tf.train.init_from_checkpoint

tf.train.init_from_checkpoint(
    ckpt_dir_or_file,
    assignment_map
)

Defined in tensorflow/python/training/checkpoint_utils.py.

Initializes current variables with tensors loaded from given checkpoint.

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:

# 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.variable_scope('new_scope_1'):
  var1 = tf.get_variable('var1', shape=[20, 2],
                         initializer=tf.zeros_initializer())
with tf.variable_scope('new_scope_2'):
  var2 = tf.get_variable('var2', shape=[50, 4],
                         initializer=tf.zeros_initializer())
  # Partition into 5 variables along the first axis.
  var3 = tf.get_variable(name='var3', shape=[100, 100],
                         initializer=tf.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',
                     {'old_scope_1/var1': var1,
                      'old_scope_1/var2': var2})

# Initialize partitioned variables using variable's name
init_from_checkpoint('/tmp/model.ckpt',
                     {'old_scope_2/var3': 'new_scope_2/var3'})

# Or specify the list of tf.Variable objects.
init_from_checkpoint('/tmp/model.ckpt',
                     {'old_scope_2/var3': var3._get_variable_list()})

Args:

• ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.
• assignment_map: Dict, where keys are names of the variables in the checkpoint and values are current variables or names of current variables (in default graph).

Raises:

• tf.errors.OpError: If missing checkpoints or tensors in checkpoints.
• ValueError: If missing variables in current graph.