tfl.layers.Lattice

View source on GitHub

Lattice layer.

tfl.layers.Lattice(
    lattice_sizes,
    units=1,
    monotonicities=None,
    unimodalities=None,
    edgeworth_trusts=None,
    trapezoid_trusts=None,
    monotonic_dominances=None,
    range_dominances=None,
    joint_monotonicities=None,
    joint_unimodalities=None,
    output_min=None,
    output_max=None,
    num_projection_iterations=10,
    monotonic_at_every_step=True,
    clip_inputs=True,
    interpolation='hypercube',
    kernel_initializer='random_uniform_or_linear_initializer',
    kernel_regularizer=None,
    **kwargs
)

Used in the notebooks

Layer performs interpolation using one of units d-dimensional lattices with arbitrary number of keypoints per dimension. There are trainable weights associated with lattice vertices. Input to this layer is considered to be a d-dimensional point within the lattice. If point coincides with one of the lattice vertex then interpolation result for this point is equal to weight associated with that vertex. Otherwise, all surrounding vertices contribute to the interpolation result inversely proportional to the distance from them.

For example lattice sizes: [2, 3] produce following lattice:

o---o---o
|   |   |
o---o---o

First coordinate of input tensor must be within [0, 1], and the second within [0, 2]. If coordinates are outside of this range they will be clipped into it.

There are several types of constraints on the shape of the learned function that are either 1 or 2 dimensional:

• Monotonicity: constrains the function to be increasing in the corresponding dimension. To achieve decreasing monotonicity, either pass the inputs through a tfl.layers.PWLCalibration with decreasing monotonicity, or manually reverse the inputs as lattice_size - 1 - inputs.
• Unimodality: constrains the function to be unimodal in that dimension with minimum being in the center lattice vertex of that dimension. Single dimension can not be constrained to be both monotonic and unimodal. Unimodal dimensions must have at least 3 lattice vertices.
• Edgeworth Trust: constrains the function to be more responsive to a main feature as a secondary conditional feature increases or decreases. For example, we may want the model to rely more on average rating (main feature) when the number of reviews (conditional feature) is high. In particular, the constraint guarantees that a given change in the main feature's value will change the model output by more when a secondary feature indicates higher trust in the main feature. Note that the constraint only works when the model is monotonic in the main feature.
• Trapezoid Trust: conceptually similar to edgeworth trust, but this constraint guarantees that the range of possible outputs along the main feature dimension, when a conditional feature indicates low trust, is a subset of the range of outputs when a conditional feature indicates high trust. When lattices have 2 vertices in each constrained dimension, this implies edgeworth trust (which only constrains the size of the relevant ranges). With more than 2 lattice vertices per dimension, the two constraints diverge and are not necessarily 'weaker' or 'stronger' than each other - edgeworth trust acts throughout the lattice interior on delta shifts in the main feature, while trapezoid trust only acts on the min and max extremes of the main feature, constraining the overall range of outputs across the domain of the main feature. The two types of trust constraints can be applied jointly.
• Monotonic Dominance: constrains the function to require the effect (slope) in the direction of the dominant dimension to be greater than that of the weak dimension for any point in the lattice. Both dominant and weak dimensions must be monotonic. Note that this constraint might not be strictly satisified at the end of training. In such cases, increase the number of projection iterations.
• Range Dominance: constraints the function to require the range of possible outputs to be greater than if one varies the dominant dimension than if one varies the weak dimension for any point. Both dominant and weak dimensions must be monotonic. Note that this constraint might not be strictly satisified at the end of training. In such cases, increase the number of projection iterations.
• Joint Monotonicity: constrains the function to be monotonic along a diagonal direction of a two dimensional subspace when all other dimensions are fixed. For example, if our function is scoring the profit given A hotel guests and B hotel beds, it may be wrong to constrain the profit to be increasing in either hotel guests or hotel beds in-dependently, but along the diagonal (+ 1 guest and +1 bed), the profit should be monotonic. Note that this constraint might not be strictly satisified at the end of training. In such cases, increase the number of projection iterations.

There are upper and lower bound constraints on the output.

All units share the same layer configuration, but each has their separate set of trained parameters.

Example:

lattice = tfl.layers.Lattice(
    # Number of vertices along each dimension.
    lattice_sizes=[2, 2, 3, 4, 2, 2, 3],
    # You can specify monotonicity constraints.
    monotonicities=['increasing', 'none', 'increasing', 'increasing',
                    'increasing', 'increasing', 'increasing'],
    # You can specify trust constraints between pairs of features. Here we
    # constrain the function to be more responsive to a main feature (index 4)
    # as a secondary conditional feature (index 3) increases (positive
    # direction).
    edgeworth_trusts=(4, 3, 'positive'),
    # Output can be bounded.
    output_min=0.0,
    output_max=1.0)

self.input_spec = tf.keras.layers.InputSpec(ndim=4)

ValueError: Input 0 of layer conv2d is incompatible with the layer:
expected ndim=4, found ndim=1. Full shape received: [2]

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.
len(model.losses)
0
model.add_loss(tf.abs(tf.reduce_mean(x)))
len(model.losses)
1

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>]

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

Methods

add_loss

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:

class MyLayer(tf.keras.layers.Layer):
  def call(self, inputs):
    self.add_loss(tf.abs(tf.reduce_mean(inputs)))
    return inputs

The same code works in distributed training: the input to add_loss() is treated like a regularization loss and averaged across replicas by the training loop (both built-in Model.fit() and compliant custom training loops).

The add_loss 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 Inputs. 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 zero-argument 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,))
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))

assert_constraints

View source

assert_constraints(
    eps=1e-06
)

Asserts that weights satisfy all constraints.

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

build

View source

build(
    input_shape
)

Standard Keras build() method.

build_from_config

build_from_config(
    config
)

Builds the layer's states with the supplied config dict.

By default, this method calls the build(config["input_shape"]) method, which creates weights based on the layer's input shape in the supplied config. If your config contains other information needed to load the layer's state, you should override this method.

compute_mask

compute_mask(
    inputs, mask=None
)

Computes an output mask tensor.

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.

finalize_constraints

View source

finalize_constraints()

Ensures layers weights strictly satisfy constraints.

Applies approximate projection to strictly satisfy specified constraints. If monotonic_at_every_step == True there is no need to call this function.

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).

get_build_config

get_build_config()

Returns a dictionary with the layer's input shape.

This method returns a config dict that can be used by build_from_config(config) to create all states (e.g. Variables and Lookup tables) needed by the layer.

By default, the config only contains the input shape that the layer was built with. If you're writing a custom layer that creates state in an unusual way, you should override this method to make sure this state is already created when TF-Keras attempts to load its value upon model loading.

get_config

View source

get_config()

Standard Keras config for serialization.

get_weights

get_weights()

Returns the current weights of the layer, as NumPy arrays.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable 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: the kernel matrix and the bias vector. These can be used to set the weights of another Dense layer:

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

load_own_variables

load_own_variables(
    store
)

Loads the state of the layer.

You can override this method to take full control of how the state of the layer is loaded upon calling keras.models.load_model().

save_own_variables

save_own_variables(
    store
)

Saves the state of the layer.

You can override this method to take full control of how the state of the layer is saved upon calling model.save().

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: the kernel matrix and the bias vector. These can be used to set the weights of another Dense layer:

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

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.Variables and tf.Tensors 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)>

__call__

__call__(
    *args, **kwargs
)

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