tfp.experimental.nn.Affine

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 self.name_scope.name which includes parent module names.
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.

Methods

load

View source

load(
    filename
)

save

View source

save(
    filename
)

summary

View source

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.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.

__call__

View source

__call__(
    x
)

Call self as a function.