import tensorflow as tf

sess = tf.Session()
check_point_path = 'variables' 
saver = tf.train.import_meta_graph('variables/save_variables.ckpt.meta')

saver.restore(sess, tf.train.latest_checkpoint(check_point_path))

graph = tf.get_default_graph()

#print(graph.get_operations())

#with open('op.txt','a') as f:
#    f.write(str(graph.get_operations()))
op1 = graph.get_tensor_by_name('fully_connected/biases:0')
print(op1)
 

    使用函式graph.get_operations()獲取ckpt.meta中儲存的graph中的所有operation，而tensor_name為'op_name:0'。

然後使用graph.get_tensor_by_name('op_name:0') 獲取tensor資訊。

     程式碼從ckpt檔案中獲取儲存的variable的資料(tensor的name和value)：

import os
import tensorflow as tf
from tensorflow.python import pywrap_tensorflow
check_point_path = 'variables'
#checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')
ckpt = tf.train.get_checkpoint_state(checkpoint_dir=check_point_path)
checkpoint_path = os.path.join('.', ckpt.model_checkpoint_path)
#print(ckpt.model_checkpoint_path)
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))
 

法二：

from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file

print_tensors_in_checkpoint_file("variables/save_variables.ckpt",tensor_name='', all_tensors=False, all_tensor_names=False)

注意：tf.train.latest_checkpoint(check_point_path) 方法用來獲取最後一次ckeckpoint的路徑，等價於

ckpt = tf.train.get_checkpoint_state(check_point_path)
ckpt.model_checkpoint_path
 

參考部落格