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Piecewise linear calibration layer.
tfl.layers.PWLCalibration(
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, **kwargs
)
Used in the notebooks
Used in the tutorials 

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 rank2 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:
Rank2 tensor with shape: (batch_size, units)
.
Example:
calibrator = tfl.layers.PWLCalibration(
# Keypoints of piecewiselinear 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, 1e4),
)
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:

kernel_regularizer

None or single element or list of following:
("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.("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.("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. 
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.

**kwargs

Other args passed to tf.keras.layers.Layer initializer.

Raises  

ValueError

If layer hyperparameters are invalid. 
Attributes  

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.

activity_regularizer

Optional regularizer function for the output of this layer. 
dtype

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

Whether the layer is dynamic (eageronly); set in the constructor. 
input

Retrieves the input tensor(s) of a layer.
Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer. 
input_spec

InputSpec instance(s) describing the input format for this layer.
When you create a layer subclass, you can set
Now, if you try to call the layer on an input that isn't rank 4
(for instance, an input of shape
Input checks that can be specified via
For more information, see 
losses

Losses which are associated with this Layer .
Variable regularization tensors are created when this property is accessed,
so it is eager safe: accessing 
metrics

List of tf.keras.metrics.Metric instances tracked by the layer.

name

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

Returns a tf.name_scope instance for this class.

non_trainable_weights

List of all nontrainable weights tracked by this layer.
Nontrainable weights are not updated during training. They are expected
to be updated manually in 
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. 
submodules

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

trainable


trainable_weights

List of all trainable weights tracked by this layer.
Trainable weights are updated via gradient descent during training. 
weights

Returns the list of all layer variables/weights. 
Methods
add_loss
add_loss(
losses, inputs=None
)
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:
class MyLayer(tf.keras.layers.Layer):
def call(inputs, self):
self.add_loss(tf.abs(tf.reduce_mean(inputs)), inputs=True)
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:
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
zeroargument lambda. These losses are not tracked as part of the model's
topology since they can't be serialized.
Example:
inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Weight regularization.
model.add_loss(lambda: tf.reduce_mean(x.kernel))
The get_losses_for
method allows to retrieve the losses relevant to a
specific set of inputs.
Arguments  

losses

Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zeroargument callables which create a loss tensor. 
inputs

Ignored when executing eagerly. If anything other than None is
passed, it signals the losses are conditional on some of the layer's
inputs, and thus they should only be run where these inputs are
available. This is the case for activity regularization losses, for
instance. If None is passed, the losses are assumed
to be unconditional, and will apply across all dataflows of the layer
(e.g. weight regularization losses).

add_metric
add_metric(
value, aggregation=None, name=None
)
Adds metric tensor to the layer.
Args  

value

Metric tensor. 
aggregation

Samplewise metric reduction function. If aggregation=None ,
it indicates that the metric tensor provided has been aggregated
already. eg, bin_acc = BinaryAccuracy(name='acc') followed by
model.add_metric(bin_acc(y_true, y_pred)) . If aggregation='mean', the
given metric tensor will be samplewise reduced using mean function.
eg, model.add_metric(tf.reduce_sum(outputs), name='output_mean',
aggregation='mean') .

name

String metric name. 
Raises  

ValueError

If aggregation is anything other than None or mean .

assert_constraints
assert_constraints(
eps=1e06
)
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. 
build
build(
input_shape
)
Standard Keras build() method.
compute_mask
compute_mask(
inputs, mask=None
)
Computes an output mask tensor.
Arguments  

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
compute_output_shape(
input_shape
)
Standard Keras compute_output_shape() method.
count_params
count_params()
Count the total number of scalars composing the weights.
Returns  

An integer count. 
Raises  

ValueError

if the layer isn't yet built (in which case its weights aren't yet defined). 
from_config
@classmethod
from_config( config )
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
).
Arguments  

config

A Python dictionary, typically the output of get_config. 
Returns  

A layer instance. 
get_config
get_config()
Standard Keras config for serialization.
get_weights
get_weights()
Returns the current weights of the layer.
The weights of a layer represent the state of the layer. This function returns both trainable and nontrainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.
For example, a Dense layer returns a list of two values peroutput 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)]
Returns  

Weights values as a list of numpy arrays. 
keypoints_outputs
keypoints_outputs()
Returns tensor which corresponds to outputs of layer for keypoints.
set_weights
set_weights(
weights
)
Sets the weights of the layer, from Numpy arrays.
The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer's weights must be instantiated before calling this function by calling the layer.
For example, a Dense layer returns a list of two values peroutput 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)]
Arguments  

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. 
with_name_scope
@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)>
Args  

method

The method to wrap. 
Returns  

The original method wrapped such that it enters the module's name scope. 
__call__
__call__(
*args, **kwargs
)
Wraps call
, applying pre and postprocessing steps.
Arguments  

*args

Positional arguments to be passed to self.call .

**kwargs

Keyword arguments to be passed to self.call .

Returns  

Output tensor(s). 
Note:
 The following optional keyword arguments are reserved for specific uses:
training
: Boolean scalar tensor of Python boolean indicating whether thecall
is meant for training or inference.mask
: Boolean input mask.
 If the layer's
call
method takes amask
argument (as some Keras layers do), its default value will be set to the mask generated forinputs
by the previous layer (ifinput
did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support.
Raises  

ValueError

if the layer's call method returns None (an invalid value).

RuntimeError

if super().__init__() was not called in the constructor.
