Class Variable

tensorflow/python/ops/variables.py:468–1565

See the [Variables Guide](https://tensorflow.org/guide/variables). A variable maintains state in the graph across calls to `run()`. You add a variable to the graph by constructing an instance of the class `Variable`. The `Variable()` constructor requires an initial value for the variable,

@tf_export("Variable", v1=[])
class Variable(six.with_metaclass(VariableMetaclass, trackable.Trackable)):
  """See the [Variables Guide](https://tensorflow.org/guide/variables).

  A variable maintains state in the graph across calls to `run()`. You add a
  variable to the graph by constructing an instance of the class `Variable`.

  The `Variable()` constructor requires an initial value for the variable,
  which can be a `Tensor` of any type and shape. The initial value defines the
  type and shape of the variable. After construction, the type and shape of
  the variable are fixed. The value can be changed using one of the assign
  methods.

  If you want to change the shape of a variable later you have to use an
  `assign` Op with `validate_shape=False`.

  Just like any `Tensor`, variables created with `Variable()` can be used as
  inputs for other Ops in the graph. Additionally, all the operators
  overloaded for the `Tensor` class are carried over to variables, so you can
  also add nodes to the graph by just doing arithmetic on variables.

  ```python
  import tensorflow as tf

  # Create a variable.
  w = tf.Variable(<initial-value>, name=<optional-name>)

  # Use the variable in the graph like any Tensor.
  y = tf.matmul(w, ...another variable or tensor...)

  # The overloaded operators are available too.
  z = tf.sigmoid(w + y)

  # Assign a new value to the variable with `assign()` or a related method.
  w.assign(w + 1.0)
  w.assign_add(1.0)
  ```

  When you launch the graph, variables have to be explicitly initialized before
  you can run Ops that use their value. You can initialize a variable by
  running its *initializer op*, restoring the variable from a save file, or
  simply running an `assign` Op that assigns a value to the variable. In fact,
  the variable *initializer op* is just an `assign` Op that assigns the
  variable&#x27;s initial value to the variable itself.

  ```python
  # Launch the graph in a session.
  with tf.compat.v1.Session() as sess:
      # Run the variable initializer.
      sess.run(w.initializer)
      # ...you now can run ops that use the value of 'w'...
  ```

  The most common initialization pattern is to use the convenience function
  `global_variables_initializer()` to add an Op to the graph that initializes
  all the variables. You then run that Op after launching the graph.

  ```python
  # Add an Op to initialize global variables.