Tensorflow FailedPreconditionError：尝试使用未初始化的值变量答案

【问题标题】：Tensorflow FailedPreconditionError: Attempting to use uninitialized value VariableTensorflow FailedPreconditionError：尝试使用未初始化的值变量
【发布时间】：2018-01-24 09:40:39
【问题描述】：

我按照官方网站上的“构建多层卷积网络”的说明进行操作。我的代码与他们在网站上提供的代码完全相同。 [https://www.tensorflow.org/get_started/mnist/pros]

我还记得调用初始化全局变量。但是，会出现错误。但是，如果我将 tf.Session() 更改为 tf.InteractiveSession()，它会起作用。

这里有什么问题？提前致谢。

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)
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')

x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])

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

W_conv2 = weight_variable([5,5,32,64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2)+b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

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

keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2)+b_fc2

cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.Session() as sess:
    init = tf.global_variables_initializer()
    sess.run(init)
    for i in range(100):
        batch = mnist.train.next_batch(50)
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={
                x:batch[0], y_:batch[1], keep_prob:1})
            print('step %d, training accuracy %g' % (i, train_accuracy))
        train_step.run(feed_dict={
            x:batch[0], y_:batch[1], keep_prob:0.5})
print('test accuracy %g' % accuracy.eval(feed_dict={
    x: mnist.test.images, y_:mnist.test.labels, keep_prob: 1.0}))

【问题讨论】：

标签： tensorflow

【解决方案1】：

with tf.Session() as sess:
    ...
    print('test accuracy %g' % accuracy.eval(feed_dict={
        x: mnist.test.images, y_:mnist.test.labels, keep_prob: 1.0}))

当使用tf.Session 时，您应该将print 方法放在with 块中，以便在运行eval 时设置sess。

对于InteractiveSession，它将设置默认会话，因此您可以使用此默认会话执行eval 和run。

【讨论】：