下面是我对这两种基本情况的简单解决方案，这两种情况的不同之处在于您是想从文件加载图形还是在运行时构建它。

这个答案适用于Tensorflow 0.12+(包括1.0)。

在代码中重建图形

储蓄

graph = ... # build the graph
saver = tf.train.Saver()  # create the saver after the graph
with ... as sess:  # your session object
    saver.save(sess, 'my-model')

加载

graph = ... # build the graph
saver = tf.train.Saver()  # create the saver after the graph
with ... as sess:  # your session object
    saver.restore(sess, tf.train.latest_checkpoint('./'))
    # now you can use the graph, continue training or whatever

还从文件中加载图形

当使用这种技术时，确保所有的层/变量都显式地设置了唯一的名称。否则Tensorflow将使名称本身是唯一的，因此它们将不同于存储在文件中的名称。在前一种技术中，这不是问题，因为名称在加载和保存时都以相同的方式“损坏”。

储蓄

graph = ... # build the graph

for op in [ ... ]:  # operators you want to use after restoring the model
    tf.add_to_collection('ops_to_restore', op)

saver = tf.train.Saver()  # create the saver after the graph
with ... as sess:  # your session object
    saver.save(sess, 'my-model')

加载

with ... as sess:  # your session object
    saver = tf.train.import_meta_graph('my-model.meta')
    saver.restore(sess, tf.train.latest_checkpoint('./'))
    ops = tf.get_collection('ops_to_restore')  # here are your operators in the same order in which you saved them to the collection

在新版本的tensorflow 2.0中，保存/加载模型的过程要容易得多。因为Keras API的实现，一个TensorFlow的高级API。

保存一个模型: 请查阅相关文档以作参考: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/models/save_model

tf.keras.models.save_model(model_name, filepath, save_format)

加载一个模型:

https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/models/load_model

model = tf.keras.models.load_model(filepath)

我的环境:Python 3.6, Tensorflow 1.3.0

虽然有很多解决方案，但大多数都是基于tf.train.Saver。当我们加载由Saver保存的.ckpt文件时，我们必须要么重新定义张量流网络，要么使用一些奇怪且难以记住的名称，例如:“placehold_0:0”,“密集/亚当/重量:0”。这里我推荐使用tf。saved_model，下面给出的一个最简单的例子，你可以从为TensorFlow模型服务中学到更多:

保存模型:

import tensorflow as tf

# define the tensorflow network and do some trains
x = tf.placeholder("float", name="x")
w = tf.Variable(2.0, name="w")
b = tf.Variable(0.0, name="bias")

h = tf.multiply(x, w)
y = tf.add(h, b, name="y")
sess = tf.Session()
sess.run(tf.global_variables_initializer())

# save the model
export_path =  './savedmodel'
builder = tf.saved_model.builder.SavedModelBuilder(export_path)

tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)

prediction_signature = (
  tf.saved_model.signature_def_utils.build_signature_def(
      inputs={'x_input': tensor_info_x},
      outputs={'y_output': tensor_info_y},
      method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))

builder.add_meta_graph_and_variables(
  sess, [tf.saved_model.tag_constants.SERVING],
  signature_def_map={
      tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
          prediction_signature 
  },
  )
builder.save()

加载模型:

import tensorflow as tf
sess=tf.Session() 
signature_key = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
input_key = 'x_input'
output_key = 'y_output'

export_path =  './savedmodel'
meta_graph_def = tf.saved_model.loader.load(
           sess,
          [tf.saved_model.tag_constants.SERVING],
          export_path)
signature = meta_graph_def.signature_def

x_tensor_name = signature[signature_key].inputs[input_key].name
y_tensor_name = signature[signature_key].outputs[output_key].name

x = sess.graph.get_tensor_by_name(x_tensor_name)
y = sess.graph.get_tensor_by_name(y_tensor_name)

y_out = sess.run(y, {x: 3.0})

你也可以用更简单的方法。

步骤1:初始化所有变量

W1 = tf.Variable(tf.truncated_normal([6, 6, 1, K], stddev=0.1), name="W1")
B1 = tf.Variable(tf.constant(0.1, tf.float32, [K]), name="B1")

Similarly, W2, B2, W3, .....

步骤2:在模型Saver中保存会话并保存它

model_saver = tf.train.Saver()

# Train the model and save it in the end
model_saver.save(session, "saved_models/CNN_New.ckpt")

步骤3:恢复模型

with tf.Session(graph=graph_cnn) as session:
    model_saver.restore(session, "saved_models/CNN_New.ckpt")
    print("Model restored.") 
    print('Initialized')

步骤4:检查变量

W1 = session.run(W1)
print(W1)

在不同的python实例中运行时，使用

with tf.Session() as sess:
    # Restore latest checkpoint
    saver.restore(sess, tf.train.latest_checkpoint('saved_model/.'))

    # Initalize the variables
    sess.run(tf.global_variables_initializer())

    # Get default graph (supply your custom graph if you have one)
    graph = tf.get_default_graph()

    # It will give tensor object
    W1 = graph.get_tensor_by_name('W1:0')

    # To get the value (numpy array)
    W1_value = session.run(W1)

下面是我对这两种基本情况的简单解决方案，这两种情况的不同之处在于您是想从文件加载图形还是在运行时构建它。

这个答案适用于Tensorflow 0.12+(包括1.0)。

在代码中重建图形

储蓄

graph = ... # build the graph
saver = tf.train.Saver()  # create the saver after the graph
with ... as sess:  # your session object
    saver.save(sess, 'my-model')

加载

graph = ... # build the graph
saver = tf.train.Saver()  # create the saver after the graph
with ... as sess:  # your session object
    saver.restore(sess, tf.train.latest_checkpoint('./'))
    # now you can use the graph, continue training or whatever

还从文件中加载图形

当使用这种技术时，确保所有的层/变量都显式地设置了唯一的名称。否则Tensorflow将使名称本身是唯一的，因此它们将不同于存储在文件中的名称。在前一种技术中，这不是问题，因为名称在加载和保存时都以相同的方式“损坏”。

储蓄

graph = ... # build the graph

for op in [ ... ]:  # operators you want to use after restoring the model
    tf.add_to_collection('ops_to_restore', op)

saver = tf.train.Saver()  # create the saver after the graph
with ... as sess:  # your session object
    saver.save(sess, 'my-model')

加载

with ... as sess:  # your session object
    saver = tf.train.import_meta_graph('my-model.meta')
    saver.restore(sess, tf.train.latest_checkpoint('./'))
    ops = tf.get_collection('ops_to_restore')  # here are your operators in the same order in which you saved them to the collection

无论你想把模型保存在哪里，

self.saver = tf.train.Saver()
with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            ...
            self.saver.save(sess, filename)

确保你所有的任务。变量有名称，因为您可能希望稍后使用它们的名称来恢复它们。 在你想预测的地方，

saver = tf.train.import_meta_graph(filename)
name = 'name given when you saved the file' 
with tf.Session() as sess:
      saver.restore(sess, name)
      print(sess.run('W1:0')) #example to retrieve by variable name

确保该保护程序在相应的会话中运行。 请记住，如果使用tf.train.latest_checkpoint('./')，那么将只使用最新的检查点。