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CNN的Tensor流中占位符丢失错误

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我正在使用张量流在MNIST数据库上运行卷积神经网络 . 但是我收到以下错误 .

tensorflow.python.framework.errors.InvalidArgumentError: You must feed a value for placeholder tensor 'x' with dtype float [[Node: x = Placeholderdtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]]

x = tf.placeholder(tf.float32, [None, 784], name='x') # mnist data image of shape 28*28=784

我以为我正确使用feed_dict更新x的值，但它说我没有更新占位符x的值 .

另外，我的代码中还有其他逻辑缺陷吗？

任何帮助将不胜感激 . 谢谢 .

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 = 10
batch_size = 100
display_step = 1

# tf Graph Input
#x = tf.placeholder(tf.float32, [50, 784], name='x') # mnist data image of shape 28*28=784
#y = tf.placeholder(tf.float32, [50, 10], name='y') # 0-9 digits recognition => 10 classes

# 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)


    # Training cycle
    for i in range(1000):
        print i
        batch_xs, batch_ys = mnist.train.next_batch(50)

        x_image = tf.reshape(x, [-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(y * tf.log(y_conv), reduction_indices=[1]))
        sess.run(
          [cross_entropy, y_conv],
          feed_dict={x: batch_xs, y: batch_ys})

        correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y,1))
        print correct_prediction.eval()
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
python tensorflow deep-learning torch mnist

3 回答

  • 0

    你为什么要创建占位符变量？您应该能够直接使用 mnist.train.next_batch(50) 生成的输出，前提是您在模型内部移动 correct_prediction 的计算和精度 .

    batch_xs, batch_ys = mnist.train.next_batch(50)
x_image = tf.reshape(batch_xs, [-1,28,28,1])
...
cross_entropy = tf.reduce_mean(-tf.reduce_sum(batch_ys * tf.log(y_conv), reduction_indices=[1]))
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(batch_ys,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
predictions_correct, acc = sess.run([cross_entropy, y_conv, correct_prediction, accuracy])
print predictions_correct, acc
    回复于
  • 1

    你're receiving that error because you'重新尝试在 correct_prediction 上运行 eval() . 该张量需要批量输入（x和y）才能进行评估 . 您可以通过将其更改为：更正错误：

    print correct_prediction.eval(feed_dict={x: batch_xs, y: batch_ys})

    但正如Benoit Steiner所提到的，你可以轻松地将其拉入模型中 .

    更一般地说，你在这里没有进行任何优化，但也许你还没有完成任何优化 . 就目前而言，它只会打印出糟糕的预测一段时间 . :)

    回复于
  • 2

    首先，你的x和y被注释掉了，如果你的实际代码中存在这个问题很可能就是问题 .

    correct_prediction.eval() 等同于 tf.session.run(correct_prediction) （或者在您的情况下为 sess.run() ），因此需要相同的语法* . 所以它需要 correct_prediction.eval(feed_dict={x: batch_xs, y: batch_ys}) 才能运行，但是请注意，这通常是RAM密集型的，并且可能导致系统挂起 . 由于使用ram，将精度函数拉入模型可能是一个好主意 .

    我没有看到使用你的交叉熵的优化函数，但是我从来没有尝试过不使用它，所以如果有效则不要修复它 . 但如果它最终导致错误你可能想尝试：

    optimizer = optimizer = tf.train.AdamOptimizer().minimize(cross_entropy)

    并替换' cross_entropy '

    sess.run([cross_entropy, y_conv],feed_dict={x: batch_xs, y: batch_ys})

    与' optimizer '

    https://pythonprogramming.net/tensorflow-neural-network-session-machine-learning-tutorial/

    检查脚本的准确性评估部分 .

    回复于

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