tf.contrib.framework.init_from_checkpoint(checkpoint_dir, assignment_map)

tf.contrib.framework.init_from_checkpoint(checkpoint_dir, assignment_map)

See the guide: Framework (contrib) > Checkpoint utilities

Using assingment map initializes current variables with loaded tensors.

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 variable names. 'checkpoint_scope_name/some_other_variable': 'scope_name/variable_name' - will initalize scope_name/variable_name variable from checkpoint_scope_name/some_other_variable. 'scope_variable_name': variable - will initialize given tf.Variable object with variable from the checkpoint. 'scope_variable_name': list(variable) - will initialize list of partitioned variables with variable 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 '/part_'.

Example:

# Create variables.
with tf.variable_scope('test'):
  m = tf.get_variable('my_var')
with tf.variable_scope('test2'):
  var2 = tf.get_variable('my_var')
var3 = tf.get_variable(name="my1", shape=[100, 100],
                       partitioner=lambda shape, dtype: [5, 1])
...
# Specify which variables to intialize from checkpoint.
init_from_checkpoint(checkpoint_dir, {
  'some_var': 'test/my_var',
  'some_scope/': 'test2/'})
...
# Or use `Variable` objects to identify what to initialize.
init_from_checkpoint(checkpoint_dir, {
  'some_scope/var2': var2,
})
# Initialize partitioned variables
init_from_checkpoint(checkpoint_dir, {
  'some_var_from_ckpt': 'part_var',
})
# Or specifying the list of `Variable` objects.
init_from_checkpoint(checkpoint_dir, {
  'some_var_from_ckpt': var3._get_variable_list(),
})
...
# Initialize variables as usual.
session.run(tf.get_all_variables())

Args:

• checkpoint_dir: 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.

Defined in tensorflow/contrib/framework/python/framework/checkpoint_utils.py.