tfm.nlp.models.BertPretrainer  |  TensorFlow v2.16.1 (original) (raw)

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tfm.nlp.models.BertPretrainer

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BERT pretraining model.

tfm.nlp.models.BertPretrainer(
    network,
    num_classes,
    num_token_predictions,
    embedding_table=None,
    activation=None,
    initializer='glorot_uniform',
    output='logits',
    **kwargs
)

[Note] Please use the new BertPretrainerV2 for your projects.

The BertPretrainer allows a user to pass in a transformer stack, and instantiates the masked language model and classification networks that are used to create the training objectives.

Args
network A transformer network. This network should output a sequence output and a classification output.
num_classes Number of classes to predict from the classification network.
num_token_predictions Number of tokens to predict from the masked LM.
embedding_table Embedding table of a network. If None, the "network.get_embedding_table()" is used.
activation The activation (if any) to use in the masked LM network. If None, no activation will be used.
initializer The initializer (if any) to use in the masked LM and classification networks. Defaults to a Glorot uniform initializer.
output The output style for this network. Can be either logits orpredictions.

| Attributes | | | | ------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | activity_regularizer | Optional regularizer function for the output of this layer. | | | autotune_steps_per_execution | Settable property to enable tuning for steps_per_execution | | | compute_dtype | The dtype of the layer's computations.This is equivalent to Layer.dtype_policy.compute_dtype. Unless mixed precision is used, this is the same as Layer.dtype, the dtype of the weights. Layers automatically cast their inputs to the compute dtype, which causes computations and the output to be in the compute dtype as well. This is done by the base Layer class in Layer.call, so you do not have to insert these casts if implementing your own layer. Layers often perform certain internal computations in higher precision when compute_dtype is float16 or bfloat16 for numeric stability. The output will still typically be float16 or bfloat16 in such cases. | | | 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. | | | dtype | The dtype of the layer weights.This is equivalent to Layer.dtype_policy.variable_dtype. Unless mixed precision is used, this is the same as Layer.compute_dtype, the dtype of the layer's computations. | | | dtype_policy | The dtype policy associated with this layer.This is an instance of a tf.keras.mixed_precision.Policy. | | | dynamic | Whether the layer is dynamic (eager-only); set in the constructor. | | | input | Retrieves the input tensor(s) of a layer.Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer. | | | input_spec | InputSpec instance(s) describing the input format for this layer.When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__(): self.input_spec = tf.keras.layers.InputSpec(ndim=4) Now, if you try to call the layer on an input that isn't rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error: ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] Input checks that can be specified via input_spec include: Structure (e.g. a single input, a list of 2 inputs, etc) Shape Rank (ndim) Dtype For more information, see tf.keras.layers.InputSpec. | | | 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 theXLA documentation. Also refer toknown XLA issuesfor more details. | | | layers | | | | losses | List of losses added using the add_loss() API.Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under atf.GradientTape will propagate gradients back to the corresponding variables. class MyLayer(tf.keras.layers.Layer): def call(self, inputs): self.add_loss(tf.abs(tf.reduce_mean(inputs))) return inputs l = MyLayer() l(np.ones((10, 1))) l.losses [1.0] inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. len(model.losses) 0 model.add_loss(tf.abs(tf.reduce_mean(x))) len(model.losses) 1 inputs = tf.keras.Input(shape=(10,)) d = tf.keras.layers.Dense(10, kernel_initializer='ones') x = d(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(d.kernel)) model.losses [<tf.Tensor: shape=(), dtype=float32, numpy=1.0>] | | | metrics | Return metrics added using compile() or add_metric().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"]) [m.name for m in model.metrics] [] x = np.random.random((2, 3)) y = np.random.randint(0, 2, (2, 2)) model.fit(x, y) [m.name for m in model.metrics] ['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.add_metric( tf.reduce_sum(output_2), name='mean', aggregation='mean') model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"]) model.fit(x, (y, y)) [m.name for m in model.metrics] ['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae', 'out_1_acc', 'mean'] | | | 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'] | | | non_trainable_weights | List of all non-trainable weights tracked by this layer.Non-trainable weights are not updated during training. They are expected to be updated manually in call(). | | | output | Retrieves the output tensor(s) of a layer.Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer. | | | run_eagerly | Settable attribute indicating whether the model should run eagerly.Running eagerly means that your model will be run step by step, like Python code. Your model might run slower, but it should become easier for you to debug it by stepping into individual layer calls. By default, we will attempt to compile your model to a static graph to deliver the best execution performance. | | | steps_per_execution | Settable steps_per_execution variable. Requires a compiled model. supports_masking Whether this layer supports computing a mask usingcompute_mask. trainable` | | | trainable_weights | List of all trainable weights tracked by this layer.Trainable weights are updated via gradient descent during training. | | | variable_dtype | Alias of Layer.dtype, the dtype of the weights. | | | weights | Returns the list of all layer variables/weights. | |

Methods

add_loss

add_loss(
    losses, **kwargs
)

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries inlayer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model's callfunction, in which case losses should be a Tensor or list of Tensors.

Example:

class MyLayer(tf.keras.layers.Layer):
  def call(self, inputs):
    self.add_loss(tf.abs(tf.reduce_mean(inputs)))
    return inputs

The same code works in distributed training: the input to add_loss()is treated like a regularization loss and averaged across replicas by the training loop (both built-in Model.fit() and compliant custom training loops).

The add_loss method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model's Inputs. These losses become part of the model's topology and are tracked inget_config.

Example:

inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Activity regularization.
model.add_loss(tf.abs(tf.reduce_mean(x)))

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model's layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model's topology since they can't be serialized.

Example:

inputs = tf.keras.Input(shape=(10,))
d = tf.keras.layers.Dense(10)
x = d(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Weight regularization.
model.add_loss(lambda: tf.reduce_mean(d.kernel))
Args
losses Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor.
**kwargs Used for backwards compatibility only.

build

build(
    input_shape
)

Builds the model based on input shapes received.

This is to be used for subclassed models, which do not know at instantiation time what their inputs look like.

This method only exists for users who want to call model.build() in a standalone way (as a substitute for calling the model on real data to build it). It will never be called by the framework (and thus it will never throw unexpected errors in an unrelated workflow).

Args
input_shape Single tuple, TensorShape instance, or list/dict of shapes, where shapes are tuples, integers, or TensorShapeinstances.
Raises
ValueError In case of invalid user-provided data (not of type tuple, list, TensorShape, or dict). If the model requires call arguments that are agnostic to the input shapes (positional or keyword arg in call signature). If not all layers were properly built. If float type inputs are not supported within the layers. In each of these cases, the user should build their model by calling it on real tensor data.

build_from_config

build_from_config(
    config
)

Builds the layer's states with the supplied config dict.

By default, this method calls the build(config["input_shape"]) method, which creates weights based on the layer's input shape in the supplied config. If your config contains other information needed to load the layer's state, you should override this method.

Args
config Dict containing the input shape associated with this layer.

call

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

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

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

Args
inputs Input tensor, or dict/list/tuple of input tensors.
training Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.
mask A mask or list of masks. A mask can be either a boolean tensor or None (no mask). For more details, check the guidehere.
Returns
A tensor if there is a single output, or a list of tensors if there are more than one outputs.

compile

compile(
    optimizer='rmsprop',
    loss=None,
    metrics=None,
    loss_weights=None,
    weighted_metrics=None,
    run_eagerly=None,
    steps_per_execution=None,
    jit_compile=None,
    pss_evaluation_shards=0,
    **kwargs
)

Configures the model for training.

Example:

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
              loss=tf.keras.losses.BinaryCrossentropy(),
              metrics=[tf.keras.metrics.BinaryAccuracy(),
                       tf.keras.metrics.FalseNegatives()])
Args
optimizer String (name of optimizer) or optimizer instance. Seetf.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, andy_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.Metricinstance. See tf.keras.metrics. Typically you will usemetrics=['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 asmetrics={'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 asmetrics=[['accuracy'], ['accuracy', 'mse']]or metrics=['accuracy', ['accuracy', 'mse']]. When you pass the strings 'accuracy' or 'acc', we convert this to one oftf.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 bysample_weight or class_weight during training and testing.
run_eagerly Bool. If True, this Model's logic will not be wrapped in a tf.function. Recommended to leave this as Noneunless your Model cannot be run inside a tf.function.run_eagerly=True is not supported when usingtf.distribute.experimental.ParameterServerStrategy. Defaults toFalse.
steps_per_execution Int or 'auto'. The number of batches to run during each tf.function call. If set to "auto", keras will automatically tune steps_per_execution during runtime. Running multiple batches inside a single tf.function call can greatly improve performance on TPUs, when used with distributed strategies such as ParameterServerStrategy, or with small models with a large Python overhead. At most, one full epoch will be run each execution. If a number larger than the size of the epoch is passed, the execution will be truncated to the size of the epoch. Note that if steps_per_execution is set to N,Callback.on_batch_begin and Callback.on_batch_end methods will only be called every N batches (i.e. before/after eachtf.function execution). Defaults to 1.
jit_compile If True, compile the model training step with XLA.XLA is an optimizing compiler for machine learning.jit_compile is not enabled for by default. Note that jit_compile=Truemay not necessarily work for all models. For more information on supported operations please refer to theXLA documentation. Also refer toknown XLA issuesfor more details.
pss_evaluation_shards Integer or 'auto'. Used fortf.distribute.ParameterServerStrategy training only. This arg sets the number of shards to split the dataset into, to enable an exact visitation guarantee for evaluation, meaning the model will be applied to each dataset element exactly once, even if workers fail. The dataset must be sharded to ensure separate workers do not process the same data. The number of shards should be at least the number of workers for good performance. A value of 'auto' turns on exact evaluation and uses a heuristic for the number of shards based on the number of workers. 0, meaning no visitation guarantee is provided. NOTE: Custom implementations ofModel.test_step will be ignored when doing exact evaluation. Defaults to 0.
**kwargs Arguments supported for backwards compatibility only.

compile_from_config

compile_from_config(
    config
)

Compiles the model with the information given in config.

This method uses the information in the config (optimizer, loss, metrics, etc.) to compile the model.

Args
config Dict containing information for compiling the model.

compute_loss

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

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

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

Example:

class MyModel(tf.keras.Model):

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

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

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

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

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

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

optimizer = tf.keras.optimizers.SGD()
model.compile(optimizer, loss='mse', steps_per_execution=10)
model.fit(dataset, epochs=2, steps_per_epoch=10)
print('My custom loss: ', model.loss_tracker.result().numpy())
Args
x Input data.
y Target data.
y_pred Predictions returned by the model (output of model(x))
sample_weight Sample weights for weighting the loss function.
Returns
The total loss as a tf.Tensor, or None if no loss results (which is the case when called by Model.test_step).

compute_mask

compute_mask(
    inputs, mask=None
)

Computes an output mask tensor.

Args
inputs Tensor or list of tensors.
mask Tensor or list of tensors.
Returns
None or a tensor (or list of tensors, one per output tensor of the layer).

compute_metrics

compute_metrics(
    x, y, y_pred, sample_weight
)

Update metric states and collect all metrics to be returned.

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

Example:

class MyModel(tf.keras.Sequential):

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

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

    # Note that `self.custom_metric` is not listed in `self.metrics`.
    self.custom_metric.update_state(x, y, y_pred, sample_weight)
    metric_results['custom_metric_name'] = self.custom_metric.result()
    return metric_results
Args
x Input data.
y Target data.
y_pred Predictions returned by the model (output of model.call(x))
sample_weight Sample weights for weighting the loss function.
Returns
A dict containing values that will be passed totf.keras.callbacks.CallbackList.on_train_batch_end(). Typically, the values of the metrics listed in self.metrics are returned. Example:{'loss': 0.2, 'accuracy': 0.7}.

compute_output_shape

compute_output_shape(
    input_shape
)

Computes the output shape of the layer.

This method will cause the layer's state to be built, if that has not happened before. This requires that the layer will later be used with inputs that match the input shape provided here.

Args
input_shape Shape tuple (tuple of integers) or tf.TensorShape, or structure of shape tuples / tf.TensorShape instances (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.
Returns
A tf.TensorShape instance or structure of tf.TensorShape instances.

count_params

count_params()

Count the total number of scalars composing the weights.

Returns
An integer count.
Raises
ValueError if the layer isn't yet built (in which case its weights aren't yet defined).

evaluate

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

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

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

Args
x Input data. It could be: 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" becomes 1 for most cases, and to 2 when used withParameterServerStrategy. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (e.g. in a production environment). Defaults to 'auto'.
sample_weight Optional Numpy array of weights for the test samples, used for weighting the loss function. You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. This argument is not supported whenx 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 stepsis 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. Seecallbacks.
max_queue_size Integer. Used for generator orkeras.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 orkeras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default toFalse. Note that because this implementation relies on multiprocessing, you should not pass non-pickleable arguments to the generator as they can't be passed easily to children processes.
return_dict If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.
**kwargs Unused at this time.

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

Returns
Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.
Raises
RuntimeError If model.evaluate is wrapped in a tf.function.

export

export(
    filepath
)

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

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

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

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

Example:

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

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

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

fit

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

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

Args
x Input data. It could be: 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 typetf.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 themetrics argument in compile(). If usingtf.distribute.experimental.ParameterServerStrategy, onlyDatasetCreator 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.Sequenceinstances (since they generate batches).
epochs Integer. Number of epochs to train the model. An epoch is an iteration over the entire x and ydata provided (unless the steps_per_epoch flag is set to something other than None). Note that in conjunction with initial_epoch,epochs is to be understood as "final epoch". The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs is reached.
verbose 'auto', 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. 'auto' becomes 1 for most cases, but 2 when used withParameterServerStrategy. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (eg, in a production environment). Defaults to 'auto'.
callbacks List of keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks. Notetf.keras.callbacks.ProgbarLogger andtf.keras.callbacks.History callbacks are created automatically and need not be passed into model.fit.tf.keras.callbacks.ProgbarLogger is created or not based onverbose argument to model.fit. Callbacks with batch-level calls are currently unsupported withtf.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 orkeras.utils.Sequence instance. If both validation_data and validation_split are provided,validation_data will override validation_split.validation_split is not yet supported withtf.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 usingvalidation_split or validation_data is not affected by regularization layers like noise and dropout.validation_data will override validation_split.validation_data could be: A tuple (x_val, y_val) of Numpy arrays or tensors. A tuple (x_val, y_val, val_sample_weights) of NumPy arrays. A tf.data.Dataset. A Python generator or keras.utils.Sequence returning(inputs, targets) or (inputs, targets, sample_weights).validation_data is not yet supported withtf.distribute.experimental.ParameterServerStrategy.
shuffle Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). This argument is ignored when x is a generator or an object of tf.data.Dataset. 'batch' is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized chunks. Has no effect when steps_per_epoch is not None.
class_weight Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class. When class_weight is specified and targets have a rank of 2 or greater, either y must be one-hot encoded, or an explicit final dimension of 1 must be included for sparse class labels.
sample_weight Optional Numpy array of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape(samples, sequence_length), to apply a different weight to every timestep of every sample. This argument is not supported when x is a dataset, generator, or keras.utils.Sequence instance, instead provide the sample_weights as the third element of x. Note that sample weighting does not apply to metrics specified via the metrics argument in compile(). To apply sample weighting to your metrics, you can specify them via theweighted_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 atf.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. Ifsteps_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.Sequenceinstances (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=2runs 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 orkeras.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, workerswill default to 1.
use_multiprocessing Boolean. Used for generator orkeras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default toFalse. Note that because this implementation relies on multiprocessing, you should not pass non-pickleable arguments to the generator as they can't be passed easily to children processes.

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

Returns
A History object. Its History.history attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).
Raises
RuntimeError If the model was never compiled or, 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.

from_config

View source

@classmethod from_config( config, custom_objects=None )

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config A Python dictionary, typically the output of get_config.
Returns
A layer instance.

get_build_config

get_build_config()

Returns a dictionary with the layer's input shape.

This method returns a config dict that can be used bybuild_from_config(config) to create all states (e.g. Variables and Lookup tables) needed by the layer.

By default, the config only contains the input shape that the layer was built with. If you're writing a custom layer that creates state in an unusual way, you should override this method to make sure this state is already created when Keras attempts to load its value upon model loading.

Returns
A dict containing the input shape associated with the layer.

get_compile_config

get_compile_config()

Returns a serialized config with information for compiling the model.

This method returns a config dictionary containing all the information (optimizer, loss, metrics, etc.) with which the model was compiled.

Returns
A dict containing information for compiling the model.

get_config

View source

get_config()

Returns the config of the Model.

Config is a Python dictionary (serializable) containing the configuration of an object, which in this case is a Model. This allows the Model to be be reinstantiated later (without its trained weights) from this configuration.

Note that get_config() does not guarantee to return a fresh copy of dict every time it is called. The callers should make a copy of the returned dict if they want to modify it.

Developers of subclassed Model are advised to override this method, and continue to update the dict from super(MyModel, self).get_config()to provide the proper configuration of this Model. The default config will return config dict for init parameters if they are basic types. Raises NotImplementedError when in cases where a customget_config() implementation is required for the subclassed model.

Returns
Python dictionary containing the configuration of this Model.

get_layer

get_layer(
    name=None, index=None
)

Retrieves a layer based on either its name (unique) or index.

If name and index are both provided, index will take precedence. Indices are based on order of horizontal graph traversal (bottom-up).

Args
name String, name of layer.
index Integer, index of layer.
Returns
A layer instance.

get_metrics_result

get_metrics_result()

Returns the model's metrics values as a dict.

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

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

get_weight_paths

get_weight_paths()

Retrieve all the variables and their paths for the model.

The variable path (string) is a stable key to identify a tf.Variableinstance 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.Variableinstances.

Example:

class SubclassModel(tf.keras.Model):

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

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

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

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

get_weights

get_weights()

Retrieves the weights of the model.

Returns
A flat list of Numpy arrays.

load_own_variables

load_own_variables(
    store
)

Loads the state of the layer.

You can override this method to take full control of how the state of the layer is loaded upon calling keras.models.load_model().

Args
store Dict from which the state of the model will be loaded.

load_weights

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

Loads all layer weights from a saved files.

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

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

Partial weight loading

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

Weight loading by name

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

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

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

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

make_predict_function

make_predict_function(
    force=False
)

Creates a function that executes one step of inference.

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

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

This function is cached the first time Model.predict orModel.predict_on_batch is called. The cache is cleared wheneverModel.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 atf.data.Iterator, and return the outputs of the Model.

make_test_function

make_test_function(
    force=False
)

Creates a function that executes one step of evaluation.

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

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

This function is cached the first time Model.evaluate orModel.test_on_batch is called. The cache is cleared wheneverModel.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 atf.data.Iterator, and return a dict containing values that will be passed to tf.keras.Callbacks.on_test_batch_end.

make_train_function

make_train_function(
    force=False
)

Creates a function that executes one step of training.

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

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

This function is cached the first time Model.fit orModel.train_on_batch is called. The cache is cleared wheneverModel.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 atf.data.Iterator, and return a dict containing values that will be passed to tf.keras.Callbacks.on_train_batch_end, such as{'loss': 0.2, 'accuracy': 0.7}.

predict

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

Generates output predictions for the input samples.

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

For small numbers of inputs that fit in one batch, directly use __call__() for faster execution, e.g.,model(x), or model(x, training=False) if you have layers such astf.keras.layers.BatchNormalization that behave differently during inference. You may pair the individual model call with a tf.functionfor 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" becomes 1 for most cases, and to 2 when used withParameterServerStrategy. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (e.g. in a production environment). Defaults to 'auto'.
steps Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of None. If x is a tf.datadataset 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 orkeras.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 orkeras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default toFalse. Note that because this implementation relies on multiprocessing, you should not pass non-pickleable arguments to the generator as they can't be passed easily to children processes.

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

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

predict_on_batch

predict_on_batch(
    x
)

Returns predictions for a single batch of samples.

Args
x Input data. It could be: 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 atf.function.

predict_step

predict_step(
    data
)

The logic for one inference step.

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

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

Configuration details for how this logic is run (e.g. tf.functionand tf.distribute.Strategy settings), should be left toModel.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 theModel on data.

reset_metrics

reset_metrics()

Resets the state of all the metrics in the model.

Examples:

inputs = tf.keras.layers.Input(shape=(3,)) outputs = tf.keras.layers.Dense(2)(inputs) model = tf.keras.models.Model(inputs=inputs, outputs=outputs) model.compile(optimizer="Adam", loss="mse", metrics=["mae"])

x = np.random.random((2, 3)) y = np.random.randint(0, 2, (2, 2)) _ = model.fit(x, y, verbose=0) assert all(float(m.result()) for m in model.metrics)

model.reset_metrics() assert all(float(m.result()) == 0 for m in model.metrics)

reset_states

reset_states()

save

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

Saves a model as a TensorFlow SavedModel or HDF5 file.

See the Serialization and Saving guide for details.

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

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

Example:

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

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

save_own_variables

save_own_variables(
    store
)

Saves the state of the layer.

You can override this method to take full control of how the state of the layer is saved upon calling model.save().

Args
store Dict where the state of the model will be saved.

save_spec

save_spec(
    dynamic_batch=True
)

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

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

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

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

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

model.save(path, signatures={
  'serving_default': serve.get_concrete_function(*arg_specs,
                                                 **kwarg_specs)
})
Args
dynamic_batch Whether to set the batch sizes of all the returnedtf.TensorSpec to None. (Note that when defining functional or Sequential models with tf.keras.Input([...], batch_size=X), the batch size will always be preserved). Defaults to True.
Returns
If the model inputs are defined, returns a tuple (args, kwargs). All elements in args and kwargs are tf.TensorSpec. If the model inputs are not defined, returns None. The model inputs are automatically set when calling the model,model.fit, model.evaluate or model.predict.

save_weights

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

Saves all layer weights.

Either saves in HDF5 or in TensorFlow format based on the save_formatargument.

When saving in HDF5 format, the weight file has:

When saving in TensorFlow format, all objects referenced by the network are saved in the same format as tf.train.Checkpoint, including anyLayer instances or Optimizer instances assigned to object attributes. For networks constructed from inputs and outputs usingtf.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 oftf.train.Checkpoint and tf.keras.Model for details.

While the formats are the same, do not mix save_weights andtf.train.Checkpoint. Checkpoints saved by Model.save_weights should be loaded using Model.load_weights. Checkpoints saved usingtf.train.Checkpoint.save should be restored using the correspondingtf.train.Checkpoint.restore. Prefer tf.train.Checkpoint oversave_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.savethis 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 theguide to training checkpoints for details on the TensorFlow format.

Args
filepath String or PathLike, path to the file to save the weights to. When saving in TensorFlow format, this is the prefix used for checkpoint files (multiple files are generated). Note that the '.h5' suffix causes weights to be saved in HDF5 format.
overwrite Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt.
save_format Either 'tf' or 'h5'. A filepath ending in '.h5' or '.keras' will default to HDF5 if save_format is None. Otherwise, None becomes 'tf'. Defaults to None.
options Optional tf.train.CheckpointOptions object that specifies options for saving weights.
Raises
ImportError If h5py is not available when attempting to save in HDF5 format.

set_weights

set_weights(
    weights
)

Sets the weights of the layer, from NumPy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer's weights must be instantiated before calling this function, by calling the layer.

For example, a Dense layer returns a list of two values: the kernel matrix and the bias vector. These can be used to set the weights of another Dense layer:

layer_a = tf.keras.layers.Dense(1, kernel_initializer=tf.constant_initializer(1.)) a_out = layer_a(tf.convert_to_tensor([[1., 2., 3.]])) layer_a.get_weights() [array([[1.], [1.], [1.]], dtype=float32), array([0.], dtype=float32)] layer_b = tf.keras.layers.Dense(1, kernel_initializer=tf.constant_initializer(2.)) b_out = layer_b(tf.convert_to_tensor([[10., 20., 30.]])) layer_b.get_weights() [array([[2.], [2.], [2.]], dtype=float32), array([0.], dtype=float32)] layer_b.set_weights(layer_a.get_weights()) layer_b.get_weights() [array([[1.], [1.], [1.]], dtype=float32), array([0.], dtype=float32)]

Args
weights a list of NumPy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights).
Raises
ValueError If the provided weights list does not match the layer's specifications.

summary

summary(
    line_length=None,
    positions=None,
    print_fn=None,
    expand_nested=False,
    show_trainable=False,
    layer_range=None
)

Prints a string summary of the network.

Args
line_length Total length of printed lines (e.g. set this to adapt the display to different terminal window sizes).
positions Relative or absolute positions of log elements in each line. If not provided, becomes[0.3, 0.6, 0.70, 1.]. Defaults to None.
print_fn Print function to use. By default, prints to stdout. If stdout doesn't work in your environment, change to print. It will be called on each line of the summary. You can set it to a custom function in order to capture the string summary.
expand_nested Whether to expand the nested models. Defaults to False.
show_trainable Whether to show if a layer is trainable. Defaults to False.
layer_range a list or tuple of 2 strings, which is the starting layer name and ending layer name (both inclusive) indicating the range of layers to be printed in summary. It also accepts regex patterns instead of exact name. In such case, start predicate will be the first element it matches to layer_range[0] and the end predicate will be the last element it matches to layer_range[1]. By default None which considers all layers of model.
Raises
ValueError if summary() is called before the model is built.

test_on_batch

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

Test the model on a single batch of samples.

Args
x Input data. It could be: 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 atf.function.

test_step

test_step(
    data
)

The logic for one evaluation step.

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

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

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

Args
data A nested structure of Tensors.
Returns
A dict containing values that will be passed totf.keras.callbacks.CallbackList.on_train_batch_end. Typically, the values of the Model's metrics are returned.

to_json

to_json(
    **kwargs
)

Returns a JSON string containing the network configuration.

To load a network from a JSON save file, usekeras.models.model_from_json(json_string, custom_objects={}).

Args
**kwargs Additional keyword arguments to be passed to *json.dumps().
Returns
A JSON string.

to_yaml

to_yaml(
    **kwargs
)

Returns a yaml string containing the network configuration.

To load a network from a yaml save file, usekeras.models.model_from_yaml(yaml_string, custom_objects={}).

custom_objects should be a dictionary mapping the names of custom losses / layers / etc to the corresponding functions / classes.

Args
**kwargs Additional keyword arguments to be passed to yaml.dump().
Returns
A YAML string.
Raises
RuntimeError announces that the method poses a security risk

train_on_batch

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

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

Args
x Input data. It could be: 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. When class_weight is specified and targets have a rank of 2 or greater, either y must be one-hot encoded, or an explicit final dimension of 1 must be included for sparse class labels.
reset_metrics If True, the metrics returned will be only for this batch. If False, the metrics will be statefully accumulated across batches.
return_dict If True, loss and metric results are returned as a dict, with each key being the name of the metric. If False, they are returned as a list.
Returns
Scalar training loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute model.metrics_names will give you the display labels for the scalar outputs.
Raises
RuntimeError If model.train_on_batch is wrapped in a tf.function.

train_step

train_step(
    data
)

The logic for one training step.

This method can be overridden to support custom training logic. For concrete examples of how to override this method seeCustomizing 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.functionand tf.distribute.Strategy settings), should be left toModel.make_train_function, which can also be overridden.

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

__call__

__call__(
    *args, **kwargs
)

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Last updated 2024-02-02 UTC.