tf.compat.v1.train.init_from_checkpoint  Replaces tf.Variable initializers so they load from a checkpoint file. 
tf.compat.v1.train.init_from_checkpoint(
    ckpt_dir_or_file, assignment_map
)
  Migrate to TF2 Caution: This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the TensorFlow v1 to TensorFlow v2 migration guide for instructions on how to migrate the rest of your code. tf.compat.v1.train.init_from_checkpoint is not recommended for restoring variable values in TF2. To restore checkpoints in TF2, please use tf.keras.Model.load_weights or tf.train.Checkpoint.restore. These APIs use use an object-based method of checkpointing, while tf.compat.v1.init_from_checkpoint relies on a more-fragile variable-name based method of checkpointing. There is no object-based equivalent of init_from_checkpoint in TF2. Please re-write your checkpoints immediately using the object-based APIs, see migration guide for more details. You can load a name-based checkpoint written by tf.compat.v1.train.Saver using tf.train.Checkpoint.restore or tf.keras.Model.load_weights. However, you may have to change the names of the variables in your model to match the variable names in the name-based checkpoint, which can be viewed with tf.train.list_variables(path). Another option is to create an assignment_map that maps the name of the variables in the name-based checkpoint to the variables in your model, eg: {
    'sequential/dense/bias': model.variables[0],
    'sequential/dense/kernel': model.variables[1]
}
 and use tf.compat.v1.train.init_from_checkpoint(path, assignment_map) to restore the name-based checkpoint. After restoring, re-encode your checkpoint using tf.train.Checkpoint.save or tf.keras.Model.save_weights. Description 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 #>'. Assignment map can be a dict, or a list of pairs. The latter is necessary to initialize multiple variables in the current graph from the same variable in the checkpoint. 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.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',
                     {'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, or a list of key-value pairs, where keys are names of the variables in the checkpoint and values are current variables or names of current variables (in default graph).   
 


 Raises
  ValueError   If missing variables in current graph, or if missing checkpoints or tensors in checkpoints.     

    
{'path': 'public/docs/tensorflow~2.9/compat/v1/train/init_from_checkpoint.html', 'source': 'DevDocs.IO'}