open3d.ml.tf.models.BaseModel

class open3d.ml.tf.models.BaseModel(*args, **kwargs)

Base class for models.

All models must inherit from this class and implement all functions to be used with a pipeline.

Parameters

**kwargs – Configuration of the model as keyword arguments.

__init__(**kwargs)

Initialize self. See help(type(self)) for accurate signature.

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 in layer.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 call function, in which case losses should be a Tensor or list of Tensors.

Example:

```python class MyLayer(tf.keras.layers.Layer):

def call(self, inputs):

self.add_loss(tf.abs(tf.reduce_mean(inputs))) return inputs

```

This 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 Input`s. These losses become part of the model’s topology and are tracked in `get_config.

Example:

`python 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:

`python 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)) `

Parameters

  • losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor.

  • **kwargs –

    Additional keyword arguments for backward compatibility. Accepted values:

    inputs - Deprecated, will be automatically inferred.

add_metric(value, name=None, **kwargs)

Adds metric tensor to the layer.

This method can be used inside the call() method of a subclassed layer or model.

```python class MyMetricLayer(tf.keras.layers.Layer):

def __init__(self):

super(MyMetricLayer, self).__init__(name=’my_metric_layer’) self.mean = metrics_module.Mean(name=’metric_1’)

def call(self, inputs):

self.add_metric(self.mean(x)) self.add_metric(math_ops.reduce_sum(x), name=’metric_2’) return inputs

```

This method can also be called directly on a Functional Model during construction. In this case, any tensor passed to this Model must be symbolic and be able to be traced back to the model’s Input`s. These metrics become part of the model’s topology and are tracked when you save the model via `save().

`python 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) model.add_metric(math_ops.reduce_sum(x), name='metric_1') `

Note: Calling add_metric() with the result of a metric object on a Functional Model, as shown in the example below, is not supported. This is because we cannot trace the metric result tensor back to the model’s inputs.

`python 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) model.add_metric(tf.keras.metrics.Mean()(x), name='metric_1') `

Parameters

  • value – Metric tensor.

  • name – String metric name.

  • **kwargs – Additional keyword arguments for backward compatibility. Accepted values: aggregation - When the value tensor provided is not the result of calling a keras.Metric instance, it will be aggregated by default using a keras.Metric.Mean.

add_update(updates, inputs=None)

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) 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 in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters

  • updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode.

  • inputs – Deprecated, will be automatically inferred.

add_variable(*args, **kwargs)

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)

Adds a new variable to the layer.

Parameters

  • name – Variable name.

  • shape – Variable shape. Defaults to scalar if unspecified.

  • dtype – The type of the variable. Defaults to self.dtype or float32.

  • initializer – Initializer instance (callable).

  • regularizer – Regularizer instance (callable).

  • trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ.

  • constraint – Constraint instance (callable).

  • partitioner – Partitioner to be passed to the Trackable API.

  • use_resource – Whether to use ResourceVariable.

  • synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True.

  • aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation.

  • **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device.

Returns

The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned.

Raises

  • RuntimeError – If called with partitioned variable regularization and eager execution is enabled.

  • ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.

apply(inputs, *args, **kwargs)

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters

  • inputs – Input tensor(s).

  • *args – additional positional arguments to be passed to self.call.

  • **kwargs – additional keyword arguments to be passed to self.call.

Returns

Output tensor(s).

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).

Parameters

input_shape – Single tuple, TensorShape, or list of shapes, where shapes are tuples, integers, or TensorShapes.

Raises

  • ValueError –

    1. In case of invalid user-provided data (not of type tuple, list, or TensorShape). 2. If the model requires call arguments that are agnostic to the input shapes (positional or kwarg in call signature). 3. If not all layers were properly built. 4. 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. –

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

Calls the model on new inputs.

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).

Parameters

  • inputs – A tensor or list of 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 tensor or None (no mask).

Returns

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

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

Configures the model for training.

Parameters

  • optimizer – String (name of optimizer) or optimizer instance. See tf.keras.optimizers.

  • loss – String (name of objective function), objective function or tf.keras.losses.Loss instance. See tf.keras.losses. An objective function is any callable with the signature loss = fn(y_true, y_pred), where y_true = ground truth values with shape = [batch_size, d0, .. dN], except sparse loss functions such as sparse categorical crossentropy where shape = [batch_size, d0, .. dN-1]. y_pred = predicted values with shape = [batch_size, d0, .. dN]. It returns a weighted loss float tensor. If a custom Loss instance is used and reduction is set to NONE, return value has the shape [batch_size, d0, .. dN-1] ie. 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.

  • 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 (len = len(outputs)) of lists of metrics 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 loss function used and the model output shape. We do a similar conversion for the strings ‘crossentropy’ and ‘ce’ as well.

  • 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.

  • **kwargs –

    Any additional arguments. Supported arguments: - experimental_steps_per_execution: Int. 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. Note that if this value is set to N, Callback.on_batch methods will only be called every N batches. This currently defaults to 1. 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.

    • sample_weight_mode for backward compatibility.

Raises

ValueError – In case of invalid arguments for optimizer, loss or metrics.

compute_mask(inputs, mask=None)

Computes an output mask tensor.

Parameters

  • 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_output_shape(input_shape)

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters

input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.

Returns

An input shape tuple.

compute_output_signature(input_signature)

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters

input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer.

Returns

Single TensorSpec or nested structure of TensorSpec objects, describing

how the layer would transform the provided input.

Raises

TypeError – If input_signature contains a non-TensorSpec object.

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(x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, return_dict=False)

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

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

Parameters

  • 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 – 0 or 1. Verbosity mode. 0 = silent, 1 = progress bar.

  • sample_weight –

    Optional Numpy array of weights for the test samples, used for weighting the loss function. You can either pass a flat (1D) Numpy array with the same length as the input samples

    (1:1 mapping between weights and samples), or in the case of

    temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. 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](/api_docs/python/tf/keras/callbacks).

  • max_queue_size – Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.

  • workers – Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using process-based threading. If unspecified, workers will default to 1. If 0, will execute the generator on the main thread.

  • use_multiprocessing – Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can’t be passed easily to children processes.

  • 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.

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 tf.function.

  • ValueError – in case of invalid arguments.

evaluate_generator(generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)

Evaluates the model on a data generator. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use Model.evaluate, which supports generators.

DEPRECATED:

Model.evaluate now supports generators, so there is no longer any need to use this endpoint.

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

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

Parameters

  • 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 below.

  • 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. Note that in conjunction with initial_epoch, epochs is to be understood as “final epoch”. The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs is reached.

  • verbose – 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (eg, in a production environment).

  • callbacks – List of keras.callbacks.Callback instances. List of callbacks to apply during training. See tf.keras.callbacks.

  • validation_split –

    Float between 0 and 1.

    Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the x and y data provided, before shuffling. This argument is not supported when x is a dataset, generator or

    keras.utils.Sequence instance.

  • validation_data –

    Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. 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 dropuout. validation_data will override validation_split. validation_data could be:

    • tuple (x_val, y_val) of Numpy arrays or tensors

    • tuple (x_val, y_val, val_sample_weights) of Numpy arrays

    • dataset

    For the first two cases, batch_size must be provided. For the last case, validation_steps could be provided. Note that validation_data does not support all the data types that are supported in x, eg, dict, generator or keras.utils.Sequence.

  • shuffle – Boolean (whether to shuffle the training data before each epoch) or str (for ‘batch’). This argument is ignored when x is a generator. ‘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.

  • 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. This argument is not supported with array inputs.

  • validation_steps – Only relevant if validation_data is provided and is a tf.data dataset. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch. If ‘validation_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. If 0, will execute the generator on the main thread.

  • use_multiprocessing – Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can’t be passed easily to children processes.

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.

fit_generator(generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, validation_data=None, validation_steps=None, validation_freq=1, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0)

Fits the model on data yielded batch-by-batch by a Python generator. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use Model.fit, which supports generators.

DEPRECATED:

Model.fit now supports generators, so there is no longer any need to use this endpoint.

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).

Parameters

config – A Python dictionary, typically the output of get_config.

Returns

A layer instance.

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns

Python dictionary.

get_input_at(node_index)

Retrieves the input tensor(s) of a layer at a given node.

Parameters

node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns

A tensor (or list of tensors if the layer has multiple inputs).

Raises

RuntimeError – If called in Eager mode.

get_input_mask_at(node_index)

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters

node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns

A mask tensor (or list of tensors if the layer has multiple inputs).

get_input_shape_at(node_index)

Retrieves the input shape(s) of a layer at a given node.

Parameters

node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns

A shape tuple (or list of shape tuples if the layer has multiple inputs).

Raises

RuntimeError – If called in Eager mode.

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).

Parameters

  • name – String, name of layer.

  • index – Integer, index of layer.

Returns

A layer instance.

Raises

ValueError – In case of invalid layer name or index.

get_loss(Loss, results, inputs)

Computes the loss given the network input and outputs.

Parameters

  • Loss – A loss object.

  • results – This is the output of the model.

  • inputs – This is the input to the model.

Returns

Returns the loss value.

get_losses_for(inputs)

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.losses instead.

Retrieves losses relevant to a specific set of inputs.

Parameters

inputs – Input tensor or list/tuple of input tensors.

Returns

List of loss tensors of the layer that depend on inputs.

abstract get_optimizer(cfg_pipeline)

Returns an optimizer object for the model.

Parameters

cfg_pipeline – A Config object with the configuration of the pipeline.

Returns

Returns a new optimizer object.

get_output_at(node_index)

Retrieves the output tensor(s) of a layer at a given node.

Parameters

node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns

A tensor (or list of tensors if the layer has multiple outputs).

Raises

RuntimeError – If called in Eager mode.

get_output_mask_at(node_index)

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters

node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns

A mask tensor (or list of tensors if the layer has multiple outputs).

get_output_shape_at(node_index)

Retrieves the output shape(s) of a layer at a given node.

Parameters

node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns

A shape tuple (or list of shape tuples if the layer has multiple outputs).

Raises

RuntimeError – If called in Eager mode.

get_updates_for(inputs)

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.updates instead.

Retrieves updates relevant to a specific set of inputs.

Parameters

inputs – Input tensor or list/tuple of input tensors.

Returns

List of update ops of the layer that depend on inputs.

get_weights()

Retrieves the weights of the model.

Returns

A flat list of Numpy arrays.

abstract inference_begin(data)

Function called right before running inference.

Parameters

data – A data from the dataset.

abstract inference_end(results)

This function is called after the inference.

This function can be implemented to apply post-processing on the network outputs.

Parameters

results – The model outputs as returned by the call() function. Post-processing is applied on this object.

Returns

Returns True if the inference is complete and otherwise False. Returning False can be used to implement inference for large point clouds which require multiple passes.

abstract inference_preprocess()

This function prepares the inputs for the model

Returns

The inputs to be consumed by the call() function of the model.

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

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

If by_name is False 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.

If by_name is True, 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.

Only topological loading (by_name=False) is supported when loading weights from the TensorFlow format. Note that topological loading differs slightly between TensorFlow and HDF5 formats for user-defined classes inheriting from tf.keras.Model: HDF5 loads based on a flattened list of weights, while the TensorFlow format loads based on the object-local names of attributes to which layers are assigned in the Model’s constructor.

Parameters

  • 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).

  • by_name – Boolean, whether to load weights by name or by topological order. Only topological loading is supported for weight files in TensorFlow format.

  • 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 weight (only valid when by_name=True).

  • options – Optional tf.train.CheckpointOptions object that specifies options for loading weights.

Returns

When loading a weight file in TensorFlow format, returns the same status object as tf.train.Checkpoint.restore. When graph building, restore ops are run automatically as soon as the network is built (on first call for user-defined classes inheriting from Model, immediately if it is already built).

When loading weights in HDF5 format, returns None.

Raises

  • ImportError – If h5py is not available and the weight file is in HDF5 format.

  • ValueError – If skip_mismatch is set to True when by_name is False.

make_predict_function()

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.

Returns

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

make_test_function()

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.

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()

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.

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(x, batch_size=None, verbose=0, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)

Generates output predictions for the input samples.

Computation is done in batches. This method is designed for performance in large scale inputs. For small amount of inputs that fit in one batch, directly using __call__ is recommended for faster execution, e.g., model(x), or model(x, training=False) if you have layers such as tf.keras.layers.BatchNormalization that behaves differently during inference. Also, note the fact that test loss is not affected by regularization layers like noise and dropout.

Parameters

  • 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 – Verbosity mode, 0 or 1.

  • steps – Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of None. If x is a tf.data dataset and steps is None, predict will run until the input dataset is exhausted.

  • callbacks – List of keras.callbacks.Callback instances. List of callbacks to apply during prediction. See [callbacks](/api_docs/python/tf/keras/callbacks).

  • max_queue_size – Integer. Used for generator or keras.utils.Sequence input only. Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.

  • workers – Integer. Used for generator or keras.utils.Sequence input only. Maximum number of processes to spin up when using process-based threading. If unspecified, workers will default to 1. If 0, will execute the generator on the main thread.

  • use_multiprocessing – Boolean. Used for generator or keras.utils.Sequence input only. If True, use process-based threading. If unspecified, use_multiprocessing will default to False. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can’t be passed easily to children processes.

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 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_generator(generator, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False, verbose=0)

Generates predictions for the input samples from a data generator. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use Model.predict, which supports generators.

DEPRECATED:

Model.predict now supports generators, so there is no longer any need to use this endpoint.

predict_on_batch(x)

Returns predictions for a single batch of samples.

Parameters

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 tf.function.

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

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 mathemetical 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.

Parameters

data – A nested structure of `Tensor`s.

Returns

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

abstract preprocess(data, attr)

Data preprocessing function.

This function is called before training to preprocess the data from a dataset.

Parameters

  • data – A sample from the dataset.

  • attr – The corresponding attributes.

Returns

Returns the preprocessed data

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()
save(filepath, overwrite=True, include_optimizer=True, save_format=None, signatures=None, options=None)

Saves the model to Tensorflow SavedModel or a single HDF5 file.

The savefile includes:

  • The model architecture, allowing to re-instantiate the model.

  • The model weights.

  • The state of the optimizer, allowing to resume training

    exactly where you left off.

This allows you to save the entirety of the state of a model in a single file.

Saved models can be reinstantiated via keras.models.load_model. The model returned by load_model is a compiled model ready to be used (unless the saved model was never compiled in the first place).

Models built with the Sequential and Functional API can be saved to both the HDF5 and SavedModel formats. Subclassed models can only be saved with the SavedModel format.

Note that the model weights may have different scoped names after being loaded. Scoped names include the model/layer names, such as “dense_1/kernel:0”. It is recommended that you use the layer properties to

access specific variables, e.g. model.get_layer(“dense_1”).kernel.

Parameters

  • filepath – String, PathLike, path to SavedModel or H5 file to save the model.

  • overwrite – Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt.

  • include_optimizer – If True, save optimizer’s state together.

  • save_format – Either ‘tf’ or ‘h5’, indicating whether to save the model to Tensorflow SavedModel or HDF5. Defaults to ‘tf’ in TF 2.X, and ‘h5’ in TF 1.X.

  • signatures – Signatures to save with the SavedModel. Applicable to the ‘tf’ format only. Please see the signatures argument in tf.saved_model.save for details.

  • options – Optional tf.saved_model.SaveOptions object that specifies options for saving to SavedModel.

Example:

```python from keras.models import load_model

model.save(‘my_model.h5’) # creates a HDF5 file ‘my_model.h5’ del model # deletes the existing model

# returns a compiled model # identical to the previous one model = load_model(‘my_model.h5’) ```

save_weights(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](https://www.tensorflow.org/guide/checkpoint) for details on the TensorFlow format.

Parameters

  • 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.

  • ValueError – For invalid/unknown format arguments.

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– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

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

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(line_length=None, positions=None, print_fn=None)

Prints a string summary of the network.

Parameters

  • line_length – Total length of printed lines (e.g. set this to adapt the display to different terminal window sizes).

  • positions – Relative or absolute positions of log elements in each line. If not provided, defaults to [.33, .55, .67, 1.].

  • print_fn – Print function to use. Defaults to print. It will be called on each line of the summary. You can set it to a custom function in order to capture the string summary.

Raises

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

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

Test the model on a single batch of samples.

Parameters

  • 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 tf.function.

  • ValueError – In case of invalid user-provided arguments.

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 mathemetical 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.

Parameters

data – A nested structure of `Tensor`s.

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(**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={}).

Parameters

**kwargs – Additional keyword arguments to be passed to json.dumps().

Returns

A JSON string.

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.

Parameters

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

Returns

A YAML string.

Raises

ImportError – if yaml module is not found.

train_on_batch(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.

Parameters

  • 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.

  • 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 tf.function.

  • ValueError – In case of invalid user-provided arguments.

train_step(data)

The logic for one training step.

This method can be overridden to support custom training logic. This method is called by Model.make_train_function.

This method should contain the mathemetical 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.

Parameters

data – A nested structure of `Tensor`s.

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}.

abstract transform(*args)

Transform function for the point cloud and features.

Parameters

args – A list of tf Tensors.

classmethod with_name_scope(method)

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)

Using the above module would produce `tf.Variable`s and `tf.Tensor`s whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>
Parameters

method – The method to wrap.

Returns

The original method wrapped such that it enters the module’s name scope.

property activity_regularizer

Optional regularizer function for the output of this layer.

property distribute_strategy

The tf.distribute.Strategy this model was created under.

property dtype

Dtype used by the weights of the layer, set in the constructor.

property dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

property inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

property 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.

Returns

Input tensor or list of input tensors.

Raises

  • RuntimeError – If called in Eager mode.

  • AttributeError – If no inbound nodes are found.

property input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns

Input mask tensor (potentially None) or list of input mask tensors.

Raises

  • AttributeError – if the layer is connected to

  • more than one incoming layers. –

property input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns

Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).

Raises

  • AttributeError – if the layer has no defined input_shape.

  • RuntimeError – if called in Eager mode.

property 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__():

`python 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.

Returns

A tf.keras.layers.InputSpec instance, or nested structure thereof.

property layers
property 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 a tf.GradientTape will propagate gradients back to the corresponding variables.

Examples:

>>> 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.
>>> model.add_loss(tf.abs(tf.reduce_mean(x)))
>>> model.losses
[<tf.Tensor 'Abs:0' shape=() dtype=float32>]

>>> 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>]
Returns

A list of tensors.

property metrics

Returns the model’s metrics added using compile, add_metric APIs.

Note: Metrics passed to compile() are available only after a keras.Model has been trained/evaluated on actual data.

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"])
>>> [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']
property metrics_names

Returns the model’s display labels for all outputs.

Note: metrics_names are available only after a keras.Model has been trained/evaluated on actual data.

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"])
>>> 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']
property name

Name of the layer (string), set in the constructor.

property name_scope

Returns a tf.name_scope instance for this class.

property non_trainable_variables
property 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().

Returns

A list of non-trainable variables.

property outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

property 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.

Returns

Output tensor or list of output tensors.

Raises

  • AttributeError – if the layer is connected to more than one incoming layers.

  • RuntimeError – if called in Eager mode.

property output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns

Output mask tensor (potentially None) or list of output mask tensors.

Raises

  • AttributeError – if the layer is connected to

  • more than one incoming layers. –

property output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns

Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).

Raises

  • AttributeError – if the layer has no defined output shape.

  • RuntimeError – if called in Eager mode.

property 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.

Returns

Boolean, whether the model should run eagerly.

property state_updates

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: This property should not be used in TensorFlow 2.0, as updates are applied automatically.

Returns the updates from all layers that are stateful.

This is useful for separating training updates and state updates, e.g. when we need to update a layer’s internal state during prediction.

Returns

A list of update ops.

property stateful
property submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True
Returns

A sequence of all submodules.

property supports_masking

Whether this layer supports computing a mask using compute_mask.

property trainable
property trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns

A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).

property trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns

A list of trainable variables.

property updates

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: This property should not be used in TensorFlow 2.0, as updates are applied automatically.

property variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns

A list of variables.

property weights

Returns the list of all layer variables/weights.

Returns

A list of variables.