TensorFlow 2 version
|
View source on GitHub
|
Linear Model for regression and classification problems.
Inherits From: Model
tf.keras.experimental.LinearModel(
units=1, activation=None, use_bias=True, kernel_initializer='glorot_uniform',
bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None,
**kwargs
)
This model approximates the following function:
$$y = \beta + \sum_{i=1}^{N} w_{i} * x_{i}$$
where
$$\beta$$
is the bias and
$$w_{i}$$
is the weight for each feature.
Example:
model = LinearModel()
model.compile(optimizer='sgd', loss='mse')
model.fit(x, y, epochs)
This model accepts sparse float inputs as well:
Example:
model = LinearModel()
opt = tf.keras.optimizers.Adam()
loss_fn = tf.keras.losses.MeanSquaredError()
with tf.GradientTape() as tape:
output = model(sparse_input)
loss = tf.reduce_mean(loss_fn(target, output))
grads = tape.gradient(loss, model.weights)
opt.apply_gradients(zip(grads, model.weights))
Args | |
|---|---|
units
|
Positive integer, output dimension without the batch size. |
activation
|
Activation function to use. If you don't specify anything, no activation is applied. |
use_bias
|
whether to calculate the bias/intercept for this model. If set to False, no bias/intercept will be used in calculations, e.g., the data is already centered. |
kernel_initializer
|
Initializer for the kernel weights matrices.
|
bias_initializer
|
Initializer for the bias vector. |
kernel_regularizer
|
regularizer for kernel vectors. |
bias_regularizer
|
regularizer for bias vector. |
**kwargs
|
The keyword arguments that are passed on to BaseLayer.init. |
Attributes | |
|---|---|
layers
|
|
metrics_names
|
Returns the model's display labels for all outputs. |
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. |
sample_weights
|
|
state_updates
|
Returns the updates from all layers that are stateful.
This is useful for separating training updates and state updates, e.g. when we need to update a layer's internal state during prediction. |
stateful
|
|
Methods
compile
compile(
optimizer='rmsprop', loss=None, metrics=None, loss_weights=None,
sample_weight_mode=None, weighted_metrics=None, target_tensors=None,
distribute=None, **kwargs
)
Configures the model for training.
| Arguments | |
|---|---|
optimizer
|
String (name of optimizer) or optimizer instance.
See tf.keras.optimizers.
|
loss
|
String (name of objective function), objective function or
tf.losses.Loss instance. See tf.losses. If the model has
multiple outputs, you can use a different loss on each output by
passing a dictionary or a list of losses. The loss value that will
be minimized by the model will then be the sum of all individual
losses.
|
metrics
|
List of metrics to be evaluated by the model during training
and testing. Typically you will use metrics=['accuracy'].
To specify different metrics for different outputs of a
multi-output model, you could also pass a dictionary, such as
metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}.
You can also pass a list (len = len(outputs)) of lists of metrics
such as metrics=[['accuracy'], ['accuracy', 'mse']] or
metrics=['accuracy', ['accuracy', 'mse']].
|
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 tensor, it is expected to map
output names (strings) to scalar coefficients.
|
sample_weight_mode
|
If you need to do timestep-wise
sample weighting (2D weights), set this to "temporal".
None defaults to sample-wise weights (1D).
If the model has multiple outputs, you can use a different
sample_weight_mode on each output by passing a
dictionary or a list of modes.
|
weighted_metrics
|
List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing. |
target_tensors
|
By default, Keras will create placeholders for the
model's target, which will be fed with the target data during
training. If instead you would like to use your own
target tensors (in turn, Keras will not expect external
Numpy data for these targets at training time), you
can specify them via the target_tensors argument. It can be
a single tensor (for a single-output model), a list of tensors,
or a dict mapping output names to target tensors.
|
distribute
|
NOT SUPPORTED IN TF 2.0, please create and compile the model under distribution strategy scope instead of passing it to compile. |
**kwargs
|
Any additional arguments. |
| Raises | |
|---|---|
ValueError
|
In case of invalid arguments for
optimizer, loss, metrics or sample_weight_mode.
|
evaluate
evaluate(
x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None,
callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False
)
Returns the loss value & metrics values for the model in test mode.
Computation is done in batches.
| Arguments | |
|---|---|
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 gradient update.
If unspecified, batch_size will default to 32.
Do not specify the batch_size is your data is in the
form of symbolic tensors, dataset,
generators, or keras.utils.Sequence instances (since they generate
batches).
|
verbose
|
0 or 1. Verbosity mode. 0 = silent, 1 = progress bar. |
sample_weight
|
Optional Numpy array of weights for
the test samples, used for weighting the loss function.
You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples),
or in the case of temporal data,
you can pass a 2D array with shape
(samples, sequence_length),
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
sample_weight_mode="temporal" in compile(). This argument is not
supported when x is a dataset, instead pass
sample weights as the third element of x.
|
steps
|
Integer or None.
Total number of steps (batches of samples)
before declaring the evaluation round finished.
Ignored with the default value of None.
If x is a tf.data dataset and steps is
None, 'evaluate' will run until the dataset is exhausted.
This argument is not supported with array inputs.
|
callbacks
|
List of keras.callbacks.Callback instances.
List of callbacks to apply during evaluation.
See callbacks.
|
max_queue_size
|
Integer. Used for generator or keras.utils.Sequence
input only. Maximum size for the generator queue.
If unspecified, max_queue_size will default to 10.
|
workers
|
Integer. Used for generator or keras.utils.Sequence input
only. Maximum number of processes to spin up when using
process-based threading. If unspecified, workers will default
to 1. If 0, will execute the generator on the main thread.
|
use_multiprocessing
|
Boolean. Used for generator or
keras.utils.Sequence input only. If True, use process-based
threading. If unspecified, use_multiprocessing will default to
False. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can't be passed easily to children processes.
|
| Returns | |
|---|---|
Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.
|
| Raises | |
|---|---|
ValueError
|
in case of invalid arguments. |
evaluate_generator
evaluate_generator(
generator, steps=None, callbacks=None, max_queue_size=10, workers=1,
use_multiprocessing=False, verbose=0
)
Evaluates the model on a data generator.
The generator should return the same kind of data
as accepted by test_on_batch.
| Arguments | |
|---|---|
generator
|
Generator yielding tuples (inputs, targets)
or (inputs, targets, sample_weights)
or an instance of keras.utils.Sequence
object in order to avoid duplicate data
when using multiprocessing.
|
steps
|
Total number of steps (batches of samples)
to yield from generator before stopping.
Optional for Sequence: if unspecified, will use
the len(generator) as a number of steps.
|
callbacks
|
List of keras.callbacks.Callback instances.
List of callbacks to apply during evaluation.
See callbacks.
|
max_queue_size
|
maximum size for the generator queue |
workers
|
Integer. Maximum number of processes to spin up
when using process-based threading.
If unspecified, workers will default to 1. If 0, will
execute the generator on the main thread.
|
use_multiprocessing
|
Boolean.
If True, use process-based threading.
If unspecified, use_multiprocessing will default to False.
Note that because this implementation relies on multiprocessing,
you should not pass non-picklable arguments to the generator
as they can't be passed easily to children processes.
|
verbose
|
Verbosity mode, 0 or 1. |
| Returns | |
|---|---|
Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.
|
| Raises | |
|---|---|
ValueError
|
in case of invalid arguments. |
| Raises | |
|---|---|
ValueError
|
In case the generator yields data in an invalid format. |
fit
fit(
x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None,
validation_split=0.0, validation_data=None, shuffle=True, class_weight=None,
sample_weight=None, initial_epoch=0, steps_per_epoch=None,
validation_steps=None, validation_freq=1, max_queue_size=10, workers=1,
use_multiprocessing=False, **kwargs
)
Trains the model for a fixed number of epochs (iterations on a dataset).
| Arguments | |
|---|---|
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 symbolic tensors, datasets,
generators, or keras.utils.Sequence instances (since they generate
batches).
|
epochs
|
Integer. Number of epochs to train the model.
An epoch is an iteration over the entire x and y
data provided.
Note that in conjunction with initial_epoch,
epochs is to be understood as "final epoch".
The model is not trained for a number of iterations
given by epochs, but merely until the epoch
of index epochs is reached.
|
verbose
|
0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (eg, in a production environment). |
callbacks
|
List of keras.callbacks.Callback instances.
List of callbacks to apply during training.
See tf.keras.callbacks.
|
validation_split
|
Float between 0 and 1.
Fraction of the training data to be used as validation data.
The model will set apart this fraction of the training data,
will not train on it, and will evaluate
the loss and any model metrics
on this data at the end of each epoch.
The validation data is selected from the last samples
in the x and y data provided, before shuffling. This argument is
not supported when x is a dataset, generator or
keras.utils.Sequence instance.
|
validation_data
|
Data on which to evaluate
the loss and any model metrics at the end of each epoch.
The model will not be trained on this data.
validation_data will override validation_split.
validation_data could be:
(x_val, y_val) of Numpy arrays or tensors(x_val, y_val, val_sample_weights) of Numpy arraysbatch_size must be provided.
For the last case, validation_steps must be provided.
|
shuffle
|
Boolean (whether to shuffle the training data
before each epoch) or str (for 'batch').
'batch' is a special option for dealing with the
limitations of HDF5 data; it shuffles in batch-sized chunks.
Has no effect when steps_per_epoch is not None.
|
class_weight
|
Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class. |
sample_weight
|
Optional Numpy array of weights for
the training samples, used for weighting the loss function
(during training only). You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples),
or in the case of temporal data,
you can pass a 2D array with shape
(samples, sequence_length),
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
sample_weight_mode="temporal" in compile(). This argument is not
supported when x is a dataset, generator, or
keras.utils.Sequence instance, instead provide the sample_weights
as the third element of x.
|
initial_epoch
|
Integer. Epoch at which to start training (useful for resuming a previous training run). |
steps_per_epoch
|
Integer or None.
Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch. When training with input tensors such as
TensorFlow data tensors, the default None is equal to
the number of samples in your dataset divided by
the batch size, or 1 if that cannot be determined. If x is a
tf.data dataset, and 'steps_per_epoch'
is None, the epoch will run until the input dataset is exhausted.
This argument is not supported with array inputs.
|
validation_steps
|
Only relevant if validation_data is provided and
is a tf.data dataset. Total number of steps (batches of
samples) to draw before stopping when performing validation
at the end of every epoch. If validation_data is a tf.data dataset
and 'validation_steps' is None, validation
will run until the validation_data dataset is exhausted.
|
validation_freq
|
Only relevant if validation data is provided. Integer
or collections_abc.Container instance (e.g. list, tuple, etc.).
If an integer, specifies how many training epochs to run before a
new validation run is performed, e.g. validation_freq=2 runs
validation every 2 epochs. If a Container, specifies the epochs on
which to run validation, e.g. validation_freq=[1, 2, 10] runs
validation at the end of the 1st, 2nd, and 10th epochs.
|
max_queue_size
|
Integer. Used for generator or keras.utils.Sequence
input only. Maximum size for the generator queue.
If unspecified, max_queue_size will default to 10.
|
workers
|
Integer. Used for generator or keras.utils.Sequence input
only. Maximum number of processes to spin up
when using process-based threading. If unspecified, workers
will default to 1. If 0, will execute the generator on the main
thread.
|
use_multiprocessing
|
Boolean. Used for generator or
keras.utils.Sequence input only. If True, use process-based
threading. If unspecified, use_multiprocessing will default to
False. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can't be passed easily to children processes.
|
**kwargs
|
Used for backwards compatibility. |
| Returns | |
|---|---|
A History object. Its History.history attribute is
a record of training loss values and metrics values
at successive epochs, as well as validation loss values
and validation metrics values (if applicable).
|
| Raises | |
|---|---|
RuntimeError
|
If the model was never compiled. |
ValueError
|
In case of mismatch between the provided input data and what the model expects. |
fit_generator
fit_generator(
generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None,
validation_data=None, validation_steps=None, validation_freq=1,
class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False,
shuffle=True, initial_epoch=0
)
Fits the model on data yielded batch-by-batch by a Python generator.
The generator is run in parallel to the model, for efficiency. For instance, this allows you to do real-time data augmentation on images on CPU in parallel to training your model on GPU.
The use of keras.utils.Sequence guarantees the ordering
and guarantees the single use of every input per epoch when
using use_multiprocessing=True.
| Arguments | |
|---|---|
generator
|
A generator or an instance of Sequence
(keras.utils.Sequence)
object in order to avoid duplicate data
when using multiprocessing.
The output of the generator must be either
|
steps_per_epoch
|
Total number of steps (batches of samples)
to yield from generator before declaring one epoch
finished and starting the next epoch. It should typically
be equal to the number of samples of your dataset
divided by the batch size.
Optional for Sequence: if unspecified, will use
the len(generator) as a number of steps.
|
epochs
|
Integer, total number of iterations on the data. |
verbose
|
Verbosity mode, 0, 1, or 2. |
callbacks
|
List of callbacks to be called during training. |
validation_data
|
This can be either
|
validation_steps
|
Only relevant if validation_data
is a generator. Total number of steps (batches of samples)
to yield from generator before stopping.
Optional for Sequence: if unspecified, will use
the len(validation_data) as a number of steps.
|
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.
|
class_weight
|
Dictionary mapping class indices to a weight for the class. |
max_queue_size
|
Integer. Maximum size for the generator queue.
If unspecified, max_queue_size will default to 10.
|
workers
|
Integer. Maximum number of processes to spin up
when using process-based threading.
If unspecified, workers will default to 1. If 0, will
execute the generator on the main thread.
|
use_multiprocessing
|
Boolean.
If True, use process-based threading.
If unspecified, use_multiprocessing will default to False.
Note that because this implementation relies on multiprocessing,
you should not pass non-picklable arguments to the generator
as they can't be passed easily to children processes.
|
shuffle
|
Boolean. Whether to shuffle the order of the batches at
the beginning of each epoch. Only used with instances
of Sequence (keras.utils.Sequence).
Has no effect when steps_per_epoch is not None.
|
initial_epoch
|
Epoch at which to start training (useful for resuming a previous training run) |
| Returns | |
|---|---|
A History object.
|
Example:
def generate_arrays_from_file(path):
while 1:
f = open(path)
for line in f:
# create numpy arrays of input data
# and labels, from each line in the file
x1, x2, y = process_line(line)
yield ({'input_1': x1, 'input_2': x2}, {'output': y})
f.close()
model.fit_generator(generate_arrays_from_file('/my_file.txt'),
steps_per_epoch=10000, epochs=10)
Raises: ValueError: In case the generator yields data in an invalid format.
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).
| Arguments | |
|---|---|
name
|
String, name of layer. |
index
|
Integer, index of layer. |
| Returns | |
|---|---|
| A layer instance. |
| Raises | |
|---|---|
ValueError
|
In case of invalid layer name or index. |
load_weights
load_weights(
filepath, by_name=False
)
Loads all layer weights, either from a TensorFlow or an HDF5 file.
predict
predict(
x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10,
workers=1, use_multiprocessing=False
)
Generates output predictions for the input samples.
Computation is done in batches.
| Arguments | |
|---|---|
x
|
Input samples. It could be:
|
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 is your data is in the
form of symbolic tensors, dataset,
generators, or keras.utils.Sequence instances (since they generate
batches).
|
verbose
|
Verbosity mode, 0 or 1. |
steps
|
Total number of steps (batches of samples)
before declaring the prediction round finished.
Ignored with the default value of None. If x is a tf.data
dataset and steps is None, predict will
run until the input dataset is exhausted.
|
callbacks
|
List of keras.callbacks.Callback instances.
List of callbacks to apply during prediction.
See callbacks.
|
max_queue_size
|
Integer. Used for generator or keras.utils.Sequence
input only. Maximum size for the generator queue.
If unspecified, max_queue_size will default to 10.
|
workers
|
Integer. Used for generator or keras.utils.Sequence input
only. Maximum number of processes to spin up when using
process-based threading. If unspecified, workers will default
to 1. If 0, will execute the generator on the main thread.
|
use_multiprocessing
|
Boolean. Used for generator or
keras.utils.Sequence input only. If True, use process-based
threading. If unspecified, use_multiprocessing will default to
False. Note that because this implementation relies on
multiprocessing, you should not pass non-picklable arguments to
the generator as they can't be passed easily to children processes.
|
| Returns | |
|---|---|
| Numpy array(s) of predictions. |
| Raises | |
|---|---|
ValueError
|
In case of mismatch between the provided input data and the model's expectations, or in case a stateful model receives a number of samples that is not a multiple of the batch size. |
predict_generator
predict_generator(
generator, steps=None, callbacks=None, max_queue_size=10, workers=1,
use_multiprocessing=False, verbose=0
)
Generates predictions for the input samples from a data generator.
The generator should return the same kind of data as accepted by
predict_on_batch.
| Arguments | |
|---|---|
generator
|
Generator yielding batches of input samples
or an instance of keras.utils.Sequence object in order to
avoid duplicate data when using multiprocessing.
|
steps
|
Total number of steps (batches of samples)
to yield from generator before stopping.
Optional for Sequence: if unspecified, will use
the len(generator) as a number of steps.
|
callbacks
|
List of keras.callbacks.Callback instances.
List of callbacks to apply during prediction.
See callbacks.
|
max_queue_size
|
Maximum size for the generator queue. |
workers
|
Integer. Maximum number of processes to spin up
when using process-based threading.
If unspecified, workers will default to 1. If 0, will
execute the generator on the main thread.
|
use_multiprocessing
|
Boolean.
If True, use process-based threading.
If unspecified, use_multiprocessing will default to False.
Note that because this implementation relies on multiprocessing,
you should not pass non-picklable arguments to the generator
as they can't be passed easily to children processes.
|
verbose
|
verbosity mode, 0 or 1. |
| Returns | |
|---|---|
| Numpy array(s) of predictions. |
| Raises | |
|---|---|
ValueError
|
In case the generator yields data in an invalid format. |
predict_on_batch
predict_on_batch(
x
)
Returns predictions for a single batch of samples.
| Arguments | |
|---|---|
x
|
Input data. It could be:
|
| Returns | |
|---|---|
| Numpy array(s) of predictions. |
| Raises | |
|---|---|
ValueError
|
In case of mismatch between given number of inputs and expectations of the model. |
reset_metrics
reset_metrics()
Resets the state of metrics.
reset_states
reset_states()
save
save(
filepath, overwrite=True, include_optimizer=True, save_format=None,
signatures=None
)
Saves the model to Tensorflow SavedModel or a single HDF5 file.
The savefile includes:
- The model architecture, allowing to re-instantiate the model.
- The model weights.
- The state of the optimizer, allowing to resume training exactly where you left off.
This allows you to save the entirety of the state of a model in a single file.
Saved models can be reinstantiated via keras.models.load_model.
The model returned by load_model
is a compiled model ready to be used (unless the saved model
was never compiled in the first place).
| Arguments | |
|---|---|
filepath: String, path to SavedModel or H5 file to save the model.
overwrite: Whether to silently overwrite any existing file at the
target location, or provide the user with a manual prompt.
include_optimizer: If True, save optimizer's state together.
save_format: Either 'tf' or 'h5', indicating whether to save the model
to Tensorflow SavedModel or HDF5. The default is currently 'h5', but
will switch to 'tf' in TensorFlow 2.0. The 'tf' option is currently
disabled (use tf.keras.experimental.export_saved_model instead).
|
|
signatures
|
Signatures to save with the SavedModel. Applicable to the 'tf'
format only. Please see the signatures argument in
tf.saved_model.save for details.
|
Example:
from keras.models import load_model
model.save('my_model.h5') # creates a HDF5 file 'my_model.h5'
del model # deletes the existing model
# returns a compiled model
# identical to the previous one
model = load_model('my_model.h5')
save_weights
save_weights(
filepath, overwrite=True, save_format=None
)
Saves all layer weights.
Either saves in HDF5 or in TensorFlow format based on the save_format
argument.
When saving in HDF5 format, the weight file has:
layer_names(attribute), a list of strings (ordered names of model layers).- For every layer, a
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.
| Arguments | |
|---|---|
filepath
|
String, path to the file to save the weights to. When saving in TensorFlow format, this is the prefix used for checkpoint files (multiple files are generated). Note that the '.h5' suffix causes weights to be saved in HDF5 format. |
overwrite
|
Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt. |
save_format
|
Either 'tf' or 'h5'. A filepath ending in '.h5' or
'.keras' will default to HDF5 if save_format is None. Otherwise
None defaults to 'tf'.
|
| Raises | |
|---|---|
ImportError
|
If h5py is not available when attempting to save in HDF5 format. |
ValueError
|
For invalid/unknown format arguments. |
summary
summary(
line_length=None, positions=None, print_fn=None
)
Prints a string summary of the network.
| Arguments | |
|---|---|
line_length
|
Total length of printed lines (e.g. set this to adapt the display to different terminal window sizes). |
positions
|
Relative or absolute positions of log elements
in each line. If not provided,
defaults to [.33, .55, .67, 1.].
|
print_fn
|
Print function to use. Defaults to print.
It will be called on each line of the summary.
You can set it to a custom function
in order to capture the string summary.
|
| Raises | |
|---|---|
ValueError
|
if summary() is called before the model is built.
|
test_on_batch
test_on_batch(
x, y=None, sample_weight=None, reset_metrics=True
)
Test the model on a single batch of samples.
| Arguments | |
|---|---|
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 y should
not be specified (since targets will be obtained from the iterator).
|
sample_weight
|
Optional array of the same length as x, containing
weights to apply to the model's loss for each sample.
In the case of temporal data, you can pass a 2D array
with shape (samples, sequence_length),
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
sample_weight_mode="temporal" in compile(). This argument is not
supported when x is a dataset.
|
reset_metrics
|
If True, the metrics returned will be only for this
batch. If False, the metrics will be statefully accumulated across
batches.
|
| Returns | |
|---|---|
Scalar test loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.
|
| Raises | |
|---|---|
ValueError
|
In case of invalid user-provided arguments. |
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={}).
| Arguments | |
|---|---|
**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.
| Arguments | |
|---|---|
**kwargs
|
Additional keyword arguments
to be passed to yaml.dump().
|
| Returns | |
|---|---|
| A YAML string. |
| Raises | |
|---|---|
ImportError
|
if yaml module is not found. |
train_on_batch
train_on_batch(
x, y=None, sample_weight=None, class_weight=None, reset_metrics=True
)
Runs a single gradient update on a single batch of data.
| Arguments | |
|---|---|
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, y should not be specified
(since targets will be obtained from the iterator).
|
sample_weight
|
Optional array of the same length as x, containing
weights to apply to the model's loss for each sample. In the case of
temporal data, you can pass a 2D array with shape (samples,
sequence_length), to apply a different weight to every timestep of
every sample. In this case you should make sure to specify
sample_weight_mode="temporal" in compile(). This argument is not
supported when x is a dataset.
|
class_weight
|
Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class. |
reset_metrics
|
If True, the metrics returned will be only for this
batch. If False, the metrics will be statefully accumulated across
batches.
|
| Returns | |
|---|---|
Scalar training loss
(if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute model.metrics_names will give you
the display labels for the scalar outputs.
|
| Raises | |
|---|---|
ValueError
|
In case of invalid user-provided arguments. |