description: Convolution layer.
View source on GitHub |
Convolution layer.
Inherits From: Layer
tfp.experimental.nn.ConvolutionV2(
input_size, output_size, filter_shape, rank=2, strides=1,
padding='VALID', dilations=1, kernel_initializer=None,
bias_initializer=None,
make_kernel_bias_fn=tfp.experimental.nn.util.make_kernel_bias, dtype=tf.float32,
index_dtype=tf.int32, batch_shape=(), activation_fn=None, validate_args=False,
name=None
)
This layer creates a Convolution kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs.
This V2 version supports alternative batch semantics. V1 layers, with batch
size B
produced outputs of shape [N, H, W, B, C]
(where N, H, W, C are
minibatch size, height, width and number of channels, as usual). V2 layers
reorder these to [N, B, H, W, C]
.
This layer has two learnable parameters, kernel
and bias
.
- The kernel
(aka filters
argument of tf.nn.convolution
) 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]
import tensorflow as tf
import tensorflow_probability as tfp
tfb = tfp.bijectors
tfd = tfp.distributions
tfn = tfp.experimental.nn
Convolution1DV2 = functools.partial(tfn.ConvolutionV2, rank=1)
Convolution2DV2 = tfn.ConvolutionV2
Convolution3DV2 = functools.partial(tfn.ConvolutionV2, 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 .
|
kernel_initializer
|
...
Default value: None (i.e.,
tfp.experimental.nn.initializers.glorot_uniform() ).
|
bias_initializer
|
...
Default value: None (i.e., tf.initializers.zeros() ).
|
make_kernel_bias_fn
|
...
Default value: tfp.experimental.nn.util.make_kernel_bias .
|
dtype
|
...
Default value: tf.float32 .
|
index_dtype
|
... |
batch_shape
|
...
Default value: () .
|
activation_fn
|
...
Default value: None .
|
validate_args
|
... |
name
|
...
Default value: None (i.e., 'ConvolutionV2' ).
|
Attributes | |
---|---|
activation_fn
|
|
also_track
|
|
bias
|
|
dtype
|
|
kernel
|
|
name
|
Returns the name of this module as passed or determined in the ctor.
NOTE: This is not the same as the |
name_scope
|
Returns a tf.name_scope instance for this class.
|
non_trainable_variables
|
Sequence of non-trainable variables owned by this module and its submodules.
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. |
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: 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. |
validate_args
|
Python bool indicating possibly expensive checks are enabled.
|
variables
|
Sequence of variables owned by this module and its submodules.
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. |
load
load(
filename
)
save
save(
filename
)
summary
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
s and tf.Tensor
s 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__(
x
)
Call self as a function.