Note that you can also omit the initial `Input`.
# In that case the model doesn't have any weights until the first call
# to a training/evaluation method (since it isn't yet built):
model = keras.Sequential()
model.add(keras.layers.Dense(8))
model.add(keras.layers.Dense(4))
# model.weights not created yet
# Whereas if you specify an `Input`, the model gets built
# continuously as you are adding layers:
model = keras.Sequential()
model.add(keras.Input(shape=(16,)))
model.add(keras.layers.Dense(8))
len(model.weights) # Returns "2"
# When using the delayed-build pattern (no input shape specified), you can
# choose to manually build your model by calling
# `build(batch_input_shape)`:
model = keras.Sequential()
model.add(keras.layers.Dense(8))
model.add(keras.layers.Dense(4))
model.build((None, 16))
len(model.weights) # Returns "4"
# Note that when using the delayed-build pattern (no input shape specified),
# the model gets built the first time you call `fit`, `eval`, or `predict`,
# or the first time you call the model on some input data.
model = keras.Sequential()
model.add(keras.layers.Dense(8))
model.add(keras.layers.Dense(1))
model.compile(optimizer='sgd', loss='mse')
# This builds the model for the first time:
model.fit(x, y, batch_size=32, epochs=10)
Methods
add
add(
layer, rebuild=True
)
Adds a layer instance on top of the layer stack.
| Args | |
|---|---|
layer
|
layer instance. |
compile
compile(
optimizer='rmsprop',
loss=None,
loss_weights=None,
metrics=None,
weighted_metrics=None,
run_eagerly=False,
steps_per_execution=1,
jit_compile='auto',
auto_scale_loss=True
)
Configures the model for training.
Example:
model.compile(
optimizer=keras.optimizers.Adam(learning_rate=1e-3),
loss=keras.losses.BinaryCrossentropy(),
metrics=[
keras.metrics.BinaryAccuracy(),
keras.metrics.FalseNegatives(),
],
)
| Args | |
|---|---|
optimizer
|
String (name of optimizer) or optimizer instance. See
keras.optimizers.
|
loss
|
Loss function. May be a string (name of loss function), or
a keras.losses.Loss instance. See 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.
|
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.
|
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 keras.metrics.Metric
instance. See keras.metrics. Typically you will use
metrics=['accuracy']. A function is any callable with the
signature result = fn(y_true, _pred). To specify different
metrics for different outputs of a multi-output model, you could
also pass a dictionary, such as
metrics={'a':'accuracy', '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
keras.metrics.BinaryAccuracy,
keras.metrics.CategoricalAccuracy,
keras.metrics.SparseCategoricalAccuracy based on the
shapes of the targets and of the model output. A similar
conversion is done 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.
|
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 forward pass
will never be compiled. It is recommended to leave this
as False when training (for best performance),
and to set it to True when debugging.
|
steps_per_execution
|
Int. The number of batches to run
during each a single compiled function call. Running multiple
batches inside a single compiled function call can
greatly improve performance on TPUs or small models with a large
Python overhead. At most, one full epoch will be run each
execution. If a number larger than the size of the epoch is
passed, the execution will be truncated to the size of the
epoch. Note that if steps_per_execution is set to N,
Callback.on_batch_begin and Callback.on_batch_end methods
will only be called every N batches (i.e. before/after
each compiled function execution).
Not supported with the PyTorch backend.
|
jit_compile
|
Bool or "auto". Whether to use XLA compilation when
compiling a model. For jax and tensorflow backends,
jit_compile="auto" enables XLA compilation if the model
supports it, and disabled otherwise.
For torch backend, "auto" will default to eager
execution and jit_compile=True will run with torch.compile
with the "inductor" backend.
|
auto_scale_loss
|
Bool. If True and the model dtype policy is
"mixed_float16", the passed optimizer will be automatically
wrapped in a LossScaleOptimizer, which will dynamically
scale the loss to prevent underflow.
|
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. |
compiled_loss
compiled_loss(
y, y_pred, sample_weight=None, regularization_losses=None
)
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(Model):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.loss_tracker = metrics.Mean(name='loss')
def compute_loss(self, x, y, y_pred, sample_weight):
loss = ops.means((y_pred - y) ** 2)
loss += ops.sum(self.losses)
self.loss_tracker.update_state(loss)
return loss
def reset_metrics(self):
self.loss_tracker.reset_state()
@property
def metrics(self):
return [self.loss_tracker]
inputs = layers.Input(shape=(10,), name='my_input')
outputs = layers.Dense(10)(inputs)
model = MyModel(inputs, outputs)
model.add_loss(ops.sum(outputs))
optimizer = SGD()
model.compile(optimizer, loss='mse', steps_per_execution=10)
dataset = ...
model.fit(dataset, epochs=2, steps_per_epoch=10)
print(f"Custom loss: {model.loss_tracker.result()}")
| 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 scalar tensor, or None if no loss results
(which is the case when called by Model.test_step).
|
compute_metrics
compute_metrics(
x, y, y_pred, sample_weight=None
)
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(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().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['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
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}.
|
evaluate
evaluate(
x=None,
y=None,
batch_size=None,
verbose='auto',
sample_weight=None,
steps=None,
callbacks=None,
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 backend-native tensor(s).
If x is a tf.data.Dataset or keras.utils.PyDataset
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.PyDataset 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.
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, evaluation will run until the dataset
is exhausted.
|
callbacks
|
List of keras.callbacks.Callback instances.
List of callbacks to apply during evaluation.
|
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.
|
export
export(
filepath, format='tf_saved_model'
)
Create a TF SavedModel artifact for inference.
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
)
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 backend-native tensor(s).
If x is a dataset, generator,
or keras.utils.PyDataset 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.PyDataset
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.
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".
|
callbacks
|
List of keras.callbacks.Callback instances.
List of callbacks to apply during training.
See keras.callbacks. Note
keras.callbacks.ProgbarLogger and
keras.callbacks.History callbacks are created
automatically and need not be passed to model.fit().
keras.callbacks.ProgbarLogger is created
or not based on the verbose argument in model.fit().
|
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.PyDataset instance.
If both validation_data and validation_split are provided,
validation_data will override validation_split.
|
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.
It could be:
(x_val, y_val) of NumPy arrays or tensors.(x_val, y_val, val_sample_weights) of NumPy
arrays.tf.data.Dataset.keras.utils.PyDataset returning
(inputs, targets) or (inputs, targets, sample_weights).
|
shuffle
|
Boolean, whether to shuffle the training data
before each epoch. This argument is
ignored when x is a generator or a tf.data.Dataset.
|
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.PyDataset 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
backend-native 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.
|
validation_steps
|
Only relevant if validation_data is provided.
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 or keras.utils.PyDataset
instances (since they generate batches).
|
validation_freq
|
Only relevant if validation data is provided.
Specifies how many training epochs to run
before a new validation run is performed,
e.g. validation_freq=2 runs validation every 2 epochs.
|
Unpacking behavior for iterator-like inputs:
A common pattern is to pass an iterator like object such as a
tf.data.Dataset or a keras.utils.PyDataset to fit(),
which will in fact yield not only features (x)
but optionally targets (y) and sample weights (sample_weight).
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.
| 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).
|
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_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_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}.
|
load_weights
load_weights(
filepath, skip_mismatch=False, **kwargs
)
Load weights from a file saved via save_weights().
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.
| Args | |
|---|---|
filepath
|
String, path to the weights file to load.
It can either be a .weights.h5 file
or a legacy .h5 weights file.
|
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. |
loss
loss(
y, y_pred, sample_weight=None
)
make_predict_function
make_predict_function(
force=False
)
make_test_function
make_test_function(
force=False
)
make_train_function
make_train_function(
force=False
)
pop
pop(
rebuild=True
)
Removes the last layer in the model.
predict
predict(
x, batch_size=None, verbose='auto', steps=None, callbacks=None
)
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
BatchNormalization that behave differently during
inference.
| 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.PyDataset
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. 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.
|
| Returns | |
|---|---|
| NumPy array(s) of predictions. |
predict_on_batch
predict_on_batch(
x
)
Returns predictions for a single batch of samples.
| Args | |
|---|---|
x
|
Input data. It must be array-like. |
| Returns | |
|---|---|
| NumPy array(s) of predictions. |
predict_step
predict_step(
data
)
reset_metrics
reset_metrics()
save
save(
filepath, overwrite=True, **kwargs
)
Saves a model as a .keras file.
| Args | |
|---|---|
filepath
|
str or pathlib.Path object. Path where to save
the model. Must end in .keras.
|
overwrite
|
Whether we should overwrite any existing model at the target location, or instead ask the user via an interactive prompt. |
save_format
|
The save_format argument is deprecated in Keras 3.
Format to use, as a string. Only the "keras" format is
supported at this time.
|
Example:
model = keras.Sequential(
[
keras.layers.Dense(5, input_shape=(3,)),
keras.layers.Softmax(),
],
)
model.save("model.keras")
loaded_model = keras.saving.load_model("model.keras")
x = keras.random.uniform((10, 3))
assert np.allclose(model.predict(x), loaded_model.predict(x))
Note that model.save() is an alias for keras.saving.save_model().
The saved .keras file contains:
- The model's configuration (architecture)
- The model's weights
- The model's optimizer's state (if any)
Thus models can be reinstantiated in the exact same state.
save_weights
save_weights(
filepath, overwrite=True
)
Saves all layer weights to a .weights.h5 file.
| Args | |
|---|---|
filepath
|
str or pathlib.Path object.
Path where to save the model. Must end in .weights.h5.
|
overwrite
|
Whether we should overwrite any existing model at the target location, or instead ask the user via an interactive prompt. |
stateless_compute_loss
stateless_compute_loss(
trainable_variables,
non_trainable_variables,
metrics_variables,
x=None,
y=None,
y_pred=None,
sample_weight=None
)
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.
|
symbolic_call
symbolic_call(
*args, **kwargs
)
test_on_batch
test_on_batch(
x, y=None, sample_weight=None, return_dict=False
)
Test the model on a single batch of samples.
| Args | |
|---|---|
x
|
Input data. Must be array-like. |
y
|
Target data. Must be array-like. |
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.
|
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 | |
|---|---|
A scalar loss value (when no metrics and return_dict=False),
a list of loss and metric values
(if there are metrics and return_dict=False), or a dict of
metric and loss values (if return_dict=True).
|
test_step
test_step(
data
)
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. |
train_on_batch
train_on_batch(
x, y=None, sample_weight=None, class_weight=None, return_dict=False
)
Runs a single gradient update on a single batch of data.
| Args | |
|---|---|
x
|
Input data. Must be array-like. |
y
|
Target data. Must be array-like. |
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.
|
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 | |
|---|---|
A scalar loss value (when no metrics and return_dict=False),
a list of loss and metric values
(if there are metrics and return_dict=False), or a dict of
metric and loss values (if return_dict=True).
|
train_step
train_step(
data
)