[英]Tensorflow: Why must `saver = tf.train.Saver()` be declared after variables are declared?
[英]Problem with tf.train.Saver() and GPU - TensorFlow
我的代码结构如下:
with tf.device('/gpu:1'):
...
model = get_model(input_pl)
...
with tf.Session() as sess:
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
for epoch in range(num_epochs):
...
for n in range(num_batches):
...
sess.run(...)
# eval epoch
saver.save(sess, ...)
我想在训练阶段后保存模型。 当我运行它给我这个错误:
InvalidArgumentError (see above for traceback): Cannot assign a device for operation 'save/SaveV2': Could not satisfy explicit device specification '/device:GPU:1' because no supported kernel for GPU devices is available.
阅读这个问题我用这种方式改变了代码:
saver = tf.train.Saver()
with tf.device('/gpu:1'):
...
model = get_model(pointcloud_pl)
...
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(num_epochs):
...
for n in range(num_batches):
...
sess.run(...)
# eval epoch
saver.save(sess, ...)
但是现在我收到了这个错误:
ValueError: No variables to save
我也尝试过这样做:
with tf.Session() as sess:
saver = tf.train.Saver()
...
with tf.device('/gpu:1'):
sess.run(tf.global_variables_initializer())
for epoch in range(num_epochs):
...
for n in range(num_batches):
...
sess.run()
# eval epoch
saver.save(sess, ...)
我仍然得到同样的错误。 该错误始终在saver = tf.train.Saver()
行中。
我怎么解决这个问题?
解决了这个问题:
tf.Session()
saver = tf.train.Saver()
with tf.device():
这是一个示例代码
with tf.Session() as sess:
...
model = get_model(input_pl)
saver = tf.train.Saver()
...
with tf.device('/gpu:1'):
sess.run(tf.global_variables_initializer())
for epoch in range(num_epochs):
...
for n in range(num_batches):
...
sess.run()
# eval epoch
saver.save(sess, ...)
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