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. |
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. |
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).
|
trainable_variables
|
Sequence of trainable variables owned by this module and its submodules. |
validate_args
|
Python bool indicating possibly expensive checks are enabled.
|
variables
|
Sequence of variables owned by this module and its submodules. |
Methods
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.