2D convolution layer (e.g. spatial convolution over images) with Flipout

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.

layer_conv_2d_flipout(
  object,
  filters,
  kernel_size,
  strides = 1,
  padding = "valid",
  data_format = "channels_last",
  dilation_rate = 1,
  activation = NULL,
  activity_regularizer = NULL,
  trainable = TRUE,
  kernel_posterior_fn = tfp$layers$util$default_mean_field_normal_fn(),
  kernel_posterior_tensor_fn = function(d) d %>% tfd_sample(),
  kernel_prior_fn = tfp$layers$util$default_multivariate_normal_fn,
  kernel_divergence_fn = function(q, p, ignore) tfd_kl_divergence(q, p),
  bias_posterior_fn = tfp$layers$util$default_mean_field_normal_fn(is_singular = TRUE),
  bias_posterior_tensor_fn = function(d) d %>% tfd_sample(),
  bias_prior_fn = NULL,
  bias_divergence_fn = function(q, p, ignore) tfd_kl_divergence(q, p),
  ...
)

Arguments

object	Model or layer object
filters	Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).
kernel_size	An integer or list of a single integer, specifying the length of the 1D convolution window.
strides	An integer or list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
padding	One of "valid" or "same" (case-insensitive).
data_format	A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, length, channels) while channels_first corresponds to inputs with shape (batch, channels, length).
dilation_rate	An integer or tuple/list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any strides value != 1.
activation	Activation function. Set it to None to maintain a linear activation.
activity_regularizer	Regularizer function for the output.
trainable	Whether the layer weights will be updated during training.
kernel_posterior_fn	Function which creates tfd$Distribution instance representing the surrogate posterior of the kernel parameter. Default value: default_mean_field_normal_fn().
kernel_posterior_tensor_fn	Function which takes a tfd$Distribution instance and returns a representative value. Default value: function(d) d %>% tfd_sample().
kernel_prior_fn	Function which creates tfd$Distribution instance. See default_mean_field_normal_fn docstring for required parameter signature. Default value: tfd_normal(loc = 0, scale = 1).
kernel_divergence_fn	Function which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are tfd$Distribution-like instances and the sample is a Tensor.
bias_posterior_fn	Function which creates a tfd$Distribution instance representing the surrogate posterior of the bias parameter. Default value: default_mean_field_normal_fn(is_singular = TRUE) (which creates an instance of tfd_deterministic).
bias_posterior_tensor_fn	Function which takes a tfd$Distribution instance and returns a representative value. Default value: function(d) d %>% tfd_sample().
bias_prior_fn	Function which creates tfd instance. See default_mean_field_normal_fn docstring for required parameter signature. Default value: NULL (no prior, no variational inference)
bias_divergence_fn	Function which takes the surrogate posterior distribution, prior distribution and random variate sample(s) from the surrogate posterior and computes or approximates the KL divergence. The distributions are tfd$Distribution-like instances and the sample is a Tensor.
...	Additional keyword arguments passed to the keras::layer_dense constructed by this layer.

Value

a Keras layer

Details

This layer implements the Bayesian variational inference analogue to a dense layer by assuming the kernel and/or the 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), 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). You can access the kernel and/or bias posterior and prior distributions after the layer is built via the kernel_posterior, kernel_prior, bias_posterior and bias_prior properties.

References

• 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.

See also

Other layers: layer_autoregressive(), layer_conv_1d_flipout(), layer_conv_1d_reparameterization(), layer_conv_2d_reparameterization(), layer_conv_3d_flipout(), layer_conv_3d_reparameterization(), layer_dense_flipout(), layer_dense_local_reparameterization(), layer_dense_reparameterization(), layer_dense_variational(), layer_variable()