tfl.layers.RTL

View source on GitHub

Layer which includes a random ensemble of lattices.

tfl.layers.RTL(
    num_lattices,
    lattice_rank,
    lattice_size=2,
    output_min=None,
    output_max=None,
    init_min=None,
    init_max=None,
    separate_outputs=False,
    random_seed=42,
    num_projection_iterations=10,
    monotonic_at_every_step=True,
    clip_inputs=True,
    interpolation='hypercube',
    parameterization='all_vertices',
    num_terms=2,
    avoid_intragroup_interaction=True,
    kernel_initializer='random_monotonic_initializer',
    kernel_regularizer=None,
    average_outputs=False,
    **kwargs
)

RTL (Random Tiny Lattices) is an ensemble of tfl.layers.Lattice layers that takes in a collection of monotonic and unconstrained features and randomly arranges them into lattices of a given rank. The input is taken as "groups", and inputs from the same group will not be used in the same lattice. E.g. the input can be the output of a calibration layer with multiple units applied to the same input feature. If there are more slots in the RTL than the number of inputs, inputs will be repeatedly used. Repeats will be approximately uniform across all inputs.

Input shape:

Output shape:

If separate_outputs == True, the output will be in the same format as the input and can be passed to follow on RTL layers: {'unconstrained': unconstrained_out, 'increasing': mon_out} where unconstrained_out and mon_out are of (batch_size, num_unconstrained_out) and (batch_size, num_mon_out) respectively, and num_unconstrained_out + num_mon_out == num_lattices. If separate_outputs == False the output will be a rank-2 tensor with shape: (batch_size, num_lattices) if average_outputs is False, or (batch_size, 1) if True.

Example:

a = keras.Input(shape=(1,))
b = keras.Input(shape=(1,))
c = keras.Input(shape=(1,))
d = keras.Input(shape=(1,))
cal_a = tfl.layers.CategoricalCalibration(
    units=10, output_min=0, output_max=1, ...)(a)
cal_b = tfl.layers.PWLCalibration(
    units=20, output_min=0, output_max=1, ...)(b)
cal_c = tfl.layers.PWLCalibration(
    units=10, output_min=0, output_max=1, monotonicity='increasing', ...)(c)
cal_d = tfl.layers.PWLCalibration(
    units=20, output_min=0, output_max=1, monotonicity='decreasing', ...)(d)
rtl_0 = RTL(
    num_lattices=20,
    lattice_rank=3,
    output_min=0,
    output_max=1,
    separate_outputs=True,
)({
    'unconstrained': [cal_a, cal_b],
    'increasing': [cal_c, cal_d],
})
rtl_1 = RTL(num_lattices=5, lattice_rank=4)(rtl_0)
outputs = tfl.layers.Linear(
    num_input_dims=5,
    monotonicities=['increasing'] * 5,
)(rtl_1)
model = keras.Model(inputs=[a, b, c, d], outputs=outputs)

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 a 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 get_config() method.

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.