Mixing
Modifying variables names after restoring from checkpoint in TensorFlow
let's say I have two models with different configurations. I train and checkpoint them.
def train_and_save_model_using_config1():
...
def train_and_save_model_using_config2():
...
I'd like to load these models in the same session so I can use them at the same time. The variable names are mostly the same for the above two models, so to avoid name mangling, I add a name scope for each model.
with tf.variable_scope("config1"):
m1 = load_model_from_ckpt_with_config1()
with tf.variable_scope("config2"):
m2 = load_model_from_ckpt_with_config2()
To restore from the checkpoint for config1, I collect the variables and variable names but want to rename with the proper scope.
path = get_path_of_config1()
var_names = tf.contrib.framework.list_variables(path)
vars = {}
for name, shape in var_names:
var = tf.contrib.framework.load_variable(path, name)
vars["config1/" + name] = var
saver = tf.train.Saver(var_list=vars)
saver.restore(sess, tf.train.latest_checkpoint(path))
But I get the following error:
TypeError: names_to_saveables must be a dict mapping string names to Tensors/Variables. Not a variable: Tensor("Const:0", shape=(128, 14987), dtype=float32)
python tensorflow neural-network deep-learning
asked Jan 31 '17 at 20:49
tokestermw
13219
add a comment |
let's say I have two models with different configurations. I train and checkpoint them.
def train_and_save_model_using_config1():
...
def train_and_save_model_using_config2():
...
I'd like to load these models in the same session so I can use them at the same time. The variable names are mostly the same for the above two models, so to avoid name mangling, I add a name scope for each model.
with tf.variable_scope("config1"):
m1 = load_model_from_ckpt_with_config1()
with tf.variable_scope("config2"):
m2 = load_model_from_ckpt_with_config2()
To restore from the checkpoint for config1, I collect the variables and variable names but want to rename with the proper scope.
path = get_path_of_config1()
var_names = tf.contrib.framework.list_variables(path)
vars = {}
for name, shape in var_names:
var = tf.contrib.framework.load_variable(path, name)
vars["config1/" + name] = var
saver = tf.train.Saver(var_list=vars)
saver.restore(sess, tf.train.latest_checkpoint(path))
But I get the following error:
TypeError: names_to_saveables must be a dict mapping string names to Tensors/Variables. Not a variable: Tensor("Const:0", shape=(128, 14987), dtype=float32)
python tensorflow neural-network deep-learning
asked Jan 31 '17 at 20:49
tokestermw
13219
Same problem. Any solution?
– Pablo Gonzalez
Jul 24 '17 at 5:34
add a comment |
let's say I have two models with different configurations. I train and checkpoint them.
def train_and_save_model_using_config1():
...
def train_and_save_model_using_config2():
...
I'd like to load these models in the same session so I can use them at the same time. The variable names are mostly the same for the above two models, so to avoid name mangling, I add a name scope for each model.
with tf.variable_scope("config1"):
m1 = load_model_from_ckpt_with_config1()
with tf.variable_scope("config2"):
m2 = load_model_from_ckpt_with_config2()
To restore from the checkpoint for config1, I collect the variables and variable names but want to rename with the proper scope.
path = get_path_of_config1()
var_names = tf.contrib.framework.list_variables(path)
vars = {}
for name, shape in var_names:
var = tf.contrib.framework.load_variable(path, name)
vars["config1/" + name] = var
saver = tf.train.Saver(var_list=vars)
saver.restore(sess, tf.train.latest_checkpoint(path))
But I get the following error:
TypeError: names_to_saveables must be a dict mapping string names to Tensors/Variables. Not a variable: Tensor("Const:0", shape=(128, 14987), dtype=float32)
python tensorflow neural-network deep-learning
asked Jan 31 '17 at 20:49
tokestermw
13219
let's say I have two models with different configurations. I train and checkpoint them.
def train_and_save_model_using_config1():
...
def train_and_save_model_using_config2():
...
I'd like to load these models in the same session so I can use them at the same time. The variable names are mostly the same for the above two models, so to avoid name mangling, I add a name scope for each model.
with tf.variable_scope("config1"):
m1 = load_model_from_ckpt_with_config1()
with tf.variable_scope("config2"):
m2 = load_model_from_ckpt_with_config2()
To restore from the checkpoint for config1, I collect the variables and variable names but want to rename with the proper scope.
path = get_path_of_config1()
var_names = tf.contrib.framework.list_variables(path)
vars = {}
for name, shape in var_names:
var = tf.contrib.framework.load_variable(path, name)
vars["config1/" + name] = var
saver = tf.train.Saver(var_list=vars)
saver.restore(sess, tf.train.latest_checkpoint(path))
But I get the following error:
TypeError: names_to_saveables must be a dict mapping string names to Tensors/Variables. Not a variable: Tensor("Const:0", shape=(128, 14987), dtype=float32)
python tensorflow neural-network deep-learning
python tensorflow neural-network deep-learning
asked Jan 31 '17 at 20:49
tokestermw
13219
asked Jan 31 '17 at 20:49
tokestermw
13219
asked Jan 31 '17 at 20:49
tokestermw
13219
asked Jan 31 '17 at 20:49
tokestermw
13219
asked Jan 31 '17 at 20:49
tokestermw
13219
13219
Same problem. Any solution?
– Pablo Gonzalez
Jul 24 '17 at 5:34
add a comment |
Same problem. Any solution?
– Pablo Gonzalez
Jul 24 '17 at 5:34
Same problem. Any solution?
– Pablo Gonzalez
Jul 24 '17 at 5:34
Same problem. Any solution?
– Pablo Gonzalez
Jul 24 '17 at 5:34
add a comment |
1 Answer
1
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oldest
votes
The vars dict in saver = tf.train.Saver(var_list=vars) must be a dict whose value is the reference to the tf.Variable of current graph in current session.
But in your case var = tf.contrib.framework.load_variable(path, name), the problem is that var is numpy.ndarray
answered Nov 19 '18 at 12:27
maruchen
12
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
The vars dict in saver = tf.train.Saver(var_list=vars) must be a dict whose value is the reference to the tf.Variable of current graph in current session.
But in your case var = tf.contrib.framework.load_variable(path, name), the problem is that var is numpy.ndarray
answered Nov 19 '18 at 12:27
maruchen
12
add a comment |
The vars dict in saver = tf.train.Saver(var_list=vars) must be a dict whose value is the reference to the tf.Variable of current graph in current session.
But in your case var = tf.contrib.framework.load_variable(path, name), the problem is that var is numpy.ndarray
answered Nov 19 '18 at 12:27
maruchen
12
add a comment |
The vars dict in saver = tf.train.Saver(var_list=vars) must be a dict whose value is the reference to the tf.Variable of current graph in current session.
But in your case var = tf.contrib.framework.load_variable(path, name), the problem is that var is numpy.ndarray
answered Nov 19 '18 at 12:27
maruchen
12
The vars dict in saver = tf.train.Saver(var_list=vars) must be a dict whose value is the reference to the tf.Variable of current graph in current session.
But in your case var = tf.contrib.framework.load_variable(path, name), the problem is that var is numpy.ndarray
answered Nov 19 '18 at 12:27
maruchen
12
answered Nov 19 '18 at 12:27
maruchen
12
answered Nov 19 '18 at 12:27
maruchen
12
answered Nov 19 '18 at 12:27
maruchen
12
12
add a comment |
add a comment |
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Same problem. Any solution?
– Pablo Gonzalez
Jul 24 '17 at 5:34