Function init_from_checkpoint

tensorflow/python/training/checkpoint_utils.py:207–294

Replaces `tf.Variable` initializers so they load from a checkpoint file. Values are not loaded immediately, but when the initializer is run (typically by running a `tf.compat.v1.global_variables_initializer` op). Note: This overrides default initialization ops of specified variables and re

(ckpt_dir_or_file, assignment_map, reset_version=False)

@tf_export(v1=["train.init_from_checkpoint"])
def init_from_checkpoint(ckpt_dir_or_file, assignment_map, reset_version=False):
  """Replaces `tf.Variable` initializers so they load from a checkpoint file.

  Values are not loaded immediately, but when the initializer is run
  (typically by running a `tf.compat.v1.global_variables_initializer` op).

  Note: This overrides default initialization ops of specified variables and
  redefines dtype.

  Assignment map supports following syntax:

  * `'checkpoint_scope_name/': 'scope_name/'` - will load all variables in
    current `scope_name` from `checkpoint_scope_name` with matching tensor
    names.
  * `'checkpoint_scope_name/some_other_variable': 'scope_name/variable_name'` -
    will initialize `scope_name/variable_name` variable
    from `checkpoint_scope_name/some_other_variable`.
  * `'scope_variable_name': variable` - will initialize given `tf.Variable`
    object with tensor 'scope_variable_name' from the checkpoint.
  * `'scope_variable_name': list(variable)` - will initialize list of
    partitioned variables with tensor 'scope_variable_name' from the checkpoint.
  * `'/': 'scope_name/'` - will load all variables in current `scope_name` from
    checkpoint's root (e.g. no scope).

  Supports loading into partitioned variables, which are represented as
  `'<variable>/part_<part #>'`.

  Example:

  ```python

  # Say, '/tmp/model.ckpt' has the following tensors:
  #  -- name='old_scope_1/var1', shape=[20, 2]
  #  -- name='old_scope_1/var2', shape=[50, 4]
  #  -- name='old_scope_2/var3', shape=[100, 100]

  # Create new model's variables
  with tf.compat.v1.variable_scope('new_scope_1'):
    var1 = tf.compat.v1.get_variable('var1', shape=[20, 2],
                           initializer=tf.compat.v1.zeros_initializer())
  with tf.compat.v1.variable_scope('new_scope_2'):
    var2 = tf.compat.v1.get_variable('var2', shape=[50, 4],
                           initializer=tf.compat.v1.zeros_initializer())
    # Partition into 5 variables along the first axis.
    var3 = tf.compat.v1.get_variable(name='var3', shape=[100, 100],
                           initializer=tf.compat.v1.zeros_initializer(),
                           partitioner=lambda shape, dtype: [5, 1])

  # Initialize all variables in `new_scope_1` from `old_scope_1`.
  init_from_checkpoint('/tmp/model.ckpt', {'old_scope_1/': 'new_scope_1'})

  # Use names to specify which variables to initialize from checkpoint.
  init_from_checkpoint('/tmp/model.ckpt',
                       {'old_scope_1/var1': 'new_scope_1/var1',
                        'old_scope_1/var2': 'new_scope_2/var2'})

  # Or use tf.Variable objects to identify what to initialize.
  init_from_checkpoint('/tmp/model.ckpt',