解决TensorFlow测试模型时NotFoundError错误的问题
在TensorFlow中,当我们测试模型时,有时会遇到NotFoundError错误。这个错误通常是由于模型文件路径不正确或者模型文件不存在导致的。本攻略将介绍如何解决这个问题,并提供两个示例。
示例1:使用绝对路径
以下是示例步骤:
- 导入必要的库。
python
import tensorflow as tf
- 定义模型。
python
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.global_variables_initializer()
saver = tf.train.Saver()
- 训练模型。
python
with tf.Session() as sess:
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
if i % 100 == 0:
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
saver.save(sess, "/absolute/path/to/model.ckpt")
- 加载模型并测试。
python
with tf.Session() as sess:
saver.restore(sess, "/absolute/path/to/model.ckpt")
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
在这个示例中,我们演示了如何使用绝对路径来解决NotFoundError错误。
示例2:使用相对路径
以下是示例步骤:
- 导入必要的库。
python
import tensorflow as tf
- 定义模型。
python
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.global_variables_initializer()
saver = tf.train.Saver()
- 训练模型。
python
with tf.Session() as sess:
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
if i % 100 == 0:
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
saver.save(sess, "./model.ckpt")
- 加载模型并测试。
python
with tf.Session() as sess:
saver.restore(sess, "./model.ckpt")
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
在这个示例中,我们演示了如何使用相对路径来解决NotFoundError错误。
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