GPU上的tensorflow不起作用。

I have the following code

我有以下代码

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data/', one_hot = True)


def init_weights(shape):
    init_random_dist = tf.truncated_normal(shape, stddev = 0.1)
    return tf.Variable(init_random_dist)

def init_bias(shape):
    init_bias_vals = tf.constant(0.1, shape = shape)
    return tf.Variable(init_bias_vals)

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

def max_pool_2by2(x):
    return tf.nn.max_pool(x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')

def convolutional_layer(input_x, shape):
    W = init_weights(shape)
    b = init_bias([shape[3]])
    return tf.nn.relu(conv2d(input_x, W) + b)

def normal_full_layer(input_layer, size):
    input_size = int(input_layer.get_shape()[1])
    W = init_weights([input_size, size])
    b = init_bias([size])
    return tf.matmul(input_layer, W) + b


with tf.device('/gpu:0'):

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

    y_true = tf.placeholder(tf.float32, shape = [None, 10])

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

    convo_1 = convolutional_layer(x_image, shape = [6,6,1,32])
    convo_1_pooling = max_pool_2by2(convo_1)

    convo_2 = convolutional_layer(convo_1_pooling, shape = [6, 6, 32, 64])
    convo_2_pooling = max_pool_2by2(convo_2)

    convo_2_flat = tf.reshape(convo_2_pooling, [-1, 7*7*64])
    full_layer_one = tf.nn.relu(normal_full_layer(convo_2_flat, 1024))

    hold_prob = tf.placeholder(tf.float32)
    full_one_dropout = tf.nn.dropout(full_layer_one, keep_prob = hold_prob)

    y_pred = normal_full_layer(full_one_dropout, 10)

    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_true, logits = y_pred))

    optimizer = tf.train.AdamOptimizer(learning_rate = 0.001)
    train = optimizer.minimize(cross_entropy)

    init = tf.global_variables_initializer()

    steps = 5000

    with tf.Session() as sess:
        sess.run(init)
        for j in range(steps):

            batch_x, batch_y = mnist.train.next_batch(50)

            sess.run(train, feed_dict = {x:batch_x, y_true:batch_y, hold_prob:0.5})

            if j%100 == 0:
                print('Currently on step %s' % j)
                print ('Accuracy is: ')

                matches = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y_true, 1))

                acc = tf.reduce_mean(tf.cast(matches, tf.float32))

                print(sess.run(acc, feed_dict = {x:mnist.test.images, y_true:mnist.test.labels, hold_prob:1.0}))
                print '\n'
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