tf.keras.Model

A model grouping layers into an object with training/inference features.

Inherits From: Layer, Module

tf.keras.Model(
    *args, **kwargs
)

inputs The input(s) of the model: a keras.Input object or a combination of keras.Input objects in a dict, list or tuple.
outputs The output(s) of the model: a tensor that originated from keras.Input objects or a combination of such tensors in a dict, list or tuple. See Functional API example below.
name String, the name of the model.

There are two ways to instantiate a Model:

1 - With the "Functional API", where you start from Input, you chain layer calls to specify the model's forward pass, and finally you create your model from inputs and outputs:

import tensorflow as tf

inputs = tf.keras.Input(shape=(3,))
x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)

A new Functional API model can also be created by using the intermediate tensors. This enables you to quickly extract sub-components of the model.

Example:

inputs = keras.Input(shape=(None, None, 3))
processed = keras.layers.RandomCrop(width=32, height=32)(inputs)
conv = keras.layers.Conv2D(filters=2, kernel_size=3)(processed)
pooling = keras.layers.GlobalAveragePooling2D()(conv)
feature = keras.layers.Dense(10)(pooling)

full_model = keras.Model(inputs, feature)
backbone = keras.Model(processed, conv)
activations = keras.Model(conv, feature)

Note that the backbone and activations models are not created with keras.Input objects, but with the tensors that are originated from keras.Input objects. Under the hood, the layers and weights will be shared across these models, so that user can train the full_model, and use backbone or activations to do feature extraction. The inputs and outputs of the model can be nested structures of tensors as well, and the created models are standard Functional API models that support all the existing APIs.

2 - By subclassing the Model class: in that case, you should define your layers in __init__() and you should implement the model's forward pass in call().

import tensorflow as tf

class MyModel(tf.keras.Model):

  def __init__(self):
    super().__init__()
    self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
    self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)

  def call(self, inputs):
    x = self.dense1(inputs)
    return self.dense2(x)

model = MyModel()

If you subclass Model, you can optionally have a training argument (boolean) in call(), which you can use to specify a different behavior in training and inference:

import tensorflow as tf

class MyModel(tf.keras.Model):

  def __init__(self):
    super().__init__()
    self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
    self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
    self.dropout = tf.keras.layers.Dropout(0.5)

  def call(self, inputs, training=False):
    x = self.dense1(inputs)
    if training:
      x = self.dropout(x, training=training)
    return self.dense2(x)

model = MyModel()

Once the model is created, you can config the model with losses and metrics with model.compile(), train the model with model.fit(), or use the model to do prediction with model.predict().

distribute_reduction_method The method employed to reduce per-replica values during training.

Unless specified, the value "auto" will be assumed, indicating that the reduction strategy should be chosen based on the current running environment. See reduce_per_replica function for more details.

distribute_strategy The tf.distribute.Strategy this model was created under.
jit_compile Specify whether to compile the model with XLA.

XLA is an optimizing compiler for machine learning. jit_compile is not enabled by default. Note that jit_compile=True may not necessarily work for all models.

For more information on supported operations please refer to the XLA documentation. Also refer to known XLA issues for more details.

layers

metrics_names Returns the model's display labels for all outputs.

inputs = tf.keras.layers.Input(shape=(3,))
outputs = tf.keras.layers.Dense(2)(inputs)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
model.metrics_names
[]
 
x = np.random.random((2, 3))
y = np.random.randint(0, 2, (2, 2))
model.fit(x, y)
model.metrics_names
['loss', 'mae']
 
inputs = tf.keras.layers.Input(shape=(3,))
d = tf.keras.layers.Dense(2, name='out')
output_1 = d(inputs)
output_2 = d(inputs)
model = tf.keras.models.Model(
   inputs=inputs, outputs=[output_1, output_2])
model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
model.fit(x, (y, y))
model.metrics_names
['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
'out_1_acc']

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.

Methods

call

View source

call(
    inputs, training=None, mask=None
)

Calls the model on new inputs and returns the outputs as tensors.

In this case call() just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Args
inputs Input tensor, or dict/list/tuple of input tensors.
training Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.
mask A mask or list of masks. A mask can be either a boolean tensor or None (no mask). For more details, check the guide here.

Returns
A tensor if there is a single output, or a list of tensors if there are more than one outputs.

compile

View source

compile(
    optimizer='rmsprop',
    loss=None,
    metrics=None,
    loss_weights=None,
    weighted_metrics=None,
    run_eagerly=None,
    steps_per_execution=None,
    jit_compile=None,
    **kwargs
)

Configures the model for training.

Example:

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
              loss=tf.keras.losses.BinaryCrossentropy(),
              metrics=[tf.keras.metrics.BinaryAccuracy(),
                       tf.keras.metrics.FalseNegatives()])

Args
optimizer String (name of optimizer) or optimizer instance. See tf.keras.optimizers.
loss Loss function. May be a string (name of loss function), or a tf.keras.losses.Loss instance. See tf.keras.losses. A loss function is any callable with the signature loss = fn(y_true, y_pred), where y_true are the ground truth values, and y_pred are the model's predictions. y_true should have shape (batch_size, d0, .. dN) (except in the case of sparse loss functions such as sparse categorical crossentropy which expects integer arrays of shape (batch_size, d0, .. dN-1)). y_pred should have shape (batch_size, d0, .. dN). The loss function should return a float tensor. If a custom Loss instance is used and reduction is set to None, return value has shape (batch_size, d0, .. dN-1) i.e. per-sample or per-timestep loss values; otherwise, it is a scalar. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss value that will be minimized by the model will then be the sum of all individual losses, unless loss_weights is specified.
metrics List of metrics to be evaluated by the model during training and testing. Each of this can be a string (name of a built-in function), function or a tf.keras.metrics.Metric instance. See tf.keras.metrics. Typically you will use metrics=['accuracy']. A function is any callable with the signature result = fn(y_true, y_pred). To specify different metrics for different outputs of a multi-output model, you could also pass a dictionary, such as metrics={'output_a':'accuracy', 'output_b':['accuracy', 'mse']}. You can also pass a list to specify a metric or a list of metrics for each output, such as metrics=[['accuracy'], ['accuracy', 'mse']] or metrics=['accuracy', ['accuracy', 'mse']]. When you pass the strings 'accuracy' or 'acc', we convert this to one of tf.keras.metrics.BinaryAccuracy, tf.keras.metrics.CategoricalAccuracy, tf.keras.metrics.SparseCategoricalAccuracy based on the shapes of the targets and of the model output. We do a similar conversion for the strings 'crossentropy' and 'ce' as well. The metrics passed here are evaluated without sample weighting; if you would like sample weighting to apply, you can specify your metrics via the weighted_metrics argument instead.
loss_weights Optional list or dictionary specifying scalar coefficients (Python floats) to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be the weighted sum of all individual losses, weighted by the loss_weights coefficients. If a list, it is expected to have a 1:1 mapping to the model's outputs. If a dict, it is expected to map output names (strings) to scalar coefficients.
weighted_metrics List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing.
run_eagerly Bool. Defaults to False. If True, this Model's logic will not be wrapped in a tf.function. Recommended to leave this as None unless your Model cannot be run inside a tf.function. run_eagerly=True is not supported when using tf.distribute.experimental.ParameterServerStrategy.
steps_per_execution Int. Defaults to 1. The number of batches to run during each tf.function call. Running multiple batches inside a single tf.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 tf.function execution).
jit_compile If True, compile the model training step with XLA. XLA is an optimizing compiler for machine learning. jit_compile is not enabled for by default. Note that jit_compile=True may not necessarily work for all models. For more information on supported operations please refer to the XLA documentation. Also refer to known XLA issues for more details.
**kwargs Arguments supported for backwards compatibility only.

compile_from_config

View source

compile_from_config(
    config
)

compute_loss

View source

compute_loss(
    x=None, y=None, y_pred=None, sample_weight=None
)

Compute the total loss, validate it, and return it.

Subclasses can optionally override this method to provide custom loss computation logic.

Example:

class MyModel(tf.keras.Model):

  def __init__(self, *args, **kwargs):
    super(MyModel, self).__init__(*args, **kwargs)
    self.loss_tracker = tf.keras.metrics.Mean(name='loss')

  def compute_loss(self, x, y, y_pred, sample_weight):
    loss = tf.reduce_mean(tf.math.squared_difference(y_pred, y))
    loss += tf.add_n(self.losses)
    self.loss_tracker.update_state(loss)
    return loss

  def reset_metrics(self):
    self.loss_tracker.reset_states()

  @property
  def metrics(self):
    return [self.loss_tracker]

tensors = tf.random.uniform((10, 10)), tf.random.uniform((10,))
dataset = tf.data.Dataset.from_tensor_slices(tensors).repeat().batch(1)

inputs = tf.keras.layers.Input(shape=(10,), name='my_input')
outputs = tf.keras.layers.Dense(10)(inputs)
model = MyModel(inputs, outputs)
model.add_loss(tf.reduce_sum(outputs))

optimizer = tf.keras.optimizers.SGD()
model.compile(optimizer, loss='mse', steps_per_execution=10)
model.fit(dataset, epochs=2, steps_per_epoch=10)
print('My custom loss: ', model.loss_tracker.result().numpy())

Args
x Input data.
y Target data.
y_pred Predictions returned by the model (output of model(x))
sample_weight Sample weights for weighting the loss function.

Returns
The total loss as a tf.Tensor, or None if no loss results (which is the case when called by Model.test_step).

compute_metrics

View source

compute_metrics(
    x, y, y_pred, sample_weight
)

Update metric states and collect all metrics to be returned.

Subclasses can optionally override this method to provide custom metric updating and collection logic.

Example:

class MyModel(tf.keras.Sequential):

  def compute_metrics(self, x, y, y_pred, sample_weight):

    # This super call updates `self.compiled_metrics` and returns
    # results for all metrics listed in `self.metrics`.
    metric_results = super(MyModel, self).compute_metrics(
        x, y, y_pred, sample_weight)

    # Note that `self.custom_metric` is not listed in `self.metrics`.
    self.custom_metric.update_state(x, y, y_pred, sample_weight)
    metric_results['custom_metric_name'] = self.custom_metric.result()
    return metric_results

Args
x Input data.
y Target data.
y_pred Predictions returned by the model (output of model.call(x))
sample_weight Sample weights for weighting the loss function.

Returns
A dict containing values that will be passed to tf.keras.callbacks.CallbackList.on_train_batch_end(). Typically, the values of the metrics listed in self.metrics are returned. Example: {'loss': 0.2, 'accuracy': 0.7}.

evaluate

View source

evaluate(
    x=None,
    y=None,
    batch_size=None,
    verbose='auto',
    sample_weight=None,
    steps=None,
    callbacks=None,
    max_queue_size=10,
    workers=1,
    use_multiprocessing=False,
    return_dict=False,
    **kwargs
)

Returns the loss value & metrics values for the model in test mode.

Computation is done in batches (see the batch_size arg.)

Args
x Input data. It could be:

  • A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
  • A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
  • A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
  • A tf.data dataset. Should return a tuple of either (inputs, targets) or (inputs, targets, sample_weights).
  • A generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample_weights). A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the Unpacking behavior for iterator-like inputs section of Model.fit.
y Target data. Like the input data x, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely). If x is a dataset, generator or keras.utils.Sequence instance, y should not be specified (since targets will be obtained from the iterator/dataset).
batch_size Integer or None. Number of samples per batch of computation. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of a dataset, generators, or keras.utils.Sequence instances (since they generate batches).
verbose "auto", 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. "auto" defaults to 1 for most cases, and to 2 when used with ParameterServerStrategy. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (e.g. in a production environment).
sample_weight Optional Numpy array of weights for the test samples, used for weighting the loss function. You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. This argument is not supported when x is a dataset, instead pass sample weights as the third element of x.
steps Integer or None. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of None. If x is a tf.data dataset and steps is None, 'evaluate' will run until the dataset is exhausted. This argument is not supported with array inputs.
callbacks List of keras.callbacks.Callback instances. List of callbacks to apply during evaluation. See callbacks.
max_queue_size Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.
workers Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using process-based threading. If unspecified, workers will default to 1.
use_multiprocessing Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes.
return_dict If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.
**kwargs Unused at this time.

See the discussion of Unpacking behavior for iterator-like inputs for Model.fit.

Returns
Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

Raises
RuntimeError If model.evaluate is wrapped in a tf.function.

export

View source

export(
    filepath
)

Create a SavedModel artifact for inference (e.g. via TF-Serving).

This method lets you export a model to a lightweight SavedModel artifact that contains the model's forward pass only (its call() method) and can be served via e.g. TF-Serving. The forward pass is registered under the name serve() (see example below).

The original code of the model (including any custom layers you may have used) is no longer necessary to reload the artifact -- it is entirely standalone.

Args
filepath str or pathlib.Path object. Path where to save the artifact.

Example:

# Create the artifact
model.export("path/to/location")

# Later, in a different process / environment...
reloaded_artifact = tf.saved_model.load("path/to/location")
predictions = reloaded_artifact.serve(input_data)

If you would like to customize your serving endpoints, you can use the lower-level keras.export.ExportArchive class. The export() method relies on ExportArchive internally.

fit

View source

fit(
    x=None,
    y=None,
    batch_size=None,
    epochs=1,
    verbose='auto',
    callbacks=None,
    validation_split=0.0,
    validation_data=None,
    shuffle=True,
    class_weight=None,
    sample_weight=None,
    initial_epoch=0,
    steps_per_epoch=None,
    validation_steps=None,
    validation_batch_size=None,
    validation_freq=1,
    max_queue_size=10,
    workers=1,
    use_multiprocessing=False
)

Trains the model for a fixed number of epochs (dataset iterations).

Args
x Input data. It could be:

  • A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
  • A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
  • A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
  • A tf.data dataset. Should return a tuple of either (inputs, targets) or (inputs, targets, sample_weights).
  • A generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample_weights).
  • A tf.keras.utils.experimental.DatasetCreator, which wraps a callable that takes a single argument of type tf.distribute.InputContext, and returns a tf.data.Dataset. DatasetCreator should be used when users prefer to specify the per-replica batching and sharding logic for the Dataset. See tf.keras.utils.experimental.DatasetCreator doc for more information. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given below. If these include sample_weights as a third component, note that sample weighting applies to the weighted_metrics argument but not the metrics argument in compile(). If using tf.distribute.experimental.ParameterServerStrategy, only DatasetCreator type is supported for x.
y Target data. Like the input data x, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely). If x is a dataset, generator, or keras.utils.Sequence instance, y should not be specified (since targets will be obtained from x).
batch_size Integer or None. Number of samples per gradient update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches).
epochs Integer. Number of epochs to train the model. An epoch is an iteration over the entire x and y data provided (unless the steps_per_epoch flag is set to something other than None). Note that in conjunction with initial_epoch, epochs is to be understood as "final epoch". The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs is reached.
verbose 'auto', 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. 'auto' defaults to 1 for most cases, but 2 when used with ParameterServerStrategy. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (eg, in a production environment).
callbacks List of keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks. Note tf.keras.callbacks.ProgbarLogger and tf.keras.callbacks.History callbacks are created automatically and need not be passed into model.fit. tf.keras.callbacks.ProgbarLogger is created or not based on verbose argument to model.fit. Callbacks with batch-level calls are currently unsupported with tf.distribute.experimental.ParameterServerStrategy, and users are advised to implement epoch-level calls instead with an appropriate steps_per_epoch value.
validation_split Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the x and y data provided, before shuffling. This argument is not supported when x is a dataset, generator or keras.utils.Sequence instance. If both validation_data and validation_split are provided, validation_data will override validation_split. validation_split is not yet supported with tf.distribute.experimental.ParameterServerStrategy.
validation_data Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. Thus, note the fact that the validation loss of data provided using validation_split or validation_data is not affected by regularization layers like noise and dropout. validation_data will override validation_split. validation_data could be:
shuffle Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). This argument is ignored when x is a generator or an object of tf.data.Dataset. 'batch' is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized chunks. Has no effect when steps_per_epoch is not None.
class_weight Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class.
sample_weight Optional Numpy array of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. This argument is not supported when x is a dataset, generator, or keras.utils.Sequence instance, instead provide the sample_weights as the third element of x. Note that sample weighting does not apply to metrics specified via the metrics argument in compile(). To apply sample weighting to your metrics, you can specify them via the weighted_metrics in compile() instead.
initial_epoch Integer. Epoch at which to start training (useful for resuming a previous training run).
steps_per_epoch Integer or None. Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors such as TensorFlow data tensors, the default None is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a tf.data dataset, and 'steps_per_epoch' is None, the epoch will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the steps_per_epoch argument. If steps_per_epoch=-1 the training will run indefinitely with an infinitely repeating dataset. This argument is not supported with array inputs. When using tf.distribute.experimental.ParameterServerStrategy:
  • steps_per_epoch=None is not supported.
    • validation_steps Only relevant if validation_data is provided and is a tf.data dataset. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch. If 'validation_steps' is None, validation will run until the validation_data dataset is exhausted. In the case of an infinitely repeated dataset, it will run into an infinite loop. If 'validation_steps' is specified and only part of the dataset will be consumed, the evaluation will start from the beginning of the dataset at each epoch. This ensures that the same validation samples are used every time.
    validation_batch_size Integer or None. Number of samples per validation batch. If unspecified, will default to batch_size. Do not specify the validation_batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches).
    validation_freq Only relevant if validation data is provided. Integer or collections.abc.Container instance (e.g. list, tuple, etc.). If an integer, specifies how many training epochs to run before a new validation run is performed, e.g. validation_freq=2 runs validation every 2 epochs. If a Container, specifies the epochs on which to run validation, e.g. validation_freq=[1, 2, 10] runs validation at the end of the 1st, 2nd, and 10th epochs.
    max_queue_size Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.
    workers Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using process-based threading. If unspecified, workers will default to 1.
    use_multiprocessing Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes.

    Unpacking behavior for iterator-like inputs: A common pattern is to pass a tf.data.Dataset, generator, or tf.keras.utils.Sequence to the x argument of fit, which will in fact yield not only features (x) but optionally targets (y) and sample weights. Keras requires that the output of such iterator-likes be unambiguous. The iterator should return a tuple of length 1, 2, or 3, where the optional second and third elements will be used for y and sample_weight respectively. Any other type provided will be wrapped in a length one tuple, effectively treating everything as 'x'. When yielding dicts, they should still adhere to the top-level tuple structure. e.g. ({"x0": x0, "x1": x1}, y). Keras will not attempt to separate features, targets, and weights from the keys of a single dict. A notable unsupported data type is the namedtuple. The reason is that it behaves like both an ordered datatype (tuple) and a mapping datatype (dict). So given a namedtuple of the form: namedtuple("example_tuple", ["y", "x"]) it is ambiguous whether to reverse the order of the elements when interpreting the value. Even worse is a tuple of the form: namedtuple("other_tuple", ["x", "y", "z"]) where it is unclear if the tuple was intended to be unpacked into x, y, and sample_weight or passed through as a single element to x. As a result the data processing code will simply raise a ValueError if it encounters a namedtuple. (Along with instructions to remedy the issue.)

    Returns
    A History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

    Raises
    RuntimeError

    1. If the model was never compiled or,
    2. If model.fit is wrapped in tf.function.
    ValueError In case of mismatch between the provided input data and what the model expects or when the input data is empty.

    get_compile_config

    View source

    get_compile_config()

    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).

    Args
    name String, name of layer.
    index Integer, index of layer.

    Returns
    A layer instance.

    get_metrics_result

    View source

    get_metrics_result()

    Returns the model's metrics values as a dict.

    If any of the metric result is a dict (containing multiple metrics), each of them gets added to the top level returned dict of this method.

    Returns
    A dict containing values of the metrics listed in self.metrics.
    Example {'loss': 0.2, 'accuracy': 0.7}.

    get_weight_paths

    View source

    get_weight_paths()

    Retrieve all the variables and their paths for the model.

    The variable path (string) is a stable key to identify a tf.Variable instance owned by the model. It can be used to specify variable-specific configurations (e.g. DTensor, quantization) from a global view.

    This method returns a dict with weight object paths as keys and the corresponding tf.Variable instances as values.

    Note that if the model is a subclassed model and the weights haven't been initialized, an empty dict will be returned.

    Returns
    A dict where keys are variable paths and values are tf.Variable instances.

    Example:

    class SubclassModel(tf.keras.Model):

  def __init__(self, name=None):
    super().__init__(name=name)
    self.d1 = tf.keras.layers.Dense(10)
    self.d2 = tf.keras.layers.Dense(20)

  def call(self, inputs):
    x = self.d1(inputs)
    return self.d2(x)

model = SubclassModel()
model(tf.zeros((10, 10)))
weight_paths = model.get_weight_paths()
# weight_paths:
# {
#    'd1.kernel': model.d1.kernel,
#    'd1.bias': model.d1.bias,
#    'd2.kernel': model.d2.kernel,
#    'd2.bias': model.d2.bias,
# }

# Functional model
inputs = tf.keras.Input((10,), batch_size=10)
x = tf.keras.layers.Dense(20, name='d1')(inputs)
output = tf.keras.layers.Dense(30, name='d2')(x)
model = tf.keras.Model(inputs, output)
d1 = model.layers[1]
d2 = model.layers[2]
weight_paths = model.get_weight_paths()
# weight_paths:
# {
#    'd1.kernel': d1.kernel,
#    'd1.bias': d1.bias,
#    'd2.kernel': d2.kernel,
#    'd2.bias': d2.bias,
# }

    load_weights

    View source

    load_weights(
    filepath, skip_mismatch=False, by_name=False, options=None
)

    Loads all layer weights from a saved files.

    The saved file could be a SavedModel file, a .keras file (v3 saving format), or a file created via model.save_weights().

    By default, weights are loaded based on the network's topology. This means the architecture should be the same as when the weights were saved. Note that layers that don't have weights are not taken into account in the topological ordering, so adding or removing layers is fine as long as they don't have weights.

    Partial weight loading

    If you have modified your model, for instance by adding a new layer (with weights) or by changing the shape of the weights of a layer, you can choose to ignore errors and continue loading by setting skip_mismatch=True. In this case any layer with mismatching weights will be skipped. A warning will be displayed for each skipped layer.

    Weight loading by name

    If your weights are saved as a .h5 file created via model.save_weights(), you can use the argument by_name=True.

    In this case, weights are loaded into layers only if they share the same name. This is useful for fine-tuning or transfer-learning models where some of the layers have changed.

    Note that only topological loading (by_name=False) is supported when loading weights from the .keras v3 format or from the TensorFlow SavedModel format.

    Args
    filepath String, path to the weights file to load. For weight files in TensorFlow format, this is the file prefix (the same as was passed to save_weights()). This can also be a path to a SavedModel or a .keras file (v3 saving format) saved via model.save().
    skip_mismatch Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weights.
    by_name Boolean, whether to load weights by name or by topological order. Only topological loading is supported for weight files in the .keras v3 format or in the TensorFlow SavedModel format.
    options Optional tf.train.CheckpointOptions object that specifies options for loading weights (only valid for a SavedModel file).

    make_predict_function

    View source

    make_predict_function(
    force=False
)

    Creates a function that executes one step of inference.

    This method can be overridden to support custom inference logic. This method is called by Model.predict and Model.predict_on_batch.

    Typically, this method directly controls tf.function and tf.distribute.Strategy settings, and delegates the actual evaluation logic to Model.predict_step.

    This function is cached the first time Model.predict or Model.predict_on_batch is called. The cache is cleared whenever Model.compile is called. You can skip the cache and generate again the function with force=True.

    Args
    force Whether to regenerate the predict function and skip the cached function if available.

    Returns
    Function. The function created by this method should accept a tf.data.Iterator, and return the outputs of the Model.

    make_test_function

    View source

    make_test_function(
    force=False
)

    Creates a function that executes one step of evaluation.

    This method can be overridden to support custom evaluation logic. This method is called by Model.evaluate and Model.test_on_batch.

    Typically, this method directly controls tf.function and tf.distribute.Strategy settings, and delegates the actual evaluation logic to Model.test_step.

    This function is cached the first time Model.evaluate or Model.test_on_batch is called. The cache is cleared whenever Model.compile is called. You can skip the cache and generate again the function with force=True.

    Args
    force Whether to regenerate the test function and skip the cached function if available.

    Returns
    Function. The function created by this method should accept a tf.data.Iterator, and return a dict containing values that will be passed to tf.keras.Callbacks.on_test_batch_end.

    make_train_function

    View source

    make_train_function(
    force=False
)

    Creates a function that executes one step of training.

    This method can be overridden to support custom training logic. This method is called by Model.fit and Model.train_on_batch.

    Typically, this method directly controls tf.function and tf.distribute.Strategy settings, and delegates the actual training logic to Model.train_step.

    This function is cached the first time Model.fit or Model.train_on_batch is called. The cache is cleared whenever Model.compile is called. You can skip the cache and generate again the function with force=True.

    Args
    force Whether to regenerate the train function and skip the cached function if available.

    Returns
    Function. The function created by this method should accept a tf.data.Iterator, and return a dict containing values that will be passed to tf.keras.Callbacks.on_train_batch_end, such as {'loss': 0.2, 'accuracy': 0.7}.

    predict

    View source

    predict(
    x,
    batch_size=None,
    verbose='auto',
    steps=None,
    callbacks=None,
    max_queue_size=10,
    workers=1,
    use_multiprocessing=False
)

    Generates output predictions for the input samples.

    Computation is done in batches. This method is designed for batch processing of large numbers of inputs. It is not intended for use inside of loops that iterate over your data and process small numbers of inputs at a time.

    For small numbers of inputs that fit in one batch, directly use __call__() for faster execution, e.g., model(x), or model(x, training=False) if you have layers such as tf.keras.layers.BatchNormalization that behave differently during inference. You may pair the individual model call with a tf.function for additional performance inside your inner loop. If you need access to numpy array values instead of tensors after your model call, you can use tensor.numpy() to get the numpy array value of an eager tensor.

    Also, note the fact that test loss is not affected by regularization layers like noise and dropout.

    Args
    x Input samples. It could be:

    • A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
    • A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
    • A tf.data dataset.
    • A generator or keras.utils.Sequence instance. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the Unpacking behavior for iterator-like inputs section of Model.fit.
    batch_size Integer or None. Number of samples per batch. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of dataset, generators, or keras.utils.Sequence instances (since they generate batches).
    verbose "auto", 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. "auto" defaults to 1 for most cases, and to 2 when used with ParameterServerStrategy. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (e.g. in a production environment).
    steps Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of None. If x is a tf.data dataset and steps is None, predict() will run until the input dataset is exhausted.
    callbacks List of keras.callbacks.Callback instances. List of callbacks to apply during prediction. See callbacks.
    max_queue_size Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.
    workers Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using process-based threading. If unspecified, workers will default to 1.
    use_multiprocessing Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes.

    See the discussion of Unpacking behavior for iterator-like inputs for Model.fit. Note that Model.predict uses the same interpretation rules as Model.fit and Model.evaluate, so inputs must be unambiguous for all three methods.

    Returns
    Numpy array(s) of predictions.

    Raises
    RuntimeError If model.predict is wrapped in a tf.function.
    ValueError In case of mismatch between the provided input data and the model's expectations, or in case a stateful model receives a number of samples that is not a multiple of the batch size.

    predict_on_batch

    View source

    predict_on_batch(
    x
)

    Returns predictions for a single batch of samples.

    Args
    x Input data. It could be:

    • A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
    • A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).

    Returns
    Numpy array(s) of predictions.

    Raises
    RuntimeError If model.predict_on_batch is wrapped in a tf.function.

    predict_step

    View source

    predict_step(
    data
)

    The logic for one inference step.

    This method can be overridden to support custom inference logic. This method is called by Model.make_predict_function.

    This method should contain the mathematical logic for one step of inference. This typically includes the forward pass.

    Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_predict_function, which can also be overridden.

    Args
    data A nested structure of Tensors.

    Returns
    The result of one inference step, typically the output of calling the Model on data.

    reset_metrics

    View source

    reset_metrics()

    Resets the state of all the metrics in the model.

    Examples:

    inputs = tf.keras.layers.Input(shape=(3,))
outputs = tf.keras.layers.Dense(2)(inputs)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
     
    x = np.random.random((2, 3))
y = np.random.randint(0, 2, (2, 2))
_ = model.fit(x, y, verbose=0)
assert all(float(m.result()) for m in model.metrics)
     
    model.reset_metrics()
assert all(float(m.result()) == 0 for m in model.metrics)

    reset_states

    View source

    reset_states()

    save

    View source

    save(
    filepath, overwrite=True, save_format=None, **kwargs
)

    Saves a model as a TensorFlow SavedModel or HDF5 file.

    See the Serialization and Saving guide for details.

    Args
    model Keras model instance to be saved.
    filepath str or pathlib.Path object. Path where to save the model.
    overwrite Whether we should overwrite any existing model at the target location, or instead ask the user via an interactive prompt.
    save_format Either "keras", "tf", "h5", indicating whether to save the model in the native Keras format (.keras), in the TensorFlow SavedModel format (referred to as "SavedModel" below), or in the legacy HDF5 format (.h5). Defaults to "tf" in TF 2.X, and "h5" in TF 1.X.

    SavedModel format arguments: include_optimizer: Only applied to SavedModel and legacy HDF5 formats. If False, do not save the optimizer state. Defaults to True. signatures: Only applies to SavedModel format. Signatures to save with the SavedModel. See the signatures argument in tf.saved_model.save for details. options: Only applies to SavedModel format. tf.saved_model.SaveOptions object that specifies SavedModel saving options. save_traces: Only applies to SavedModel format. When enabled, the SavedModel will store the function traces for each layer. This can be disabled, so that only the configs of each layer are stored. Defaults to True. Disabling this will decrease serialization time and reduce file size, but it requires that all custom layers/models implement a get_config() method.

    Example:

    model = tf.keras.Sequential([
    tf.keras.layers.Dense(5, input_shape=(3,)),
    tf.keras.layers.Softmax()])
model.save("model.keras")
loaded_model = tf.keras.models.load_model("model.keras")
x = tf.random.uniform((10, 3))
assert np.allclose(model.predict(x), loaded_model.predict(x))

    Note that model.save() is an alias for tf.keras.models.save_model().

    save_spec

    View source

    save_spec(
    dynamic_batch=True
)

    Returns the tf.TensorSpec of call args as a tuple (args, kwargs).

    This value is automatically defined after calling the model for the first time. Afterwards, you can use it when exporting the model for serving:

    model = tf.keras.Model(...)

@tf.function
def serve(*args, **kwargs):
  outputs = model(*args, **kwargs)
  # Apply postprocessing steps, or add additional outputs.
  ...
  return outputs

# arg_specs is `[tf.TensorSpec(...), ...]`. kwarg_specs, in this
# example, is an empty dict since functional models do not use keyword
# arguments.
arg_specs, kwarg_specs = model.save_spec()

model.save(path, signatures={
  'serving_default': serve.get_concrete_function(*arg_specs,
                                                 **kwarg_specs)
})

    Args
    dynamic_batch Whether to set the batch sizes of all the returned tf.TensorSpec to None. (Note that when defining functional or Sequential models with tf.keras.Input([...], batch_size=X), the batch size will always be preserved). Defaults to True.

    Returns
    If the model inputs are defined, returns a tuple (args, kwargs). All elements in args and kwargs are tf.TensorSpec. If the model inputs are not defined, returns None. The model inputs are automatically set when calling the model, model.fit, model.evaluate or model.predict.

    save_weights

    View source

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

    Saves all layer weights.

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

    When saving in HDF5 format, the weight file has:

    • layer_names (attribute), a list of strings (ordered names of model layers).
    • For every layer, a 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.

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

    Raises
    ImportError If h5py is not available when attempting to save in HDF5 format.

    summary

    View source

    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, defaults to [.33, .55, .67, 1.].
    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. If not provided, defaults to False.
    show_trainable Whether to show if a layer is trainable. If not provided, defaults to False.
    layer_range a list or tuple of 2 strings, which is the starting layer name and ending layer name (both inclusive) indicating the range of layers to be printed in summary. It also accepts regex patterns instead of exact name. In such case, start predicate will be the first element it matches to layer_range[0] and the end predicate will be the last element it matches to layer_range[1]. By default None which considers all layers of model.

    Raises
    ValueError if summary() is called before the model is built.

    test_on_batch

    View source

    test_on_batch(
    x, y=None, sample_weight=None, reset_metrics=True, return_dict=False
)

    Test the model on a single batch of samples.

    Args
    x Input data. It could be:

    • A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
    • A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
    • A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
    y Target data. Like the input data x, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with x (you cannot have Numpy inputs and tensor targets, or inversely).
    sample_weight Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample.
    reset_metrics If True, the metrics returned will be only for this batch. If False, the metrics will be statefully accumulated across batches.
    return_dict If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.

    Returns
    Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

    Raises
    RuntimeError If model.test_on_batch is wrapped in a tf.function.

    test_step

    View source

    test_step(
    data
)

    The logic for one evaluation step.

    This method can be overridden to support custom evaluation logic. This method is called by Model.make_test_function.

    This function should contain the mathematical logic for one step of evaluation. This typically includes the forward pass, loss calculation, and metrics updates.

    Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_test_function, which can also be overridden.

    Args
    data A nested structure of Tensors.

    Returns
    A dict containing values that will be passed to tf.keras.callbacks.CallbackList.on_train_batch_end. Typically, the values of the Model's metrics are returned.

    to_json

    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={}).

    Args
    **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.

    Args
    **kwargs Additional keyword arguments to be passed to yaml.dump().

    Returns
    A YAML string.

    Raises
    RuntimeError announces that the method poses a security risk

    train_on_batch

    View source

    train_on_batch(
    x,
    y=None,
    sample_weight=None,
    class_weight=None,
    reset_metrics=True,
    return_dict=False
)

    Runs a single gradient update on a single batch of data.

    Args
    x Input data. It could be:

    • A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
    • A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
    • A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
    y Target data. Like the input data x, it could be either Numpy array(s) or TensorFlow tensor(s).
    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.
    reset_metrics If True, the metrics returned will be only for this batch. If False, the metrics will be statefully accumulated across batches.
    return_dict If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.

    Returns
    Scalar training loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.

    Raises
    RuntimeError If model.train_on_batch is wrapped in a tf.function.

    train_step

    View source

    train_step(
    data
)

    The logic for one training step.

    This method can be overridden to support custom training logic. For concrete examples of how to override this method see Customizing what happens in fit. This method is called by Model.make_train_function.

    This method should contain the mathematical logic for one step of training. This typically includes the forward pass, loss calculation, backpropagation, and metric updates.

    Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_train_function, which can also be overridden.

    Args
    data A nested structure of Tensors.

    Returns
    A dict containing values that will be passed to tf.keras.callbacks.CallbackList.on_train_batch_end. Typically, the values of the Model's metrics are returned. Example: {'loss': 0.2, 'accuracy': 0.7}.