在tensorflow深MNIST例如使用GPU VS CPU

Question

我使用我複製粘貼從here了一些小改動的程序。這是我的代碼以試圖提高訓練速度：

from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) 

import tensorflow as tf 

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

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') 

with tf.device('/gpu:0'): 
    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(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)) as sess: 
    sess.run(tf.global_variables_initializer()) 
    for i in range(20000): 
     batch = mnist.train.next_batch(50) 
     if i % 100 == 0: 
     train_accuracy = accuracy.eval(feed_dict={ 
      x: batch[0], y_: batch[1], keep_prob: 1.0}) 
     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})) 

將會產生以下的輸出：

Extracting MNIST_data/train-images-idx3-ubyte.gz 
Extracting MNIST_data/train-labels-idx1-ubyte.gz 
Extracting MNIST_data/t10k-images-idx3-ubyte.gz 
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz 
step 0, training accuracy 0.22 
step 100, training accuracy 0.76 
step 200, training accuracy 0.88 
... 

的問題是，在本教程中所採取的原始代碼的時間（即沒有with tf.device（'/ gpu：0'）：在第26行）並且這段代碼沒有可測量的差別（每個步驟大約10秒）。我成功安裝了cuda-8.0和cuDNN（經過數小時的失敗嘗試）。 「$ NVIDIA-SMI」返回以下輸出：

Sun Jul 2 13:57:10 2017  
+-----------------------------------------------------------------------------+ 
| NVIDIA-SMI 375.26     Driver Version: 375.26     | 
|-------------------------------+----------------------+----------------------+ 
| GPU Name  Persistence-M| Bus-Id  Disp.A | Volatile Uncorr. ECC | 
| Fan Temp Perf Pwr:Usage/Cap|   Memory-Usage | GPU-Util Compute M. | 
|===============================+======================+======================| 
| 0 GeForce GT 710  Off | 0000:01:00.0  N/A |     N/A | 
| N/A 49C P0 N/A/N/A | 406MiB/2000MiB |  N/A  Default | 
+-------------------------------+----------------------+----------------------+ 


+-----------------------------------------------------------------------------+ 
| Processes:              GPU Memory | 
| GPU  PID Type Process name        Usage  | 
|=============================================================================| 
| 0     Not Supported           | 
+-----------------------------------------------------------------------------+ 

所以，問題是：

1）是工作過小，產生在選擇CPU或GPU沒有區別？ 2）或者在我的實現中有一些愚蠢的錯誤？

感謝您閱讀整個問題。

Answer 1

沒有任何錯誤提示，TensorFlow絕對可以用GPU上運行，你可以運行此代碼的事實。這裏的問題是，當你按原樣運行TensorFlow時，默認情況下它會嘗試在GPU上運行。有幾種方法可以強制它在CPU上運行。

1. 以此方式運行：CUDA_VISIBLE_DEVICES= python code.py。請注意，當你這樣做，仍然有with tf.device('/gpu:0')，它會中斷，所以刪除它。
2. 變化評論

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

編輯從問題的更多信息，什麼allow_soft_placement和log_device_placement意味着ConfigProto見here。