在Tensorflow中恢复保存的神经网络
在Tensorflow中恢复保存的神经网络
提问于 2019-01-02 11:52:44
在把我的问题标为重复之前，我想让大家明白，我已经经历了很多问题，但没有一个解决办法能够消除我的疑虑，解决我的问题。我有一个经过训练的神经网络，我想要保存，然后用这个模型来测试这个模型对测试数据集。
我试着保存和恢复它，但我没有得到预期的结果。恢复似乎不起作用，也许我错误地使用了它，它只是使用全局变量初始化器给出的值。
这是我用来保存模型的代码。
sess.run(tf.initializers.global_variables())
#num_epochs = 7
for epoch in range(num_epochs):
start_time = time.time()
train_accuracy = 0
train_loss = 0
val_loss = 0
val_accuracy = 0
for bid in range(int(train_data_size/batch_size)):
X_train_batch = X_train[bid*batch_size:(bid+1)*batch_size]
y_train_batch = y_train[bid*batch_size:(bid+1)*batch_size]
sess.run(optimizer, feed_dict = {x:X_train_batch, y:y_train_batch,prob:0.50})
train_accuracy = train_accuracy + sess.run(model_accuracy, feed_dict={x : X_train_batch,y:y_train_batch,prob:0.50})
train_loss = train_loss + sess.run(loss_value, feed_dict={x : X_train_batch,y:y_train_batch,prob:0.50})
for bid in range(int(val_data_size/batch_size)):
X_val_batch = X_val[bid*batch_size:(bid+1)*batch_size]
y_val_batch = y_val[bid*batch_size:(bid+1)*batch_size]
val_accuracy = val_accuracy + sess.run(model_accuracy,feed_dict = {x:X_val_batch, y:y_val_batch,prob:0.75})
val_loss = val_loss + sess.run(loss_value, feed_dict = {x:X_val_batch, y:y_val_batch,prob:0.75})
train_accuracy = train_accuracy/int(train_data_size/batch_size)
val_accuracy = val_accuracy/int(val_data_size/batch_size)
train_loss = train_loss/int(train_data_size/batch_size)
val_loss = val_loss/int(val_data_size/batch_size)
end_time = time.time()
saver.save(sess,'./blood_model_x_v2',global_step = epoch) 保存模型后，这些文件会写在我的工作目录中，如下所示。
血模型x_v2-2数据-0000-0001
blood\_model\_x\_v2-2.indexblood\_model\_x\_v2-2.meta类似地，v2-3，等到v2-6，然后是一个‘检查点’文件。然后，我尝试使用这个代码片段(初始化后)恢复它，但是得到了与预期的结果不同的结果。我做错什么了？
saver = tf.train.import_meta_graph('blood_model_x_v2-5.meta')
saver.restore(test_session,tf.train.latest_checkpoint('./'))回答 1
Stack Overflow用户
回答已采纳
发布于 2019-01-02 12:36:18
还原还原以前保存的变量。 此方法运行由构造函数添加的用于还原变量的操作。它需要一个启动图形的会话。要还原的变量不必已初始化，因为还原本身就是初始化变量的一种方法。
让我们看看一个例子：
我们保存与此类似的模型：
import tensorflow as tf
# Prepare to feed input, i.e. feed_dict and placeholders
w1 = tf.placeholder("float", name="w1")
w2 = tf.placeholder("float", name="w2")
b1 = tf.Variable(2.0, name="bias")
feed_dict = {w1: 4, w2: 8}
# Define a test operation that we will restore
w3 = tf.add(w1, w2)
w4 = tf.multiply(w3, b1, name="op_to_restore")
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Create a saver object which will save all the variables
saver = tf.train.Saver()
# Run the operation by feeding input
print (sess.run(w4, feed_dict))
# Prints 24 which is sum of (w1+w2)*b1
# Now, save the graph
saver.save(sess, './ckpnt/my_test_model', global_step=1000)然后加载经过训练的模型：
import tensorflow as tf
sess = tf.Session()
# First let's load meta graph and restore weights
saver = tf.train.import_meta_graph('./ckpnt/my_test_model-1000.meta')
saver.restore(sess, tf.train.latest_checkpoint('./ckpnt'))
# Now, let's access and create placeholders variables and
# create feed-dict to feed new data
graph = tf.get_default_graph()
w1 = graph.get_tensor_by_name("w1:0")
w2 = graph.get_tensor_by_name("w2:0")
feed_dict = {w1: 13.0, w2: 17.0}
# Now, access the op that you want to run.
op_to_restore = graph.get_tensor_by_name("op_to_restore:0")
print (sess.run(op_to_restore, feed_dict))
# This will print 60 which is calculated
# using new values of w1 and w2 and saved value of b1.如您所见，我们不会在还原部分初始化会话。使用Checkpoint保存和恢复模型有更好的方法，它允许您检查模型是否被正确还原。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/54005871
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