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)

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})

在大多数情况下，使用tf.train.Saver从磁盘保存和恢复是最好的选择:

... # build your model
saver = tf.train.Saver()

with tf.Session() as sess:
    ... # train the model
    saver.save(sess, "/tmp/my_great_model")

with tf.Session() as sess:
    saver.restore(sess, "/tmp/my_great_model")
    ... # use the model

您还可以保存/恢复图结构本身(详细信息请参阅MetaGraph文档)。默认情况下，保存程序将图形结构保存到.meta文件中。您可以调用import_meta_graph()来恢复它。它恢复图形结构并返回一个你可以用来恢复模型状态的保护程序:

saver = tf.train.import_meta_graph("/tmp/my_great_model.meta")

with tf.Session() as sess:
    saver.restore(sess, "/tmp/my_great_model")
    ... # use the model

然而，在某些情况下，您需要更快的方法。例如，如果您实现了早期停止，那么您希望在训练期间每次模型改进时都保存检查点(在验证集上测量)，然后如果一段时间内没有进展，则希望回滚到最佳模型。如果每次模型改进时都将其保存到磁盘，则会极大地降低训练速度。诀窍是将变量状态保存到内存中，然后稍后恢复它们:

... # build your model

# get a handle on the graph nodes we need to save/restore the model
graph = tf.get_default_graph()
gvars = graph.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
assign_ops = [graph.get_operation_by_name(v.op.name + "/Assign") for v in gvars]
init_values = [assign_op.inputs[1] for assign_op in assign_ops]

with tf.Session() as sess:
    ... # train the model

    # when needed, save the model state to memory
    gvars_state = sess.run(gvars)

    # when needed, restore the model state
    feed_dict = {init_value: val
                 for init_value, val in zip(init_values, gvars_state)}
    sess.run(assign_ops, feed_dict=feed_dict)

A quick explanation: when you create a variable X, TensorFlow automatically creates an assignment operation X/Assign to set the variable's initial value. Instead of creating placeholders and extra assignment ops (which would just make the graph messy), we just use these existing assignment ops. The first input of each assignment op is a reference to the variable it is supposed to initialize, and the second input (assign_op.inputs[1]) is the initial value. So in order to set any value we want (instead of the initial value), we need to use a feed_dict and replace the initial value. Yes, TensorFlow lets you feed a value for any op, not just for placeholders, so this works fine.

如第6255期所述:

use '**./**model_name.ckpt'
saver.restore(sess,'./my_model_final.ckpt')

而不是

saver.restore('my_model_final.ckpt')

在TensorFlow 0.11.0RC1版本中，你可以通过调用tf.train直接保存和恢复你的模型。Export_meta_graph和tf.train。根据https://www.tensorflow.org/programmers_guide/meta_graph的Import_meta_graph。

保存模型

w1 = tf.Variable(tf.truncated_normal(shape=[10]), name='w1')
w2 = tf.Variable(tf.truncated_normal(shape=[20]), name='w2')
tf.add_to_collection('vars', w1)
tf.add_to_collection('vars', w2)
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver.save(sess, 'my-model')
# `save` method will call `export_meta_graph` implicitly.
# you will get saved graph files:my-model.meta

恢复模型

sess = tf.Session()
new_saver = tf.train.import_meta_graph('my-model.meta')
new_saver.restore(sess, tf.train.latest_checkpoint('./'))
all_vars = tf.get_collection('vars')
for v in all_vars:
    v_ = sess.run(v)
    print(v_)

你也可以在TensorFlow/skflow中查看例子，它提供了保存和恢复方法，可以帮助你轻松地管理模型。它具有一些参数，您还可以控制备份模型的频率。