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tfp.experimental.nn.ConvolutionTranspose

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ConvolutionTranspose layer.

tfp.experimental.nn.ConvolutionTranspose(
    input_size, output_size, filter_shape, rank=2, strides=1, padding='VALID',
    dilations=1, output_padding=None, init_kernel_fn=None, init_bias_fn=None,
    make_kernel_bias_fn=tfp.experimental.nn.util.make_kernel_bias, dtype=tf.float32,
    activation_fn=None, name=None
)

This layer creates a ConvolutionTranspose kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs.

This layer has two learnable parameters, kernel and bias.

  • The kernel (aka filters argument of tf.nn.conv_transpose) is a tf.Variable with rank + 2 ndims and shape given by concat([filter_shape, [input_size, output_size]], axis=0). Argument filter_shape is either a length-rank vector or expanded as one, i.e., filter_size * tf.ones(rank) when filter_shape is an int (which we denote as filter_size).
  • The bias is a tf.Variable with 1 ndims and shape [output_size].

In summary, the shape of learnable parameters is governed by the following arguments: filter_shape, input_size, output_size and possibly rank (if filter_shape needs expansion).

For more information on convolution layers, we recommend the following:

  • [Deconvolution Checkerboard][https://distill.pub/2016/deconv-checkerboard/]
  • [Convolution Animations][https://github.com/vdumoulin/conv_arithmetic]
  • [What are Deconvolutional Layers?][ https://datascience.stackexchange.com/questions/6107/what-are-deconvolutional-layers]

Examples

import tensorflow as tf
import tensorflow_probability as tfp
tfb = tfp.bijectors
tfd = tfp.distributions
tfn = tfp.experimental.nn

ConvolutionTranspose1D = functools.partial(tfn.ConvolutionTranspose, rank=1)
ConvolutionTranspose2D = tfn.ConvolutionTranspose
ConvolutionTranspose3D = functools.partial(tfn.ConvolutionTranspose, rank=3)

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.
  • output_padding: An int or length-rank tuple/list representing the amount of padding along the input spatial dimensions (e.g., depth, height, width). A single int indicates the same value for all spatial dimensions. The amount of output padding along a given dimension must be lower than the stride along that same dimension. If set to None (default), the output shape is inferred. In Keras, this argument has the same name and semantics. Default value: None (i.e., inferred).
  • init_kernel_fn: ... Default value: None (i.e., tfp.experimental.nn.initializers.glorot_uniform()).
  • init_bias_fn: ... Default value: None (i.e., tf.zeros).
  • make_kernel_bias_fn: ... Default value: tfp.experimental.nn.util.make_kernel_bias.
  • dtype: ... Default value: tf.float32.
  • activation_fn: ... Default value: None.
  • name: ... Default value: None (i.e., 'ConvolutionTranspose').

Attributes:

  • activation_fn
  • also_track
  • bias
  • dtype
  • extra_loss
  • extra_result
  • kernel
  • name: Returns the name of this module as passed or determined in the ctor.

    NOTE: This is not the same as the self.name_scope.name which includes parent module names.

  • name_scope: Returns a tf.name_scope instance for this class.

  • 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.

  • variables: Sequence of variables owned by this module and its submodules.

Methods

__call__

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__call__(
    inputs, **kwargs
)

Call self as a function.

eval

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eval(
    x, is_training=True
)

load

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load(
    filename
)

save

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save(
    filename
)

summary

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summary()

with_name_scope

@classmethod
with_name_scope(
    cls, 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)> 

Args:

  • method: The method to wrap.

Returns:

The original method wrapped such that it enters the module's name scope.