|---| | [ View source on GitHub](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/experimental/nn/convolutional_layers.py#L206-L460) | Convolution layer class with reparameterization estimator. Inherits From: [`VariationalLayer`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/nn/VariationalLayer), [`Layer`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/nn/Layer) tfp.experimental.nn.ConvolutionVariationalReparameterization( input_size, output_size, filter_shape, rank=2, strides=1, padding='VALID', dilations=1, kernel_initializer=None, bias_initializer=None, make_posterior_fn=https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/nn/util/make_kernel_bias_posterior_mvn_diag, make_prior_fn=https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/nn/util/make_kernel_bias_prior_spike_and_slab, posterior_value_fn=https://www.tensorflow.org/probability/api_docs/python/tfp/distributions/Distribution#sample, unpack_weights_fn=unpack_kernel_and_bias, dtype=tf.float32, activation_fn=None, seed=None, validate_args=False, name=None ) This layer implements the Bayesian variational inference analogue to a Convolution 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, kernel, bias ~ posterior outputs = matmul(inputs, kernel) + bias It uses the reparameterization estimator \[(Kingma and Welling, 2014)\]\[1\], which performs a Monte Carlo approximation of the distribution integrating over the `kernel` and `bias`. 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. #### Examples We illustrate a Bayesian neural network with [variational inference](https://en.wikipedia.org/wiki/Variational_Bayesian_methods), assuming a dataset of images and length-10 one-hot `targets`. import functools import tensorflow.compat.v2 as tf import tensorflow_probability as tfp import tensorflow_datasets as tfds tfb = tfp.bijectors tfd = tfp.distributions tfn = tfp.experimental.nn # 1 Prepare Dataset [train_dataset, eval_dataset], datasets_info = tfds.load( name='mnist', split=['train', 'test'], with_info=True, as_supervised=True, shuffle_files=True) def _preprocess(image, label): # image = image < tf.random.uniform(tf.shape(image)) # Randomly binarize. image = tf.cast(image, tf.float32) / 255. # Scale to unit interval. lo = 0.001 image = (1. - 2. * lo) * image + lo # Rescale to *open* unit interval. return image, label batch_size = 32 train_size = datasets_info.splits['train'].num_examples train_dataset = tfn.util.tune_dataset( train_dataset, batch_shape=(batch_size,), shuffle_size=int(train_size / 7), preprocess_fn=_preprocess) train_iter = iter(train_dataset) eval_iter = iter(eval_dataset) x, y = next(train_iter) evidence_shape = x.shape[1:] targets_shape = y.shape[1:] # 2 Specify Model n = tf.cast(train_size, tf.float32) BayesConv2D = functools.partial( tfn.ConvolutionVariationalReparameterization, rank=2, padding='same', filter_shape=5, # Use `he_uniform` because we'll use the `relu` family. kernel_initializer=tf_keras.initializers.he_uniform(), penalty_weight=1. / n) BayesAffine = functools.partial( tfn.AffineVariationalReparameterization, penalty_weight=1. / n) scale = tfp.util.TransformedVariable(1., tfb.Softplus()) bnn = tfn.Sequential([ BayesConv2D(evidence_shape[-1], 32, filter_shape=7, strides=2, activation_fn=tf.nn.leaky_relu), # [b, 14, 14, 32] tfn.util.flatten_rightmost(ndims=3), # [b, 14 * 14 * 32] BayesAffine(14 * 14 * 32, np.prod(target_shape) - 1), # [b, 9] lambda loc: tfb.SoftmaxCentered()( tfd.Independent(tfd.Normal(loc, scale), reinterpreted_batch_ndims=1)), # [b, 10] ], name='bayesian_neural_network') print(bnn.summary()) # 3 Train. def loss_fn(): x, y = next(train_iter) nll = -tf.reduce_mean(bnn(x).log_prob(y), axis=-1) kl = bnn.extra_loss # Already normalized via `penalty_weight` arg. loss = nll + kl return loss, (nll, kl) opt = tf_keras.optimizers.Adam() fit_op = tfn.util.make_fit_op(loss_fn, opt, bnn.trainable_variables) for _ in range(200): loss, (nll, kl), g = fit_op() This example uses reparameterization gradients 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 \[1\]: Diederik Kingma and Max Welling. Auto-Encoding Variational Bayes. In *International Conference on Learning Representations* , 2014.


| ## Args || |---|---| | `input_size` | ... In Keras, this argument is inferred from the rightmost input shape, i.e., `tf.shape(inputs)[-1]`. This argument specifies the size of the second from the rightmost dimension of both `inputs` and `kernel`. Default value: `None`. | | `output_size` | ... In Keras, this argument is called `filters`. This argument specifies the rightmost dimension size of both `kernel` and `bias`. | | `filter_shape` | ... In Keras, this argument is called `kernel_size`. This argument specifies the leftmost `rank` dimensions' sizes of `kernel`. | | `rank` | An integer, the rank of the convolution, e.g. "2" for 2D convolution. This argument implies the number of `kernel` dimensions, i.e., `kernel.shape.rank == rank + 2`. In Keras, this argument has the same name and semantics. Default value: `2`. | | `strides` | An integer or tuple/list of n integers, specifying the stride length of the convolution. In Keras, this argument has the same name and semantics. Default value: `1`. | | `padding` | One of `"VALID"` or `"SAME"` (case-insensitive). In Keras, this argument has the same name and semantics (except we don't support `"CAUSAL"`). Default value: `'VALID'`. | | `dilations` | An integer or tuple/list of `rank` integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilations` value != 1 is incompatible with specifying any `strides` value != 1. In Keras, this argument is called `dilation_rate`. Default value: `1`. | | `kernel_initializer` | ... Default value: `None` (i.e., [`tfp.experimental.nn.initializers.glorot_uniform()`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/nn/initializers/glorot_uniform)). | | `bias_initializer` | ... Default value: `None` (i.e., `tf_keras.initializers.zeros()`). | | `make_posterior_fn` | ... Default value: [`tfp.experimental.nn.util.make_kernel_bias_posterior_mvn_diag`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/nn/util/make_kernel_bias_posterior_mvn_diag). | | `make_prior_fn` | ... Default value: [`tfp.experimental.nn.util.make_kernel_bias_prior_spike_and_slab`](https://www.tensorflow.org/probability/api_docs/python/tfp/experimental/nn/util/make_kernel_bias_prior_spike_and_slab). | | `posterior_value_fn` | ... Default valye: `tfd.Distribution.sample` | | `unpack_weights_fn` | Default value: `unpack_kernel_and_bias` | | `dtype` | ... Default value: [`tf.float32`](https://www.tensorflow.org/api_docs/python/tf#float32). | | `activation_fn` | ... Default value: `None`. | | `seed` | ... Default value: `None` (i.e., no seed). | | `validate_args` | ... | | `name` | ... Default value: `None` (i.e., `'ConvolutionVariationalReparameterization'`). |



| ## Attributes || |---|---| | `activation_fn` |

| | `also_track` |

| | `dtype` |

| | `name` | Returns the name of this module as passed or determined in the ctor.
> [!NOTE] > **Note:** This is not the same as the `self.name_scope.name` which includes parent module names. | | `name_scope` | Returns a [`tf.name_scope`](https://www.tensorflow.org/api_docs/python/tf/name_scope) instance for this class. | | `non_trainable_variables` | Sequence of non-trainable variables owned by this module and its submodules. > [!NOTE] > **Note:** this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change. | | `posterior` |
| | `posterior_value` |

| | `posterior_value_fn` |

| | `prior` |

| | `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 list(a.submodules) == [b, c] True list(b.submodules) == [c] True list(c.submodules) == [] True
| | `trainable_variables` | Sequence of trainable variables owned by this module and its submodules.
> [!NOTE] > **Note:** this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change. | | `unpack_weights_fn` |
| | `validate_args` | Python `bool` indicating possibly expensive checks are enabled. | | `variables` | Sequence of variables owned by this module and its submodules.
> [!NOTE] > **Note:** this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change. | ## Methods ### `load` [View source](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/experimental/nn/layers.py#L55-L56) load( filename ) ### `save` [View source](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/experimental/nn/layers.py#L52-L53) save( filename ) ### `summary` [View source](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/experimental/nn/layers.py#L49-L50) summary() ### `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.Variable`](https://www.tensorflow.org/api_docs/python/tf/Variable)s and [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/tf/Tensor)s whose names included the module name: mod = MyModule() mod(tf.ones([1, 2])) mod.w


| Args || |---|---| | `method` | The method to wrap. |



| Returns || |---|---| | The original method wrapped such that it enters the module's name scope. ||
### `__call__` [View source](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/experimental/nn/variational_base.py#L147-L159) __call__( x, **kwargs ) Call self as a function.