description: Basic affine layer.
View source on GitHub |
Basic affine layer.
Inherits From: Layer
tfp.experimental.nn.Affine(
input_size, output_size, kernel_initializer=None, bias_initializer=None,
make_kernel_bias_fn=tfp.experimental.nn.util.make_kernel_bias, dtype=tf.float32,
batch_shape=(), activation_fn=None, name=None
)
Args | |
---|---|
input_size
|
... |
output_size
|
... |
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 .
|
batch_shape
|
...
Default value: () .
|
activation_fn
|
...
Default value: None .
|
name
|
...
Default value: None (i.e., 'Affine' ).
|
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.