LostTech.TensorFlow : API Documentation

Type Module

Namespace tensorflow

Parent AutoTrackable

Interfaces IModule

Base neural network module class.

A module is a named container for tf.Variables, other tf.Modules and functions which apply to user input. For example a dense layer in a neural network might be implemented as a tf.Module: You can use the Dense layer as you would expect: By subclassing tf.Module instead of `object` any tf.Variable or tf.Module instances assigned to object properties can be collected using the `variables`, `trainable_variables` or `submodules` property: Subclasses of tf.Module can also take advantage of the `_flatten` method which can be used to implement tracking of any other types.

All tf.Module classes have an associated tf.name_scope which can be used to group operations in TensorBoard and create hierarchies for variable names which can help with debugging. We suggest using the name scope when creating nested submodules/parameters or for forward methods whose graph you might want to inspect in TensorBoard. You can enter the name scope explicitly using `with self.name_scope:` or you can annotate methods (apart from `__init__`) with `@tf.Module.with_name_scope`.
Show Example 
class Dense(tf.Module):
              def __init__(self, in_features, output_features, name=None):
                super(Dense, self).__init__(name=name)
                self.w = tf.Variable(
                    tf.random.normal([input_features, output_features]), name='w')
                self.b = tf.Variable(tf.zeros([output_features]), name='b') 
 def __call__(self, x):
  y = tf.matmul(x, self.w) + self.b
  return tf.nn.relu(y)

Methods

Properties

Public static methods

object with_name_scope_dyn<TClass>(object 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], 64])) 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([8, 32])) # ==> mod.w # ==> ```
Parameters
object method
The method to wrap.
Returns
object
The original method wrapped such that it enters the module's name scope.

TClass with_name_scope<TClass>(PythonFunctionContainer 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], 64])) 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([8, 32])) # ==> mod.w # ==> ```
Parameters
PythonFunctionContainer method
The method to wrap.
Returns
TClass
The original method wrapped such that it enters the module's name scope.

Public properties

object name get;

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.

object name_dyn get;

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.

object name_scope get;

Returns a tf.name_scope instance for this class.

object name_scope_dyn get;

Returns a tf.name_scope instance for this class.

object PythonObject get;

ValueTuple<object> submodules get;

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 assert list(a.submodules) == [b, c] assert list(b.submodules) == [c] assert list(c.submodules) == [] ```

object submodules_dyn get;

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 assert list(a.submodules) == [b, c] assert list(b.submodules) == [c] assert list(c.submodules) == [] ```

object trainable_variables get;

Sequence of variables owned by this module and it's 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.

object trainable_variables_dyn get;

Sequence of variables owned by this module and it's 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.

object variables get;

Sequence of variables owned by this module and it's 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.

object variables_dyn get;

Sequence of variables owned by this module and it's 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.