Question

我正在尝试使用Adagrad优化器构建CNN，但收到以下错误。

tensorflow.python.framework.errors.FailedPreconditionError：尝试使用未初始化的值Variable_7 / Adadelta

[[Node：Adadelta / update_Variable_7 / ApplyAdadelta = ApplyAdadelta [T = DT_FLOAT，_class = [“loc：@ Variable_7”]，use_locking = false，_device =“/ job：localhost / replica：0 / task ：0 / cpu：0“]（Variable_7，Variable_7 / Adadelta，Variable_7 / Adadelta_1，Adadelta / lr，Adadelta / rho，Adadelta / epsilon，gradients / add_3_grad / tuple / control_dependency_1）]] 引起op u'Adadelta / update_Variable_7 / ApplyAdadelta'，

optimizer = tf.train.AdadeltaOptimizer（learning_rate）.minimize（cross_entropy）

我尝试在adgrad语句之后重新初始化会话变量，如本文所述，但这也无济于事。

如何避免此错误？感谢。

Tensorflow: Using Adam optimizer

import tensorflow as tf
import numpy
from tensorflow.examples.tutorials.mnist import input_data

def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')

def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)


mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

# Parameters
learning_rate = 0.01
training_epochs = 100
batch_size = 1000
display_step = 1


# Set model weights
W = tf.Variable(tf.zeros([784, 10]), name="weights")
b = tf.Variable(tf.zeros([10]), name="bias")

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])


W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])


W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

# Initializing the variables
init = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(training_epochs):
        total_batch = int(mnist.train.num_examples/batch_size)
        for i in range(total_batch):

            batch_xs, batch_ys = mnist.train.next_batch(batch_size)

            x_image = tf.reshape(batch_xs, [-1,28,28,1])

            h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
            h_pool1 = max_pool_2x2(h_conv1)

            h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
            h_pool2 = max_pool_2x2(h_conv2)

            h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
            h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)


            y_conv=tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)

            cross_entropy = tf.reduce_mean(-tf.reduce_sum(batch_ys * tf.log(y_conv), reduction_indices=[1]))
            #optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)

            optimizer = tf.train.AdadeltaOptimizer(learning_rate).minimize(cross_entropy)
            sess.run(init)

            correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(batch_ys,1))
            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
            sess.run([cross_entropy, y_conv,optimizer])
            print cross_entropy.eval()

Answer 1

这里的问题是tf.initialize_all_variables()是一个误导性的名称。它实际上意味着“返回一个操作，初始化已经创建的所有变量（在默认图中）”。当您致电tf.train.AdadeltaOptimizer(...).minimize()时，TensorFlow会创建其他变量，这些变量不在您之前创建的init操作中。

移动线：

init = tf.initialize_all_variables()

... 之后<{1}}的构建应该会使您的计划有效。

N.B。除了变量之外，您的程序还会在每个培训步骤中重建整个网络。这可能是非常低效的，并且Adadelta算法将无法按预期进行调整，因为其状态在每个步骤上重新创建。我强烈建议将代码从tf.train.AdadeltaOptimizer的定义移动到两个嵌套batch_xs循环之外的optimizer的创建。您应为for和tf.placeholder()输入定义batch_xs操作，并使用batch_ys参数feed_dict传递sess.run()返回的值}。

在Tensorflow中使用Adadelta优化器时未初始化的值错误

1 个答案: