View source on GitHub
|
3D convolution layer (e.g. spatial convolution over volumes) with Flipout.
tfp.layers.Convolution3DFlipout(
filters,
kernel_size,
strides=(1, 1, 1),
padding='valid',
data_format='channels_last',
dilation_rate=(1, 1, 1),
activation=None,
activity_regularizer=None,
kernel_posterior_fn=tfp_layers_util.default_mean_field_normal_fn(),
kernel_posterior_tensor_fn=(lambda d: d.sample()),
kernel_prior_fn=tfp.layers.default_multivariate_normal_fn,
kernel_divergence_fn=(lambda q, p, ignore: kl_lib.kl_divergence(q, p)),
bias_posterior_fn=tfp_layers_util.default_mean_field_normal_fn(is_singular=True),
bias_posterior_tensor_fn=(lambda d: d.sample()),
bias_prior_fn=None,
bias_divergence_fn=(lambda q, p, ignore: kl_lib.kl_divergence(q, p)),
seed=None,
**kwargs
)
This layer creates a convolution kernel that is convolved
(actually cross-correlated) with the layer input to produce a tensor of
outputs. It may also include a bias addition and activation function
on the outputs. It assumes the kernel and/or bias are drawn from
distributions.
By default, the layer implements a stochastic forward pass via sampling from the kernel and bias posteriors,
outputs = f(inputs; kernel, bias), kernel, bias ~ posterior
where f denotes the layer's calculation. It uses the Flipout
estimator [(Wen et al., 2018)][1], which performs a Monte Carlo approximation
of the distribution integrating over the kernel and bias. Flipout uses
roughly twice as many floating point operations as the reparameterization
estimator but has the advantage of significantly lower variance.
The arguments permit separate specification of the surrogate posterior
(q(W|x)), prior (p(W)), and divergence for both the kernel and bias
distributions.
Upon being built, this layer adds losses (accessible via the losses
property) representing the divergences of kernel and/or bias surrogate
posteriors and their respective priors. When doing minibatch stochastic
optimization, make sure to scale this loss such that it is applied just once
per epoch (e.g. if kl is the sum of losses for each element of the batch,
you should pass kl / num_examples_per_epoch to your optimizer).
Examples
We illustrate a Bayesian neural network with variational inference,
assuming a dataset of features and labels.
import tensorflow as tf
import tensorflow_probability as tfp
model = tf.keras.Sequential([
tf.keras.layers.Reshape([256, 32, 32, 3]),
tfp.layers.Convolution3DFlipout(
64, kernel_size=5, padding='SAME', activation=tf.nn.relu),
tf.keras.layers.MaxPooling3D(pool_size=[2, 2, 2],
strides=[2, 2, 2],
padding='SAME'),
tf.keras.layers.Flatten(),
tfp.layers.DenseFlipout(10),
])
logits = model(features)
neg_log_likelihood = tf.nn.softmax_cross_entropy_with_logits(
labels=labels, logits=logits)
kl = sum(model.losses)
loss = neg_log_likelihood + kl
train_op = tf.train.AdamOptimizer().minimize(loss)
It uses the Flipout gradient estimator to minimize the Kullback-Leibler divergence up to a constant, also known as the negative Evidence Lower Bound. It consists of the sum of two terms: the expected negative log-likelihood, which we approximate via Monte Carlo; and the KL divergence, which is added via regularizer terms which are arguments to the layer.
References
[1]: Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, and Roger Grosse. Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. In International Conference on Learning Representations, 2018. https://arxiv.org/abs/1803.04386
Attributes | |
|---|---|
activity_regularizer
|
Optional regularizer function for the output of this layer. |
compute_dtype
|
The dtype of the layer's computations.
This is equivalent to Layers automatically cast their inputs to the compute dtype, which
causes computations and the output to be in the compute dtype as well.
This is done by the base Layer class in Layers often perform certain internal computations in higher precision
when |
dtype
|
The dtype of the layer weights.
This is equivalent to |
dtype_policy
|
The dtype policy associated with this layer.
This is an instance of a |
dynamic
|
Whether the layer is dynamic (eager-only); 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
|
List of losses added using the add_loss() API.
Variable regularization tensors are created when this property is
accessed, so it is eager safe: accessing
|
metrics
|
List of metrics attached to 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 non-trainable weights tracked by this layer.
Non-trainable 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 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).
|
supports_masking
|
Whether this layer supports computing a mask using compute_mask.
|
trainable
|
|
trainable_weights
|
List of all trainable weights tracked by this layer.
Trainable weights are updated via gradient descent during training. |
variable_dtype
|
Alias of Layer.dtype, the dtype of the weights.
|
weights
|
Returns the list of all layer variables/weights. |
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))
| Args | |
|---|---|
losses
|
Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. |
**kwargs
|
Used for backwards compatibility only. |
build
build(
input_shape
)
Creates the variables of the layer (for subclass implementers).
This is a method that implementers of subclasses of Layer or Model
can override if they need a state-creation step in-between
layer instantiation and layer call. It is invoked automatically before
the first execution of call().
This is typically used to create the weights of Layer subclasses
(at the discretion of the subclass implementer).
| Args | |
|---|---|
input_shape
|
Instance of TensorShape, or list of instances of
TensorShape if the layer expects a list of inputs
(one instance per input).
|
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.
| Args | |
|---|---|
config
|
Dict containing the input shape associated with this layer. |
compute_mask
compute_mask(
inputs, mask=None
)
Computes an output mask tensor.
| Args | |
|---|---|
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
)
Computes the output shape of the layer.
| Args | |
|---|---|
input_shape
|
Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer. |
| Returns | |
|---|---|
output_shape
|
A tuple representing the output shape. |
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
@classmethodfrom_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.
| Args | |
|---|---|
config
|
A Python dictionary, typically the output of get_config.
|
| Returns | |
|---|---|
layer
|
A layer instance. |
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 Keras attempts to load its value upon model loading.
| Returns | |
|---|---|
| A dict containing the input shape associated with the layer. |
get_config
get_config()
Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
| Returns | |
|---|---|
config
|
A Python dictionary of class keyword arguments and their serialized values. |
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)]
| Returns | |
|---|---|
| Weights values as a list of NumPy arrays. |
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().
| Args | |
|---|---|
store
|
Dict from which the state of the model will be loaded. |
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().
| Args | |
|---|---|
store
|
Dict where the state of the model will be saved. |
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)]
| Args | |
|---|---|
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
@classmethodwith_name_scope( method )
Decorator to automatically enter the module name scope.
class MyModule(tf.Module):@tf.Module.with_name_scopedef __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)>
| 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 post-processing steps.
| Args | |
|---|---|
*args
|
Positional arguments to be passed to self.call.
|
**kwargs
|
Keyword arguments to be passed to self.call.
|
| Returns | |
|---|---|
| Output tensor(s). |
| Note | |
|---|---|
|
| Raises | |
|---|---|
ValueError
|
if the layer's call method returns None (an invalid
value).
|
RuntimeError
|
if super().__init__() was not called in the
constructor.
|