def get_variable_name_dict_for_attention_rnn(self):
"""Constructs a dict mapping the checkpoint variables to those in new graph.
Returns:
A dict mapping variable names in the checkpoint to variables in the graph.
"""
var_dict = dict()
if self.note_rnn_type != 'attention_rnn':
return var_dict
print("Global vars : {0}".format(tf.global_variables()))
for var in self.variables():
inner_name = rl_tuner_ops.get_inner_scope(var.name)
inner_name = rl_tuner_ops.trim_variable_postfixes(inner_name)
if '/Adam' in var.name:
# TODO(lukaszkaiser): investigate the problem here and remove this hack.
pass
elif 'fully_connected/bias' in var.name:
var_dict['fully_connected/biases'] = var
elif 'fully_connected/weights' in var.name:
var_dict['fully_connected/weights'] = var
return var_dict
def get_variable_name_dict(self):
"""Constructs a dict mapping the checkpoint variables to those in new graph.
Returns:
A dict mapping variable names in the checkpoint to variables in the graph.
"""
var_dict = dict()
for var in self.variables():
inner_name = rl_tuner_ops.get_inner_scope(var.name)
inner_name = rl_tuner_ops.trim_variable_postfixes(inner_name)
if self.note_rnn_type == 'basic_rnn':
var_dict[inner_name] = var
else:
var_dict[self.checkpoint_scope + '/' + inner_name] = var
return var_dict
def get_variable_name_dict(self):
"""Constructs a dict mapping the checkpoint variables to those in new graph.
Returns:
A dict mapping variable names in the checkpoint to variables in the graph.
"""
var_dict = dict()
for var in self.variables():
inner_name = rl_tuner_ops.get_inner_scope(var.name)
inner_name = rl_tuner_ops.trim_variable_postfixes(inner_name)
if '/Adam' in var.name:
# TODO(lukaszkaiser): investigate the problem here and remove this hack.
pass
elif self.note_rnn_type == 'basic_rnn':
var_dict[inner_name] = var
else:
var_dict[self.checkpoint_scope + '/' + inner_name] = var
return var_dict
def get_variable_name_dict(self):
"""Constructs a dict mapping the checkpoint variables to those in new graph.
Returns:
A dict mapping variable names in the checkpoint to variables in the graph.
"""
var_dict = dict()
for var in self.variables():
inner_name = rl_tuner_ops.get_inner_scope(var.name)
inner_name = rl_tuner_ops.trim_variable_postfixes(inner_name)
if '/Adam' in var.name:
# TODO(lukaszkaiser): investigate the problem here and remove this hack.
pass
elif self.note_rnn_type == 'basic_rnn':
var_dict[inner_name] = var
else:
var_dict[self.checkpoint_scope + '/' + inner_name] = var
return var_dict
def get_variable_name_dict(self):
"""Constructs a dict mapping the checkpoint variables to those in new graph.
Returns:
A dict mapping variable names in the checkpoint to variables in the graph.
"""
var_dict = dict()
for var in self.variables():
inner_name = rl_tuner_ops.get_inner_scope(var.name)
inner_name = rl_tuner_ops.trim_variable_postfixes(inner_name)
if self.note_rnn_type == 'basic_rnn':
if 'fully_connected' in inner_name and 'bias' in inner_name:
# 'fully_connected/bias' has been changed to 'fully_connected/biases'
# in newest checkpoints.
var_dict[inner_name + 'es'] = var
else:
var_dict[inner_name] = var
else:
var_dict[self.checkpoint_scope + '/' + inner_name] = var
return var_dict
