saver.py

Question

我曾尝试使用检查点恢复我的图表，但没有运气。经过多次尝试后，我决定在http://cv-tricks.com/tensorflow-tutorial/save-restore-tensorflow-models-quick-complete-tutorial/运行演示代码，这也不起作用。

我正在使用最新的tensorflow 1.1.0和GPU构建。

这是我使用过的演示代码：

saver.py

import tensorflow as tf
w1 = tf.Variable(tf.random_normal(shape=[2]), name='w1')
w2 = tf.Variable(tf.random_normal(shape=[5]), name='w2')
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver.save(sess, 'my_test_model')

restore.py

import tensorflow as tf

with tf.Session() as sess:    
    saver = tf.train.import_meta_graph('my_test_model.meta')
    saver.restore(sess,tf.train.latest_checkpoint('./'))
    print(sess.run(w1))

控制台输出

me@ubuntu:~/Projects/playground$ python3 saver.py
2017-05-11 17:12:16.833443: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-11 17:12:16.833469: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-11 17:12:16.833476: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-05-11 17:12:16.833481: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-11 17:12:16.833486: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-05-11 17:12:17.085355: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties: 
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.7335
pciBusID 0000:06:00.0
Total memory: 7.92GiB
Free memory: 7.81GiB
2017-05-11 17:12:17.085421: W tensorflow/stream_executor/cuda/cuda_driver.cc:485] creating context when one is currently active; existing: 0x2a74b80
2017-05-11 17:12:17.299874: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 1 with properties: 
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.7335
pciBusID 0000:05:00.0
Total memory: 7.92GiB
Free memory: 7.30GiB
2017-05-11 17:12:17.300703: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 1 
2017-05-11 17:12:17.300716: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0:   Y Y 
2017-05-11 17:12:17.300723: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 1:   Y Y 
2017-05-11 17:12:17.300732: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:06:00.0)
2017-05-11 17:12:17.300739: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 1080, pci bus id: 0000:05:00.0)
me@ubuntu:~/Projects/playground$ python3 restore.py
2017-05-11 17:12:26.689147: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-11 17:12:26.689174: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-11 17:12:26.689182: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-05-11 17:12:26.689188: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-11 17:12:26.689194: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-05-11 17:12:26.950831: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties: 
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.7335
pciBusID 0000:06:00.0
Total memory: 7.92GiB
Free memory: 7.81GiB
2017-05-11 17:12:26.950894: W tensorflow/stream_executor/cuda/cuda_driver.cc:485] creating context when one is currently active; existing: 0x25565b0
2017-05-11 17:12:27.159857: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 1 with properties: 
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.7335
pciBusID 0000:05:00.0
Total memory: 7.92GiB
Free memory: 7.30GiB
2017-05-11 17:12:27.160708: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 1 
2017-05-11 17:12:27.160722: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0:   Y Y 
2017-05-11 17:12:27.160729: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 1:   Y Y 
2017-05-11 17:12:27.160742: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:06:00.0)
2017-05-11 17:12:27.160750: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 1080, pci bus id: 0000:05:00.0)
Traceback (most recent call last):
  File "restore.py", line 6, in <module>
    print(sess.run(w1))
NameError: name 'w1' is not defined

我相信我所阅读的内容意味着应该在恢复后自动创建w1。对此的任何想法都会受到欢迎。谢谢！

Answer 1

错误＆＃39; w1＆＃39;未定义是因为您已将代码拆分为两个不同的文件。现在你的restore.py找不到w1变量。

要解决此问题，请按以下步骤编辑restore.py

import tensorflow as tf
from saver import w1
with tf.Session() as sess:    
    saver = tf.train.import_meta_graph('my_test_model.meta')
    saver.restore(sess,tf.train.latest_checkpoint('./'))
    print(sess.run(w1))

确保saver.py位于同一目录中。这会从saver.py文件中导入w1变量。

Answer 2

您可以看到以下示例代码：

import tensorflow as tf

tf.reset_default_graph()
w1 = tf.Variable(tf.random_normal(shape=[2]), name='w1')
w2 = tf.Variable(tf.random_normal(shape=[5]), name='w2')
saver = tf.train.Saver()
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(w1))
    print(sess.run(w2))
    saver.save(sess, './my_test_model')

tf.reset_default_graph()
# Method 1: redefine computation graph
w1 = tf.Variable(tf.random_normal(shape=[2]), name='w1')
w2 = tf.Variable(tf.random_normal(shape=[5]), name='w2')
saver = tf.train.Saver()
with tf.Session() as sess:    
    saver.restore(sess, tf.train.latest_checkpoint('./'))
    print(sess.run(w1))
    print(sess.run(w2))

tf.reset_default_graph()
# Method 2: import computation graph and get tensor
saver = tf.train.import_meta_graph('my_test_model.meta')
w1 = tf.get_default_graph().get_tensor_by_name('w1:0')
w2 = tf.get_default_graph().get_tensor_by_name('w2:0')
with tf.Session() as sess:    
    saver.restore(sess, tf.train.latest_checkpoint('./'))
    print(sess.run(w1))
    print(sess.run(w2))

tf.train.Saver（）不起作用

saver.py

restore.py

控制台输出

2 个答案: