tf.keras.experimental.WideDeepModel

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Wide & Deep Model for regression and classification problems.

Inherits From: Model

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.experimental.WideDeepModel, `tf.compat.v2.keras.experimental.WideDeepModel`

tf.keras.experimental.WideDeepModel(
    linear_model, dnn_model, activation=None, **kwargs
)

This model jointly train a linear and a dnn model.

Example:

linear_model = LinearModel()
dnn_model = keras.Sequential([keras.layers.Dense(units=64),
                             keras.layers.Dense(units=1)])
combined_model = WideDeepModel(dnn_model, linear_model)
combined_model.compile(optimizer=['sgd', 'adam'], 'mse', ['mse'])
# define dnn_inputs and linear_inputs as separate numpy arrays or
# a single numpy array if dnn_inputs is same as linear_inputs.
combined_model.fit([dnn_inputs, linear_inputs], y, epochs)
# or define a single `tf.data.Dataset` that contains a single tensor or
# separate tensors for dnn_inputs and linear_inputs.
dataset = tf.data.Dataset.from_tensors(([dnn_inputs, linear_inputs], y))
combined_model.fit(dataset, epochs)

Both linear and dnn model can be pre-compiled and trained separately before jointly training:

Example:

linear_model = LinearModel()
linear_model.compile('adagrad', 'mse')
linear_model.fit(linear_inputs, y, epochs)
dnn_model = keras.Sequential([keras.layers.Dense(units=1)])
dnn_model.compile('rmsprop', 'mse')
dnn_model.fit(dnn_inputs, y, epochs)
combined_model = WideDeepModel(dnn_model, linear_model)
combined_model.compile(optimizer=['sgd', 'adam'], 'mse', ['mse'])
combined_model.fit([dnn_inputs, linear_inputs], y, epochs)

Args
linear_model	a premade LinearModel, its output must match the output of the dnn model.
dnn_model	a tf.keras.Model, its output must match the output of the linear model.
activation	Activation function. Set it to None to maintain a linear activation.
**kwargs	The keyword arguments that are passed on to BaseLayer.init. Allowed keyword arguments include name.

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

View source

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

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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: A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). A dict mapping input names to the corresponding array/tensors, if the model has named inputs. A tf.data dataset. A generator or keras.utils.Sequence instance.
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

View source

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

View source

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: A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). A dict mapping input names to the corresponding array/tensors, if the model has named inputs. A tf.data dataset. Should return a tuple of either (inputs, targets) or (inputs, targets, sample_weights). A generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample weights).
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: tuple (x_val, y_val) of Numpy arrays or tensors tuple (x_val, y_val, val_sample_weights) of Numpy arrays dataset For the first two cases, batch_size must be provided. For the last case, validation_steps 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

View source

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 a tuple (inputs, targets) a tuple (inputs, targets, sample_weights). This tuple (a single output of the generator) makes a single batch. Therefore, all arrays in this tuple must have the same length (equal to the size of this batch). Different batches may have different sizes. For example, the last batch of the epoch is commonly smaller than the others, if the size of the dataset is not divisible by the batch size. The generator is expected to loop over its data indefinitely. An epoch finishes when steps_per_epoch batches have been seen by the model.
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 a generator for the validation data a tuple (inputs, targets) a tuple (inputs, targets, sample_weights).
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

View source

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

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load_weights(
    filepath, by_name=False
)

Loads all layer weights, either from a TensorFlow or an HDF5 file.

predict

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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: A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). A tf.data dataset. A generator or keras.utils.Sequence instance.
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

View source

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

View source

predict_on_batch(
    x
)

Returns predictions for a single batch of samples.

Arguments
x	Input data. It could be: A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). A tf.data dataset.

Returns
Numpy array(s) of predictions.

Raises
ValueError	In case of mismatch between given number of inputs and expectations of the model.

reset_metrics

View source

reset_metrics()

Resets the state of metrics.

reset_states

View source

reset_states()

save

View source

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

View source

save_weights(
    filepath, overwrite=True, save_format=None
)

Saves all layer weights.

Either saves in HDF5 or in TensorFlow format based on the save_format argument.

When saving in HDF5 format, the weight file has:

• layer_names (attribute), a list of strings (ordered names of model layers).
• For every layer, a group named layer.name
  • For every such layer group, a group attribute weight_names, a list of strings (ordered names of weights tensor of the layer).
  • For every weight in the layer, a dataset storing the weight value, named after the weight tensor.

When saving in TensorFlow format, all objects referenced by the network are saved in the same format as tf.train.Checkpoint, including any Layer instances or Optimizer instances assigned to object attributes. For networks constructed from inputs and outputs using tf.keras.Model(inputs, outputs), Layer instances used by the network are tracked/saved automatically. For user-defined classes which inherit from tf.keras.Model, Layer instances must be assigned to object attributes, typically in the constructor. See the documentation of tf.train.Checkpoint and tf.keras.Model for details.

While the formats are the same, do not mix save_weights and tf.train.Checkpoint. Checkpoints saved by Model.save_weights should be loaded using Model.load_weights. Checkpoints saved using tf.train.Checkpoint.save should be restored using the corresponding tf.train.Checkpoint.restore. Prefer tf.train.Checkpoint over save_weights for training checkpoints.

The TensorFlow format matches objects and variables by starting at a root object, self for save_weights, and greedily matching attribute names. For Model.save this is the Model, and for Checkpoint.save this is the Checkpoint even if the Checkpoint has a model attached. This means saving a tf.keras.Model using save_weights and loading into a tf.train.Checkpoint with a Model attached (or vice versa) will not match the Model's variables. See the guide to training checkpoints for details on the TensorFlow format.

Arguments
filepath	String, path to the file to save the weights to. When saving in TensorFlow format, this is the prefix used for checkpoint files (multiple files are generated). Note that the '.h5' suffix causes weights to be saved in HDF5 format.
overwrite	Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt.
save_format	Either 'tf' or 'h5'. A filepath ending in '.h5' or '.keras' will default to HDF5 if save_format is None. Otherwise None defaults to 'tf'.

Raises
ImportError	If h5py is not available when attempting to save in HDF5 format.
ValueError	For invalid/unknown format arguments.

summary

View source

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

View source

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: A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). A dict mapping input names to the corresponding array/tensors, if the model has named inputs. A tf.data dataset.
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

View source

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

View source

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

View source

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: A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). A dict mapping input names to the corresponding array/tensors, if the model has named inputs. A tf.data dataset.
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.