TensorFlow, 모델을 저장 한 후 3 개의 파일이있는 이유는 무엇입니까?

TensorFlow, 모델을 저장 한 후 3 개의 파일이있는 이유는 무엇입니까?

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문서 를 읽은 후에 모델을 저장했습니다 TensorFlow. 여기에 데모 코드가 있습니다.

# Create some variables.
v1 = tf.Variable(..., name="v1")
v2 = tf.Variable(..., name="v2")
...
# Add an op to initialize the variables.
init_op = tf.global_variables_initializer()

# Add ops to save and restore all the variables.
saver = tf.train.Saver()

# Later, launch the model, initialize the variables, do some work, save the
# variables to disk.
with tf.Session() as sess:
  sess.run(init_op)
  # Do some work with the model.
  ..
  # Save the variables to disk.
  save_path = saver.save(sess, "/tmp/model.ckpt")
  print("Model saved in file: %s" % save_path)

하지만 그 후 3 개의 파일이 있다는 것을 알게되었습니다.

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

그리고 model.ckpt그런 파일이 없기 때문에 파일을 복원하여 모델을 복원 할 수 없습니다 . 내 코드는 다음과 같습니다.

with tf.Session() as sess:
  # Restore variables from disk.
  saver.restore(sess, "/tmp/model.ckpt")

그렇다면 왜 3 개의 파일이 있습니까?

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이 시도:

with tf.Session() as sess:
    saver = tf.train.import_meta_graph('/tmp/model.ckpt.meta')
    saver.restore(sess, "/tmp/model.ckpt")

TensorFlow 저장 방법은 그래프 구조를 변수 값 과 별도로 저장하므로 세 종류의 파일을 저장 합니다 . 이 .meta파일은 저장된 그래프 구조를 설명하므로 체크 포인트를 복원하기 전에 가져와야합니다 (그렇지 않으면 저장된 체크 포인트 값이 어떤 변수에 해당하는지 알 수 없음).

또는 다음을 수행 할 수 있습니다.

# Recreate the EXACT SAME variables
v1 = tf.Variable(..., name="v1")
v2 = tf.Variable(..., name="v2")

...

# Now load the checkpoint variable values
with tf.Session() as sess:
    saver = tf.train.Saver()
    saver.restore(sess, "/tmp/model.ckpt")

Even though there is no file named model.ckpt, you still refer to the saved checkpoint by that name when restoring it. From the saver.py source code:

Users only need to interact with the user-specified prefix... instead of any physical pathname.

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• meta file: describes the saved graph structure, includes GraphDef, SaverDef, and so on; then apply tf.train.import_meta_graph('/tmp/model.ckpt.meta'), will restore Saver and Graph.

• index file: it is a string-string immutable table(tensorflow::table::Table). Each key is a name of a tensor and its value is a serialized BundleEntryProto. Each BundleEntryProto describes the metadata of a tensor: which of the "data" files contains the content of a tensor, the offset into that file, checksum, some auxiliary data, etc.

• data file: it is TensorBundle collection, save the values of all variables.

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I am restoring trained word embeddings from Word2Vec tensorflow tutorial.

In case you have created multiple checkpoints:

e.g. files created look like this

model.ckpt-55695.data-00000-of-00001

model.ckpt-55695.index

model.ckpt-55695.meta

try this

def restore_session(self, session):
   saver = tf.train.import_meta_graph('./tmp/model.ckpt-55695.meta')
   saver.restore(session, './tmp/model.ckpt-55695')

when calling restore_session():

def test_word2vec():
   opts = Options()    
   with tf.Graph().as_default(), tf.Session() as session:
       with tf.device("/cpu:0"):            
           model = Word2Vec(opts, session)
           model.restore_session(session)
           model.get_embedding("assistance")

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If you trained a CNN with dropout, for example, you could do this:

def predict(image, model_name):
    """
    image -> single image, (width, height, channels)
    model_name -> model file that was saved without any extensions
    """
    with tf.Session() as sess:
        saver = tf.train.import_meta_graph('./' + model_name + '.meta')
        saver.restore(sess, './' + model_name)
        # Substitute 'logits' with your model
        prediction = tf.argmax(logits, 1)
        # 'x' is what you defined it to be. In my case it is a batch of RGB images, that's why I add the extra dimension
        return prediction.eval(feed_dict={x: image[np.newaxis,:,:,:], keep_prob_dnn: 1.0})

참고URL : https://stackoverflow.com/questions/41265035/tensorflow-why-there-are-3-files-after-saving-the-model