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tfl.layers.Linear

View source on GitHub

Layer which represents linear function.

tfl.layers.Linear(
    num_input_dims, monotonicities=None, monotonic_dominances=None,
    range_dominances=None, input_min=None, input_max=None, use_bias=True,
    normalization_order=None, kernel_initializer='random_uniform',
    bias_initializer='random_uniform', kernel_regularizer=None,
    bias_regularizer=None, **kwargs
)

Monotonicity can be specified for any input dimension in which case learned weight for that dimension is guaranteed to be either non negative for increasing or non positive for decreasing monotonicity.

Monotonic dominance can be specified for any pair of dimensions referred to as dominant and weak dimensions such that the effect (slope) in the direction of the dominant dimension to be greater than that of the weak dimension for any point. Both dominant and weak dimensions must be increasing.

Range dominance can be specified for any pair of dominant and weak dimensions such that the range of possible outputs to be greater if one varies the dominant dimension than if one varies the weak dimension for any point. We require the slope of the dominant dimension scaled by its input range to be greater than the slope of the weak dimension similarly scaled by its input range. Both dimensions must have the same direction of monotonicity and their input min and max must be provided.

Weights can be constrained to have a fixed norm.

Input shape:

Rank-2 tensor with shape: (batch_size, num_input_dims)

Output shape:

Rank-2 tensor with shape: (batch_size, 1)

Args:

  • num_input_dims: Number of input dimensions.
  • monotonicities: None or list or tuple of length 'num_input_dims' of {'decreasing', 'none', 'increasing', -1, 0, 1} which specifies if the model output should be monotonic in corresponding feature, using 'increasing' or 1 to indicate increasing monotonicity, 'decreasing' or -1 to indicate decreasing monotonicity and 'none' or 0 to indicate no monotonicity constraints.. In case of decreasing monotonicity corresponding weight will be constrained to be non positive, in case of increasing non-negative. Instead of a list or tuple single value can be specified to indicate the monotonicity constraint across all dimensions.
  • monotonic_dominances: None or list of two-element tuples. First element is the index of the dominant dimension. Second element is the index of the weak dimension.
  • range_dominances: None or list of two-element tuples. First element is the index of the dominant dimension. Second element is the index of the weak dimension. Both dominant and weak dimensions must have input_min and input_max set.
  • input_min: None of list or tuple of length 'num_input_dims' of either 'none' or float which specifies the minimum value to clip by for each dimension.
  • input_max: None of list or tuple of length 'num_input_dims' of either 'none' or float which specifies the maximum value to clip by for each dimension.
  • use_bias: Whether linear function has bias.
  • normalization_order: If specified learned weights will be adjusted to have norm 1. Norm will be computed by: tf.norm(tensor, ord=normalization_order).
  • kernel_initializer: Any keras initializer to be applied to kernel.
  • bias_initializer: Any keras initializer to be applied to bias. Only valid if use_bias == True.
  • kernel_regularizer: None or single element or list of any Keras regularizer objects.
  • bias_regularizer: None or single element or list of any Keras regularizer objects.
  • **kwargs: Other args passed to tf.keras.layers.Layer initializer.

Attributes:

  • kernel: layer's kernel.
  • bias: layer's bias. Only available if use_bias == True.
  • activity_regularizer: Optional regularizer function for the output of this layer.
  • dtype
  • dynamic
  • 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_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.

  • 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.

  • input_spec

  • losses: Losses which are associated with this Layer.

    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.

  • metrics

  • name: Returns the name of this module as passed or determined in the ctor.

    NOTE: This is not the same as the self.name_scope.name which includes parent module names.

  • name_scope: Returns a tf.name_scope instance for this class.

  • non_trainable_variables

  • non_trainable_weights

  • 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.

  • 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.

  • 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.

  • 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
assert list(a.submodules) == [b, c]
assert list(b.submodules) == [c]
assert list(c.submodules) == []
  • trainable
  • trainable_variables: Sequence of trainable variables owned by this module and its submodules.

  • trainable_weights

  • updates

  • variables: Returns the list of all layer variables/weights.

    Alias of self.weights.

  • weights: Returns the list of all layer variables/weights.

Example:

layer = tfl.layers.Linear(
    num_input_dims=8,
    # Monotonicity constraints can be defined per dimension or for all dims.
    monotonicities='increasing',
    use_bias=True,
    # You can force the L1 norm to be 1. Since this is a monotonic layer,
    # the coefficients will sum to 1, making this a "weighted average".
    normalization_order=1)

Raises:

  • ValueError: if monotonicity specified incorrectly.

Methods

__call__

__call__(
    inputs, *args, **kwargs
)

Wraps call, applying pre- and post-processing steps.

Arguments:

  • 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).

Note:

  • The following optional keyword arguments are reserved for specific uses:
    • 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 (as some Keras layers do), 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.

Raises:

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

assert_constraints

View source

assert_constraints(
    eps=0.0001
)

Asserts that weights satisfy all constraints.

In graph mode builds and returns list of assertion ops. In eager mode directly executes assetions.

Args:

  • eps: Allowed constraints violation.

Returns:

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

build

View source

build(
    input_shape
)

Standard Keras build() method.

Args:

  • input_shape: Must be: (batch_size, num_input_dims)

Raises:

  • ValueError: If shape is not (batch_size, num_input_dims).

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

View source

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(
    cls, 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

View source

get_config()

Standard Keras get_config() method.

get_input_at

get_input_at(
    node_index
)

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

Arguments:

  • 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

get_input_mask_at(
    node_index
)

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

Arguments:

  • 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

get_input_shape_at(
    node_index
)

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

Arguments:

  • 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_losses_for

get_losses_for(
    inputs
)

Retrieves losses relevant to a specific set of inputs.

Arguments:

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

Returns:

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

get_output_at

get_output_at(
    node_index
)

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

Arguments:

  • 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

get_output_mask_at(
    node_index
)

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

Arguments:

  • 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

get_output_shape_at(
    node_index
)

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

Arguments:

  • 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

get_updates_for(
    inputs
)

Retrieves updates relevant to a specific set of inputs.

Arguments:

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

Returns:

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

get_weights

get_weights()

Returns the current weights of the layer.

Returns:

Weights values as a list of numpy arrays.

set_weights

set_weights(
    weights
)

Sets the weights of the layer, from Numpy arrays.

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(
    cls, 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], 64]))
    return tf.matmul(x, self.w)

Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

mod = MyModule()
mod(tf.ones([8, 32]))
# ==> <tf.Tensor: ...>
mod.w
# ==> <tf.Variable ...'my_module/w:0'>

Args:

  • method: The method to wrap.

Returns:

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