GPU上的tensorflow不起作用 [英] tensorflow on GPU doesn't work
问题描述
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'
当我选择
with tf.device('/cpu:0'):
它运行.但是当我将其更改为
It runs. but when I change it to
with tf.device('/gpu:0'):
这给我一个错误.
我需要安装一些东西吗?我使用Ubuntu 16.04，并且在运行时
Do I need to install something? I use Ubuntu 16.04 and when I run
sudo lshw -C display
我明白了
description: VGA compatible controller
product: Cedar [Radeon HD 5000/6000/7350/8350 Series]
vendor: Advanced Micro Devices, Inc. [AMD/ATI]
physical id: 0
bus info: pci@0000:01:00.0
version: 00
width: 64 bits
clock: 33MHz
capabilities: pm pciexpress msi vga_controller bus_master cap_list rom
configuration: driver=radeon latency=0
我已经安装了tensorflow_gpu.这就是我所遵循的. https://www.tensorflow.org/install/install_linux
I already installed tensorflow_gpu. This is what I followed. https://www.tensorflow.org/install/install_linux
我的GPU不兼容吗?
推荐答案
GPU支持需要使用NVIDIA硬件和软件，如以下所述. NVIDIA要求运行具有GPU支持的TensorFlow.
GPU support requires the NVIDIA hardware and software described in NVIDIA requirements to run TensorFlow with GPU support.
因此，很遗憾，由于您具有AMD/ATI设备，因此无法使用该GPU运行Tensorflow.有关支持哪种硬件的信息，请参阅 NVIDIA GPU要求.特别是，您将需要一个
So, unfortunately since you have a AMD/ATI device, you cannot use that GPU to run Tensorflow. For information on which hardware is supported, refer to this information on NVIDIA GPU requirements. In particular, you will need a
具有CUDA Compute Capability 3.0或更高版本的GPU卡.
GPU card with CUDA Compute Capability 3.0 or higher.
NVIDIA网站列出了哪些设备具有所需的CUDA支持.
The NVIDIA website lists which devices have the required CUDA support.
这篇关于GPU上的tensorflow不起作用的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
