Wie finden Sie die Variablennamen, die in einem Tensorflow-Checkpoint gespeichert sind?

Question

Ich möchte die Variablen sehen, die in einem Tensorflow-Checkpoint zusammen mit ihren Werten gespeichert sind. Wie kann ich die Variablennamen finden, die in einem Tensorflow-Checkpoint gespeichert sind?

EDIT:

I verwendet tf.train.NewCheckpointReader die here erklärt. Aber es ist nicht in der Dokumentation von Tensorflow angegeben. Gibt es einen anderen Weg?

`

import tensorflow as tf 
    v0 = tf.Variable([[1, 2, 3], [4, 5, 6]], dtype=tf.float32, name="v0") 
    v1 = tf.Variable([[[1], [2]], [[3], [4]], [[5], [6]]], dtype=tf.float32, 
        name="v1") 
    init_all_op = tf.initialize_all_variables() 
    save = tf.train.Saver({"v0": v0, "v1": v1}) 
    checkpoint_path = os.path.join(model_dir, "model.ckpt")  

    with tf.Session() as sess: 
     sess.run(init_all_op) 
     # Saves a checkpoint.  
     save.save(sess, checkpoint_path) 

     # Creates a reader. 
     reader = tf.train.NewCheckpointReader(checkpoint_path) 
     print('reder:\n', reader) 

     # Verifies that the tensors exist. 
     print('is exist v0?', reader.has_tensor("v0")) 
     print('is exist v1?', reader.has_tensor("v1")) 

     # Verifies that debug string contains the right strings. 
     debug_string = reader.debug_string() 
     print('\n All Variables: \n', debug_string) 

     # Verifies get_variable_to_shape_map() returns the correct information. 
     var_map = reader.get_variable_to_shape_map() 
     print('\n All Variables information :\n', var_map) 

     # Verifies get_tensor() returns the tensor value. 
     v0_tensor = reader.get_tensor("v0") 
     v1_tensor = reader.get_tensor("v1") 
     print('\n returns the v0 tensor value:\n', v0_tensor) 
     print('\n returns the v1 tensor value:\n', v1_tensor) 

`

Answer 1

Sie das inspect_checkpoint.py Tool verwenden können.

Answer 2

Beispiel Nutzung:

from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file 
checkpoint_path = os.path.join(model_dir, "model.ckpt") 

# List ALL tensors example output: v0/Adam (DT_FLOAT) [3,3,1,80] 
print_tensors_in_checkpoint_file(file_name=checkpoint_path, tensor_name='') 

# List contents of v0 tensor. 
# Example output: tensor_name: v0 [[[[ 9.27958265e-02 7.40226209e-02 4.52989563e-02 3.15700471e-02 
print_tensors_in_checkpoint_file(file_name=checkpoint_path, tensor_name='v0') 

# List contents of v1 tensor. 
print_tensors_in_checkpoint_file(file_name=checkpoint_path, tensor_name='v1') 

Update:all_tensors Argument wurde print_tensors_in_checkpoint_file hinzugefügt, da Tensorflow 0.12.0-rc0 so müssen Sie all_tensors=False oder all_tensors=True hinzuzufügen, falls erforderlich.

Alternative Methode:

from tensorflow.python import pywrap_tensorflow 
checkpoint_path = os.path.join(model_dir, "model.ckpt") 
reader = pywrap_tensorflow.NewCheckpointReader(checkpoint_path) 
var_to_shape_map = reader.get_variable_to_shape_map() 
for key in var_to_shape_map: 
    print("tensor_name: ", key) 
    print(reader.get_tensor(key)) # Remove this is you want to print only variable names 

Hoffe, es hilft.

Answer 3

oben Antwort hinzu:

Wenn Modell gespeichert wird mit V2-Format

model-10000.data-00000-of-00001 
model-10000.index 
model-10000.meta 

Ihr Kontrollpunkt Eingabe Name sollte nur das Präfix

print_tensors_in_checkpoint_file(file_name='/home/RNN/models/model_10000', tensor_name='',all_tensors=True) 

Quelle sein: von @LingjiaDeng bei https://github.com/tensorflow/tensorflow/issues/7696