# pyformat: disable

"""Piecewise linear calibration layer.

Layer takes input of shape `(batch_size, units)` or `(batch_size, 1)` and

transforms it using `units` number of piecewise linear functions following

monotonicity, convexity and bounds constraints if specified. If multi

dimensional input is provides, each output will be for the corresponding

input, otherwise all PWL functions will act on the same input. All units share

the same layer configuration, but each has their separate set of trained

parameters.

See `tfl.layers.ParallelCombination` layer for using PWLCalibration layer

within Sequential Keras models.

Input shape:

Single input should be a rank-2 tensor with shape: `(batch_size, units)` or

`(batch_size, 1)`. The input can also be a list of two tensors of the same

shape where the first tensor is the regular input tensor and the second is the

`is_missing` tensor. In the `is_missing` tensor, 1.0 represents missing input

and 0.0 represents available input.

Output shape:

If units > 1 and split_outputs is True, a length `units` list of Rank-2

tensors with shape `(batch_size, 1)`. Otherwise, a Rank-2 tensor with shape:

`(batch_size, units)`

Attributes:

- All `__init__` arguments.

kernel: TF variable which stores weights of piecewise linear function.

missing_output: TF variable which stores output learned for missing input.

Or TF Constant which stores `missing_output_value` if one is provided.

Available only if `impute_missing` is True.

Example:

```python

calibrator = tfl.layers.PWLCalibration(

# Key-points of piecewise-linear function.

input_keypoints=np.linspace(1., 4., num=4),

# Output can be bounded, e.g. when this layer feeds into a lattice.

output_min=0.0,

output_max=2.0,

# You can specify monotonicity and other shape constraints for the layer.

monotonicity='increasing',

# You can specify TFL regularizers as tuple ('regularizer name', l1, l2).

# You can also pass any keras Regularizer object.

kernel_regularizer=('hessian', 0.0, 1e-4),

)

```

"""

# pyformat: enable

def __init__(self,

input_keypoints,

units=1,

output_min=None,

output_max=None,

clamp_min=False,

clamp_max=False,

monotonicity="none",

convexity="none",

is_cyclic=False,

kernel_initializer="equal_heights",

kernel_regularizer=None,

impute_missing=False,

missing_input_value=None,

missing_output_value=None,

num_projection_iterations=8,

split_outputs=False,

input_keypoints_type="fixed",

**kwargs):

# pyformat: disable

"""Initializes an instance of `PWLCalibration`.

Args:

input_keypoints: Ordered list of keypoints of piecewise linear function.

Can be anything accepted by tf.convert_to_tensor().

units: Output dimension of the layer. See class comments for details.

output_min: Minimum output of calibrator.

output_max: Maximum output of calibrator.

clamp_min: For monotonic calibrators ensures that output_min is reached.

clamp_max: For monotonic calibrators ensures that output_max is reached.

monotonicity: Constraints piecewise linear function to be monotonic using

'increasing' or 1 to indicate increasing monotonicity, 'decreasing' or

-1 to indicate decreasing monotonicity and 'none' or 0 to indicate no

monotonicity constraints.

convexity: Constraints piecewise linear function to be convex or concave.

Convexity is indicated by 'convex' or 1, concavity is indicated by

'concave' or -1, 'none' or 0 indicates no convexity/concavity

constraints.

Concavity together with increasing monotonicity as well as convexity

together with decreasing monotonicity results in diminishing return

constraints.

Consider increasing the value of `num_projection_iterations` if

convexity is specified, especially with larger number of keypoints.

is_cyclic: Whether the output for last keypoint should be identical to

output for first keypoint. This is useful for features such as

"time of day" or "degree of turn". If inputs are discrete and exactly

match keypoints then is_cyclic will have an effect only if TFL

regularizers are being used.

kernel_initializer: None or one of:

- String `"equal_heights"`: For pieces of pwl function to have equal

heights.

- String `"equal_slopes"`: For pieces of pwl function to have equal

slopes.

- Any Keras initializer object. If you are passing such object make sure

that you know how layer stores its data.

kernel_regularizer: None or single element or list of following:

- Tuple `("laplacian", l1, l2)` where `l1` and `l2` are floats which

represent corresponding regularization amount for Laplacian

regularizer. It penalizes the first derivative to make the function

more constant. See `tfl.pwl_calibration.LaplacianRegularizer` for more

details.

- Tuple `("hessian", l1, l2)` where `l1` and `l2` are floats which

represent corresponding regularization amount for Hessian regularizer.

It penalizes the second derivative to make the function more linear.

See `tfl.pwl_calibration.HessianRegularizer` for more details.

- Tuple `("wrinkle", l1, l2)` where `l1` and `l2` are floats which

represent corresponding regularization amount for wrinkle regularizer.

It penalizes the third derivative to make the function more smooth.

See 'tfl.pwl_calibration.WrinkleRegularizer` for more details.

- Any Keras regularizer object.

impute_missing: Whether to learn an output for cases where input data is

missing. If set to True, either `missing_input_value` should be

initialized, or the `call()` method should get pair of tensors. See

class input shape description for more details.

missing_input_value: If set, all inputs which are equal to this value will

be considered as missing. Can not be set if `impute_missing` is False.

missing_output_value: If set, instead of learning output for missing

inputs, simply maps them into this value. Can not be set if

`impute_missing` is False.

num_projection_iterations: Number of iterations of the Dykstra's

projection algorithm. Constraints are strictly satisfied at the end of

each update, but the update will be closer to a true L2 projection with

higher number of iterations. See

`tfl.pwl_calibration_lib.project_all_constraints` for more details.

split_outputs: Whether to split the output tensor into a list of

outputs for each unit. Ignored if units < 2.

input_keypoints_type: One of "fixed" or "learned_interior". If

"learned_interior", keypoints are initialized to the values in

`input_keypoints` but then allowed to vary during training, with the

exception of the first and last keypoint location which are fixed.

Convexity can only be imposed with "fixed".

**kwargs: Other args passed to `keras.layers.Layer` initializer.

Raises:

ValueError: If layer hyperparameters are invalid.

"""

# pyformat: enable

super(PWLCalibration, self).__init__(**kwargs)

pwl_calibration_lib.verify_hyperparameters(

input_keypoints=input_keypoints,

output_min=output_min,

output_max=output_max,

monotonicity=monotonicity,

convexity=convexity,

is_cyclic=is_cyclic,

input_keypoints_type=input_keypoints_type)

if missing_input_value is not None and not impute_missing:

raise ValueError("'missing_input_value' is specified, but "

"'impute_missing' is set to False. "

"'missing_input_value': " + str(missing_input_value))

if missing_output_value is not None and not impute_missing:

raise ValueError("'missing_output_value' is specified, but "

"'impute_missing' is set to False. "

"'missing_output_value': " + str(missing_output_value))

if input_keypoints is None:

raise ValueError("'input_keypoints' can't be None")

if monotonicity is None:

raise ValueError("'monotonicity' can't be None. Did you mean '0'?")

if convexity not in ("none",

0) and input_keypoints_type == "learned_interior":

raise ValueError("Cannot set input_keypoints_type to 'learned_interior'"

" and impose convexity constraints.")

self.input_keypoints = input_keypoints

self.units = units

self.output_min = output_min

self.output_max = output_max

self.clamp_min = clamp_min

self.clamp_max = clamp_max

(self._output_init_min, self._output_init_max, self._output_min_constraints,

self._output_max_constraints

) = pwl_calibration_lib.convert_all_constraints(self.output_min,

self.output_max,

self.clamp_min,

self.clamp_max)

self.monotonicity = monotonicity

self.convexity = convexity

self.is_cyclic = is_cyclic

if kernel_initializer == "equal_heights":

self.kernel_initializer = UniformOutputInitializer(

output_min=self._output_init_min,

output_max=self._output_init_max,

monotonicity=self.monotonicity)

elif kernel_initializer == "equal_slopes":

self.kernel_initializer = UniformOutputInitializer(

output_min=self._output_init_min,

output_max=self._output_init_max,

monotonicity=self.monotonicity,

keypoints=self.input_keypoints)

else:

# Keras deserialization logic must have explicit acceess to all custom

# classes. This is standard way to provide such access.

with keras.utils.custom_object_scope({

"UniformOutputInitializer": UniformOutputInitializer,

}):

self.kernel_initializer = keras.initializers.get(kernel_initializer)

self.kernel_regularizer = []

if kernel_regularizer:

if (callable(kernel_regularizer) or

(isinstance(kernel_regularizer, tuple) and

isinstance(kernel_regularizer[0], six.string_types))):

kernel_regularizer = [kernel_regularizer]

for reg in kernel_regularizer:

if isinstance(reg, tuple):

(name, l1, l2) = reg

if name.lower() == "laplacian":

self.kernel_regularizer.append(

LaplacianRegularizer(l1=l1, l2=l2, is_cyclic=self.is_cyclic))

elif name.lower() == "hessian":

self.kernel_regularizer.append(

HessianRegularizer(l1=l1, l2=l2, is_cyclic=self.is_cyclic))

elif name.lower() == "wrinkle":

self.kernel_regularizer.append(

WrinkleRegularizer(l1=l1, l2=l2, is_cyclic=self.is_cyclic))

else:

raise ValueError("Unknown custom lattice regularizer: %s" % reg)

else:

# This is needed for Keras deserialization logic to be aware of our

# custom objects.

with keras.utils.custom_object_scope({

"LaplacianRegularizer": LaplacianRegularizer,

"HessianRegularizer": HessianRegularizer,

"WrinkleRegularizer": WrinkleRegularizer,

}):

self.kernel_regularizer.append(keras.regularizers.get(reg))

self.impute_missing = impute_missing

self.missing_input_value = missing_input_value

self.missing_output_value = missing_output_value

self.num_projection_iterations = num_projection_iterations

self.split_outputs = split_outputs

self.input_keypoints_type = input_keypoints_type

def build(self, input_shape):

"""Standard Keras build() method."""

input_keypoints = np.array(self.input_keypoints)

# Don't need last keypoint for interpolation because we need only beginnings

# of intervals.

if self.input_keypoints_type == "fixed":

self._interpolation_keypoints = tf.constant(

input_keypoints[:-1],

dtype=self.dtype,

name=INTERPOLATION_KEYPOINTS_NAME)

self._lengths = tf.constant(

input_keypoints[1:] - input_keypoints[:-1],

dtype=self.dtype,

name=LENGTHS_NAME)

else:

self._keypoint_min = input_keypoints[0]

self._keypoint_range = input_keypoints[-1] - input_keypoints[0]

# Logits are initialized such that they will recover the scaled keypoint

# gaps in input_keypoints.

initial_logits = np.log(

(input_keypoints[1:] - input_keypoints[:-1]) / self._keypoint_range)

tiled_logits = np.tile(initial_logits, self.units)

self.interpolation_logits = self.add_weight(

INTERPOLATION_LOGITS_NAME,

shape=[self.units, len(input_keypoints) - 1],

initializer=tf.constant_initializer(tiled_logits),

dtype=self.dtype)

constraints = PWLCalibrationConstraints(

monotonicity=self.monotonicity,

convexity=self.convexity,

lengths=self._lengths if self.input_keypoints_type == "fixed" else None,

output_min=self.output_min,

output_max=self.output_max,

output_min_constraints=self._output_min_constraints,

output_max_constraints=self._output_max_constraints,

num_projection_iterations=self.num_projection_iterations)

if not self.kernel_regularizer:

kernel_reg = None

elif len(self.kernel_regularizer) == 1:

kernel_reg = self.kernel_regularizer[0]

else:

# Keras interface assumes only one regularizer, so summ all regularization

# losses which we have.

kernel_reg = lambda x: tf.add_n([r(x) for r in self.kernel_regularizer])

# If 'is_cyclic' is specified - last weight will be computed from previous

# weights in order to connect last keypoint with first.

num_weights = input_keypoints.size - self.is_cyclic

# PWL calibration layer kernel is units-column matrix. First row of matrix

# represents bias. All remaining represent delta in y-value compare to

# previous point. Aka heights of segments.

self.kernel = self.add_weight(

PWL_CALIBRATION_KERNEL_NAME,

shape=[num_weights, self.units],

initializer=self.kernel_initializer,

regularizer=kernel_reg,

constraint=constraints,

dtype=self.dtype)

if self.kernel_regularizer and not tf.executing_eagerly():

# Keras has its own mechanism to handle regularization losses which

# does not use GraphKeys, but we want to also add losses to graph keys so

# they are easily accessable when layer is being used outside of Keras.

# Adding losses to GraphKeys will not interfer with Keras.

for reg in self.kernel_regularizer:

tf.compat.v1.add_to_collection(

tf.compat.v1.GraphKeys.REGULARIZATION_LOSSES, reg(self.kernel))

if self.impute_missing:

if self.missing_input_value is not None:

self._missing_input_value_tensor = tf.constant(

self.missing_input_value,

dtype=self.dtype,

name=MISSING_INPUT_VALUE_NAME)

else:

self._missing_input_value_tensor = None

if self.missing_output_value is not None:

self.missing_output = tf.constant(

self.missing_output_value, shape=[1, self.units], dtype=self.dtype)

else:

missing_init = (self._output_init_min + self._output_init_max) / 2.0

missing_constraints = NaiveBoundsConstraints(

lower_bound=self.output_min, upper_bound=self.output_max)

self.missing_output = self.add_weight(

PWL_CALIBRATION_MISSING_OUTPUT_NAME,

shape=[1, self.units],

initializer=keras.initializers.Constant(value=missing_init),

constraint=missing_constraints,

dtype=self.dtype)

super(PWLCalibration, self).build(input_shape)

def call(self, inputs):

"""Standard Keras call() method..

Args:

inputs: Either input tensor or list of 2 elements: input tensor and

`is_missing` tensor.

Returns:

Calibrated input tensor.

Raises:

ValueError: If `is_missing` tensor specified incorrectly.

"""

is_missing = None

if isinstance(inputs, list):

# Only 2 element lists are allowed. When such list is given - second

# element represents 'is_missing' tensor encoded as float value.

if not self.impute_missing:

raise ValueError("Multiple inputs for PWLCalibration layer assume "

"regular input tensor and 'is_missing' tensor, but "

"this instance of a layer is not configured to handle "

"missing value. See 'impute_missing' parameter.")

if len(inputs) > 2:

raise ValueError("Multiple inputs for PWLCalibration layer assume "

"normal input tensor and 'is_missing' tensor, but more"

" than 2 tensors given. 'inputs': " + str(inputs))

if len(inputs) == 2:

inputs, is_missing = inputs

if is_missing.shape.as_list() != inputs.shape.as_list():

raise ValueError(

"is_missing shape %s does not match inputs shape %s for "

"PWLCalibration layer" %

(str(is_missing.shape), str(inputs.shape)))

else:

[inputs] = inputs

if len(inputs.shape) != 2 or (inputs.shape[1] != self.units and

inputs.shape[1] != 1):

raise ValueError("Shape of input tensor for PWLCalibration layer must be "

"[-1, units] or [-1, 1]. It is: " + str(inputs.shape))

if self.input_keypoints_type == "fixed":

keypoints_dtype = self._interpolation_keypoints.dtype

else:

keypoints_dtype = self.interpolation_logits.dtype

if inputs.dtype != keypoints_dtype:

raise ValueError("dtype(%s) of input to PWLCalibration layer does not "

"correspond to dtype(%s) of keypoints. You can enforce "

"dtype of keypoints by explicitly providing 'dtype' "

"parameter to layer constructor or by passing keypoints "

"in such format which by default will be converted into "

"desired one." % (inputs.dtype, keypoints_dtype))

# Here is calibration. Everything else is handling of missing.

if inputs.shape[1] > 1 or (self.input_keypoints_type == "learned_interior"

and self.units > 1):

# Interpolation will have shape [batch_size, units, weights] in these

# cases. To prepare for that, we add a dimension to the input here to get

# shape [batch_size, units, 1] or [batch_size, 1, 1] if 1d input.

inputs_to_calibration = tf.expand_dims(inputs, -1)

else:

inputs_to_calibration = inputs

if self.input_keypoints_type == "learned_interior":

self._lengths = tf.multiply(

tf.nn.softmax(self.interpolation_logits, axis=1),

self._keypoint_range,

name=LENGTHS_NAME)

self._interpolation_keypoints = tf.add(

tf.cumsum(self._lengths, axis=1, exclusive=True),

self._keypoint_min,

name=INTERPOLATION_KEYPOINTS_NAME)

interpolation_weights = pwl_calibration_lib.compute_interpolation_weights(

inputs_to_calibration, self._interpolation_keypoints, self._lengths)

if self.is_cyclic:

# Need to add such last height to make all heights to sum up to 0.0 in

# order to make calibrator cyclic.

bias_and_heights = tf.concat(

[self.kernel, -tf.reduce_sum(self.kernel[1:], axis=0, keepdims=True)],

axis=0)

else:

bias_and_heights = self.kernel

# bias_and_heights has shape [weight, units].

if len(interpolation_weights.shape) > 2:

# Multi dim input has interpolation shape [batch_size, units, weights].

result = tf.reduce_sum(

interpolation_weights * tf.transpose(bias_and_heights), axis=-1)

else:

# Single dim input has interpolation shape [batch_size, weights].

result = tf.matmul(interpolation_weights, bias_and_heights)

if self.impute_missing:

if is_missing is None:

if self.missing_input_value is None:

raise ValueError("PWLCalibration layer is configured to impute "

"missing but no 'missing_input_value' specified and "

"'is_missing' tensor is not given.")

assert self._missing_input_value_tensor is not None

is_missing = tf.cast(

tf.equal(inputs, self._missing_input_value_tensor),

dtype=self.dtype)

result = is_missing * self.missing_output + (1.0 - is_missing) * result

if self.units > 1 and self.split_outputs:

result = tf.split(result, self.units, axis=1)

return result

def compute_output_shape(self, input_shape):

"""Standard Keras compute_output_shape() method."""

del input_shape

if self.units > 1 and self.split_outputs:

return [(None, 1)] * self.units

else:

return (None, self.units)

def get_config(self):

"""Standard Keras config for serialization."""

config = {

"input_keypoints": self.input_keypoints,

"units": self.units,

"output_min": self.output_min,

"output_max": self.output_max,

"clamp_min": self.clamp_min,

"clamp_max": self.clamp_max,

"monotonicity": self.monotonicity,

"convexity": self.convexity,

"is_cyclic": self.is_cyclic,

"kernel_initializer":

keras.initializers.serialize(

self.kernel_initializer, use_legacy_format=True),

"kernel_regularizer":

[keras.regularizers.serialize(r, use_legacy_format=True)

for r in self.kernel_regularizer],

"impute_missing": self.impute_missing,

"missing_input_value": self.missing_input_value,

"num_projection_iterations": self.num_projection_iterations,

"split_outputs": self.split_outputs,

"input_keypoints_type": self.input_keypoints_type,

} # pyformat: disable

config.update(super(PWLCalibration, self).get_config())

return config

def assert_constraints(self, eps=1e-6):

"""Asserts that layer weights satisfy all constraints.

In graph mode builds and returns list of assertion ops. Note that ops will

be created at the moment when this function is being called.

In eager mode directly executes assertions.

Args:

eps: Allowed constraints violation.

Returns:

List of assertion ops in graph mode or immediately asserts in eager mode.

"""

# Assert by computing outputs for keypoints and testing them against

# constraints.

test_inputs = tf.constant(

value=self.input_keypoints,

dtype=self.dtype,

shape=[len(self.input_keypoints), 1])

outputs = self.call(test_inputs)

asserts = pwl_calibration_lib.assert_constraints(

outputs=outputs,

monotonicity=utils.canonicalize_monotonicity(self.monotonicity),

output_min=self.output_min,

output_max=self.output_max,

clamp_min=self.clamp_min,

clamp_max=self.clamp_max,

debug_tensors=["weights:", self.kernel],

eps=eps)

if self.impute_missing and self.missing_output_value is None:

asserts.append(

pwl_calibration_lib.assert_constraints(

outputs=self.missing_output,

monotonicity=0,

output_min=self.output_min,

output_max=self.output_max,

clamp_min=False,

clamp_max=False,

debug_tensors=["Imputed missing value:", self.missing_output],

eps=eps))

return asserts

def keypoints_outputs(self):

"""Returns tensor of keypoint outputs of shape [num_weights, num_units]."""

kp_outputs = tf.cumsum(self.kernel)

if self.is_cyclic:

kp_outputs = tf.concat([kp_outputs, kp_outputs[0:1]], axis=0)

return kp_outputs

def keypoints_inputs(self):

"""Returns tensor of keypoint inputs of shape [num_weights, num_units]."""

# We don't store the last keypoint in self._interpolation_keypoints since

# it is not needed for training or evaluation, but we re-add it here to

# align with the keypoints_outputs function.

if self.input_keypoints_type == "fixed":

all_keypoints = tf.concat([

self._interpolation_keypoints,

self._interpolation_keypoints[-1:] + self._lengths[-1:]

],

axis=0)

return tf.stack([all_keypoints] * self.units, axis=1)

else:

lengths = tf.nn.softmax(

self.interpolation_logits, axis=-1) * self._keypoint_range

interpolation_keypoints = tf.cumsum(

lengths, axis=-1, exclusive=True) + self._keypoint_min

all_keypoints = tf.concat([

interpolation_keypoints,

interpolation_keypoints[:, -1:] + lengths[:, -1:]

],

axis=1)

# pyformat: disable

"""Initializes PWL calibration layer to represent linear function.

PWL calibration layer weights are one-d tensor. First element of tensor

represents bias. All remaining represent delta in y-value compare to previous

point. Aka heights of segments.

Attributes:

- All `__init__` arguments.

"""

# pyformat: enable

def __init__(self, output_min, output_max, monotonicity, keypoints=None):

# pyformat: disable

"""Initializes an instance of `UniformOutputInitializer`.

Args:

output_min: Minimum value of PWL calibration output after initialization.

output_max: Maximum value of PWL calibration output after initialization.

monotonicity:

- if 'none' or 'increasing', the returned function will go from

`(input_min, output_min)` to `(input_max, output_max)`.

- if 'decreasing', the returned function will go from

`(input_min, output_max)` to `(input_max, output_min)`.

keypoints:

- if not provided (None or []), all pieces of returned function

will have equal heights (i.e. `y[i+1] - y[i]` is constant).

- if provided, all pieces of returned function will have equal slopes

(i.e. `(y[i+1] - y[i]) / (x[i+1] - x[i])` is constant).

""" # pyformat: enable

pwl_calibration_lib.verify_hyperparameters(

input_keypoints=keypoints,

output_min=output_min,

output_max=output_max,

monotonicity=monotonicity)

self.output_min = output_min

self.output_max = output_max

self.monotonicity = monotonicity

self.keypoints = keypoints

def __call__(self, shape, dtype=None, partition_info=None):

"""Returns weights of PWL calibration layer.

Args:

shape: Must be a collection of the form `(k, units)` where `k >= 2`.

dtype: Standard Keras initializer param.

partition_info: Standard Keras initializer param.

Returns:

Weights of PWL calibration layer.

Raises:

ValueError: If requested shape is invalid for PWL calibration layer

weights.

"""

return pwl_calibration_lib.linear_initializer(

shape=shape,

output_min=self.output_min,

output_max=self.output_max,

monotonicity=utils.canonicalize_monotonicity(self.monotonicity),

keypoints=self.keypoints,

dtype=dtype)

def get_config(self):

"""Standard Keras config for serialization."""

return {

"output_min": self.output_min,

"output_max": self.output_max,

"monotonicity": self.monotonicity,

"keypoints": self.keypoints,

# pyformat: disable

"""Monotonicity and bounds constraints for PWL calibration layer.

Applies an approximate L2 projection to the weights of a PWLCalibration layer

such that the result satisfies the specified constraints.

Attributes:

- All `__init__` arguments.

""" # pyformat: enable

def __init__(

self,

monotonicity="none",

convexity="none",

lengths=None,

output_min=None,

output_max=None,

output_min_constraints=pwl_calibration_lib.BoundConstraintsType.NONE,

output_max_constraints=pwl_calibration_lib.BoundConstraintsType.NONE,

num_projection_iterations=8):

"""Initializes an instance of `PWLCalibration`.

Args:

monotonicity: Same meaning as corresponding parameter of `PWLCalibration`.

convexity: Same meaning as corresponding parameter of `PWLCalibration`.

lengths: Lengths of pieces of piecewise linear function. Needed only if

convexity is specified.

output_min: Minimum possible output of pwl function.

output_max: Maximum possible output of pwl function.

output_min_constraints: A `tfl.pwl_calibration_lib.BoundConstraintsType`

describing the constraints on the layer's minimum value.

output_max_constraints: A `tfl.pwl_calibration_lib.BoundConstraintsType`

describing the constraints on the layer's maximum value.

num_projection_iterations: Same meaning as corresponding parameter of

`PWLCalibration`.

"""

pwl_calibration_lib.verify_hyperparameters(

output_min=output_min,

output_max=output_max,

monotonicity=monotonicity,

convexity=convexity,

lengths=lengths)

self.monotonicity = monotonicity

self.convexity = convexity

self.lengths = lengths

self.output_min = output_min

self.output_max = output_max

self.output_min_constraints = output_min_constraints

self.output_max_constraints = output_max_constraints

self.num_projection_iterations = num_projection_iterations

canonical_convexity = utils.canonicalize_convexity(self.convexity)

canonical_monotonicity = utils.canonicalize_monotonicity(self.monotonicity)

if (canonical_convexity != 0 and canonical_monotonicity == 0 and

(output_min_constraints != pwl_calibration_lib.BoundConstraintsType.NONE

or output_max_constraints !=

pwl_calibration_lib.BoundConstraintsType.NONE)):

logging.warning("Convexity constraints are specified with bounds "

"constraints, but without monotonicity. Such combination "

"might lead to convexity being slightly violated. "

"Consider increasing num_projection_iterations to "

"reduce violation.")

def __call__(self, w):

"""Applies constraints to w."""

return pwl_calibration_lib.project_all_constraints(

weights=w,

monotonicity=utils.canonicalize_monotonicity(self.monotonicity),

output_min=self.output_min,

output_max=self.output_max,

output_min_constraints=self.output_min_constraints,

output_max_constraints=self.output_max_constraints,

convexity=utils.canonicalize_convexity(self.convexity),

lengths=self.lengths,

num_projection_iterations=self.num_projection_iterations)

def get_config(self):

"""Standard Keras config for serialization."""

return {

"monotonicity": self.monotonicity,

"output_min": self.output_min,

"output_max": self.output_max,

"output_min_constraints": self.output_min_constraints,

"output_max_constraints": self.output_max_constraints,

"convexity": self.convexity,

"lengths": self.lengths,

"num_projection_iterations": self.num_projection_iterations,

# pyformat: disable

"""Naively clips all elements of tensor to be within bounds.

This constraint is used only for the weight tensor for missing output value.

Attributes:

- All `__init__` arguments.

""" # pyformat: enable

def __init__(self, lower_bound=None, upper_bound=None):

"""Initializes an instance of `NaiveBoundsConstraints`.

Args:

lower_bound: Lower bound to clip variable values to.

upper_bound: Upper bound to clip variable values to.

"""

self.lower_bound = lower_bound

self.upper_bound = upper_bound

def __call__(self, w):

"""Applies constraints to w."""

if self.lower_bound is not None:

w = tf.maximum(w, self.lower_bound)

if self.upper_bound is not None:

w = tf.minimum(w, self.upper_bound)

return w

def get_config(self):

"""Standard Keras config for serialization."""

return {

"lower_bound": self.lower_bound,

"upper_bound": self.upper_bound

# pyformat: disable

"""Laplacian regularizer for PWL calibration layer.

Calibrator Laplacian regularization penalizes the change in the calibration

output. It is defined to be:

`l1 * ||delta||_1 + l2 * ||delta||_2^2`

where `delta` is:

`output_keypoints[1:end] - output_keypoints[0:end-1]`.

Attributes:

- All `__init__` arguments.

""" # pyformat: enable

def __init__(self, l1=0.0, l2=0.0, is_cyclic=False):

"""Initializes an instance of `LaplacianRegularizer`.

Args:

l1: l1 regularization amount as float.

l2: l2 regularization amount as float.

is_cyclic: Whether the first and last keypoints should take the same

output value.

"""

self.l1 = l1

self.l2 = l2

self.is_cyclic = is_cyclic

def __call__(self, x):

"""Returns regularization loss.

Args:

x: Tensor of shape: `(k, units)` which represents weights of PWL

calibration layer. First row of weights is bias term. All remaining

represent delta in y-value compare to previous point (segment heights).

"""

if not self.l1 and not self.l2:

return tf.constant(0.0, dtype=x.dtype, shape=())

heights = x[1:]

if self.is_cyclic:

# Need to add such last height to make all heights to sum up to 0.0 in

# order to make calibrator cyclic.

heights = tf.concat(

[heights, -tf.reduce_sum(heights, axis=0, keepdims=True)], axis=0)

losses = []

if self.l1:

losses.append(self.l1 * tf.reduce_sum(tf.abs(heights)))

if self.l2:

losses.append(self.l2 * tf.reduce_sum(tf.square(heights)))

result = losses[0]

if len(losses) == 2:

result += losses[1]

return result

def get_config(self):

"""Standard Keras config for serialization."""

return {

"l1": self.l1,

"l2": self.l2,

"is_cyclic": self.is_cyclic,

# pyformat: disable

"""Hessian regularizer for PWL calibration layer.

Calibrator hessian regularizer penalizes the change in slopes of linear

pieces. It is define to be:

`l1 * ||nonlinearity||_1 + l2 * ||nonlinearity||_2^2`

where `nonlinearity` is:

`2 * output_keypoints[1:end-1] - output_keypoints[0:end-2]

- output_keypoints[2:end]`.

This regularizer is zero when the output_keypoints form a linear function of

the index (and not necessarily linear in input values, e.g. when using

non-uniform input keypoints).

Attributes:

- All `__init__` arguments.

""" # pyformat: enable

def __init__(self, l1=0.0, l2=0.0, is_cyclic=False):

"""Initializes an instance of `HessianRegularizer`.

Args:

l1: l1 regularization amount as float.

l2: l2 regularization amount as float.

is_cyclic: Whether the first and last keypoints should take the same

output value.

"""

self.l1 = l1

self.l2 = l2

self.is_cyclic = is_cyclic

def __call__(self, x):

"""Returns regularization loss.

Args:

x: Tensor of shape: `(k, units)` which represents weights of PWL

calibration layer. First row of weights is bias term. All remaining

represent delta in y-value compare to previous point (segment heights).

"""

if not self.l1 and not self.l2:

return tf.constant(0.0, dtype=x.dtype, shape=())

if self.is_cyclic:

heights = x[1:]

heights = tf.concat(

[

heights,

-tf.reduce_sum(heights, axis=0, keepdims=True),

heights[0:1],

],

axis=0,

)

nonlinearity = heights[1:] - heights[:-1]

else:

nonlinearity = x[2:] - x[1:-1]

losses = []

if self.l1:

losses.append(self.l1 * tf.reduce_sum(tf.abs(nonlinearity)))

if self.l2:

losses.append(self.l2 * tf.reduce_sum(tf.square(nonlinearity)))

result = losses[0]

if len(losses) == 2:

result += losses[1]

return result

def get_config(self):

"""Standard Keras config for serialization."""

return {

"l1": self.l1,

"l2": self.l2,

"is_cyclic": self.is_cyclic,

# pyformat: disable

"""Wrinkle regularizer for PWL calibration layer.

Calibrator wrinkle regularization penalizes the change in the second

derivative. It is defined to be:

`l1 * ||third_derivative||_1 + l2 * ||third_derivative||_2^2`

where `third_derivative` is:

`3 * output_keypoints[1:end-2] - 3 * output_keypoints[2:end-1]

- output_keypoints[0:end-3] + output_keypoints[3:end]`.

This regularizer is zero when the output_keypoints form a 2nd order polynomial

of the index (and not necessarily in input values, e.g. when using

non-uniform input keypoints).

Attributes:

- All `__init__` arguments.

""" # pyformat: enable

def __init__(self, l1=0.0, l2=0.0, is_cyclic=False):

"""Initializes an instance of `WrinkleRegularizer`.

Args:

l1: l1 regularization amount as float.

l2: l2 regularization amount as float.

is_cyclic: Whether the first and last keypoints should take the same

output value.

"""

self.l1 = l1

self.l2 = l2

self.is_cyclic = is_cyclic

def __call__(self, x):

"""Returns regularization loss.

Args: