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A callable
tf.Module
characterized by eval(input)
.
tfp.experimental.nn.Layer(
also_track=None, name=None
)
Attributes | |
---|---|
also_track
|
|
extra_loss
|
|
extra_result
|
|
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.
|
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. |
variables
|
Sequence of variables owned by this module and its submodules. |
Methods
eval
eval(
inputs, is_training=True, **kwargs
)
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__(
inputs, **kwargs
)
Call self as a function.