Distribution

class Distribution(*args, **kwargs)

Bases: Layer

build(input_shapes)

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, **E)

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Args:

inputs: Input tensor, or dict/list/tuple of input tensors.

The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.

• NumPy array or Python scalar values in inputs get cast as tensors.

• Keras mask metadata is only collected from inputs.

• Layers are built (build(input_shape) method) using shape info from inputs only.

• input_spec compatibility is only checked against inputs.

• Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

• The SavedModel input specification is generated using inputs only.

• Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args: Additional positional arguments. May contain tensors, although

this is not recommended, for the reasons above.

**kwargs: Additional keyword arguments. May contain tensors, although

this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.

• mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns:

A tensor or list/tuple of tensors.

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.

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).

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.

Returns:

Python dictionary.

class DistributionKLLoss(*args, **kwargs)

Bases: Layer

build(input_shapes)

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, **I)

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Args:

inputs: Input tensor, or dict/list/tuple of input tensors.

The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.

• NumPy array or Python scalar values in inputs get cast as tensors.

• Keras mask metadata is only collected from inputs.

• Layers are built (build(input_shape) method) using shape info from inputs only.

• input_spec compatibility is only checked against inputs.

• Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

• The SavedModel input specification is generated using inputs only.

• Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args: Additional positional arguments. May contain tensors, although

this is not recommended, for the reasons above.

**kwargs: Additional keyword arguments. May contain tensors, although

this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.

• mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns:

A tensor or list/tuple of tensors.

class DistributionMean(*args, **kwargs)

Bases: Layer

call(x, **B)

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Args:

inputs: Input tensor, or dict/list/tuple of input tensors.

The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.

• NumPy array or Python scalar values in inputs get cast as tensors.

• Keras mask metadata is only collected from inputs.

• Layers are built (build(input_shape) method) using shape info from inputs only.

• input_spec compatibility is only checked against inputs.

• Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

• The SavedModel input specification is generated using inputs only.

• Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args: Additional positional arguments. May contain tensors, although

this is not recommended, for the reasons above.

**kwargs: Additional keyword arguments. May contain tensors, although

this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.

• mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns:

A tensor or list/tuple of tensors.

class DistributionSampleLoss(*args, **kwargs)

Bases: Layer

call(x, **D)

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Args:

inputs: Input tensor, or dict/list/tuple of input tensors.

The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.

• NumPy array or Python scalar values in inputs get cast as tensors.

• Keras mask metadata is only collected from inputs.

• Layers are built (build(input_shape) method) using shape info from inputs only.

• input_spec compatibility is only checked against inputs.

• Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

• The SavedModel input specification is generated using inputs only.

• Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args: Additional positional arguments. May contain tensors, although

this is not recommended, for the reasons above.

**kwargs: Additional keyword arguments. May contain tensors, although

this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.

• mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns:

A tensor or list/tuple of tensors.

class DistributionSamples(*args, **kwargs)

Bases: Layer

call(x, **C)

This is where the layer’s logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state, including tf.Variable instances and nested Layer instances,

in __init__(), or in the build() method that is

called automatically before call() executes for the first time.

Args:

inputs: Input tensor, or dict/list/tuple of input tensors.

The first positional inputs argument is subject to special rules: - inputs must be explicitly passed. A layer cannot have zero

arguments, and inputs cannot be provided via the default value of a keyword argument.

• NumPy array or Python scalar values in inputs get cast as tensors.

• Keras mask metadata is only collected from inputs.

• Layers are built (build(input_shape) method) using shape info from inputs only.

• input_spec compatibility is only checked against inputs.

• Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.

• The SavedModel input specification is generated using inputs only.

• Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args: Additional positional arguments. May contain tensors, although

this is not recommended, for the reasons above.

**kwargs: Additional keyword arguments. May contain tensors, although

this is not recommended, for the reasons above. The following optional keyword arguments are reserved: - training: Boolean scalar tensor of Python boolean indicating

whether the call is meant for training or inference.

• mask: Boolean input mask. If the layer’s call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns:

A tensor or list/tuple of tensors.

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).

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.

Returns:

Python dictionary.

class TrainableCovMatrix(*args, **kwargs)

Bases: Layer

build(input_shapes)

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, **B)

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.

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).

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.

Returns:

Python dictionary.

class VarCovMatrix(*args, **kwargs)

Bases: _M

build(input_shapes)

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, **F)

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.

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).

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.

Returns:

Python dictionary.

Mixture Mapping

class LinearMapping(*args, **kwargs)

Bases: _M

build(input_shapes)

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, **M)

class GeneralMapping(*args, **kwargs)

Bases: _M

build(input_shapes)

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, **S)

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).

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.

Returns:

Python dictionary.

Gaussian Mixture Regression

class DynamicGMR(*args, **kwargs)

Bases: _M

build(input_shapes)

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, **p)

fit_gmms(np_inputs, tf_inputs, input_gmm, output_gmm, n_samples=10)

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).

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.

Returns:

Python dictionary.

set_gmm_values(means, covariances, weights)

Binning

class BinYield(*args, **kwargs)

Bases: Layer

build(input_shapes)

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, **N)

class BinYieldStatic(*args, **kwargs)

Bases: Layer

build(input_shapes)

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, **I)

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.

Normalization

class FixedBackward(*args, **kwargs)

Bases: _FixedBase

call(x, **B)

class FixedForward(*args, **kwargs)

Bases: _FixedBase

call(x, **B)

Distribution Trafos

class AppendCatAsFloat(*args, **kwargs)

Bases: Layer

build(input_shapes)

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, **E)

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.

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).

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.

Returns:

Python dictionary.

class CatTrainableOffset(*args, **kwargs)

Bases: _M

build(input_shapes)

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, **F)

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.

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).

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.

Returns:

Python dictionary.

class CatTrainableScale(*args, **kwargs)

Bases: _M

build(input_shapes)

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, **H)

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.

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).

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.

Returns:

Python dictionary.

class DistMerger(*args, **kwargs)

Bases: Layer

build(input_shapes)

Creates the variables of the layer (for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call. It is invoked automatically before the first execution of call().

This is typically used to create the weights of Layer subclasses (at the discretion of the subclass implementer).

Args:

input_shape: Instance of TensorShape, or list of instances of

TensorShape if the layer expects a list of inputs (one instance per input).

call(x, **P)

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.