tf.compat.v1.get_variable

Gets an existing variable with these parameters or create a new one.

Used in the notebooks

Used in the guide

This function prefixes the name with the current variable scope and performs reuse checks. See the Variable Scope How To for an extensive description of how reusing works. Here is a basic example:

def foo():
  with tf.variable_scope("foo", reuse=tf.AUTO_REUSE):
    v = tf.get_variable("v", [1])
  return v

v1 = foo()  # Creates v.
v2 = foo()  # Gets the same, existing v.
assert v1 == v2

If initializer is None (the default), the default initializer passed in the variable scope will be used. If that one is None too, a glorot_uniform_initializer will be used. The initializer can also be a Tensor, in which case the variable is initialized to this value and shape.

Similarly, if the regularizer is None (the default), the default regularizer passed in the variable scope will be used (if that is None too, then by default no regularization is performed).

If a partitioner is provided, a PartitionedVariable is returned. Accessing this object as a Tensor returns the shards concatenated along the partition axis.

Some useful partitioners are available. See, e.g., variable_axis_size_partitioner and min_max_variable_partitioner.

name The name of the new or existing variable.
shape Shape of the new or existing variable.
dtype Type of the new or existing variable (defaults to DT_FLOAT).
initializer Initializer for the variable if one is created. Can either be an initializer object or a Tensor. If it's a Tensor, its shape must be known unless validate_shape is False.
regularizer