<p dir="auto">With the tensorflow/model/image/cifar10 example. While I run cifar10_train.py,<br>
It used to be able to achieve around 400+ examples/sec with my single K40 GPU. Today I pulled the code and installed the latest version (0.6.0x), but it can only achieve 300 examples/sec or less sometimes.</p>
<p dir="auto">I see Alexnet benchmark performance gets improved, but this actually use case gets worse performance.</p>
<p dir="auto">hi everyone:<br>
I tried to run an examples of MNIST with cnn and when i only use cpu the code can work well  <strong>but when i use gpu it is not working well and  it has very low accuracy</strong>.</p>
<h2 dir="auto">environment like this</h2>
<p dir="auto">GPU:Geforce GTX1070<br>
Cuda toolkit version：7.5<br>
cuDNN version：7.0<br>
tensorflow version：0.9r</p>
<h2 dir="auto">code like this:</h2>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

def weight_varible(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')


mnist = input_data.read_data_sets(&quot;MNIST_data/&quot;, one_hot=True)
print(&quot;Download Done!&quot;)

sess = tf.InteractiveSession()

# paras
W_conv1 = weight_varible([5, 5, 1, 32])
b_conv1 = bias_variable([32])

# conv layer-1
x = tf.placeholder(tf.float32, [None, 784])
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)

# conv layer-2
W_conv2 = weight_varible([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)

# full connection
W_fc1 = weight_varible([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)

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

# output layer: softmax
W_fc2 = weight_varible([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
y_ = tf.placeholder(tf.float32, [None, 10])

# model training
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

correct_prediction = tf.equal(tf.arg_max(y_conv, 1), tf.arg_max(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

sess.run(tf.initialize_all_variables())

for i in range(20000):
    batch = mnist.train.next_batch(50)

    if i % 100 == 0:
        train_accuacy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
        print(&quot;step %d, training accuracy %g&quot;%(i, train_accuacy))
    train_step.run(feed_dict = {x: batch[0], y_: batch[1], keep_prob: 0.5})

# accuacy on test
print(&quot;test accuracy %g&quot;%(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})))"><pre class="notranslate"><span class="pl-k">import</span> <span class="pl-s1">tensorflow</span> <span class="pl-k">as</span> <span class="pl-s1">tf</span>
<span class="pl-k">from</span> <span class="pl-s1">tensorflow</span>.<span class="pl-s1">examples</span>.<span class="pl-s1">tutorials</span>.<span class="pl-s1">mnist</span> <span class="pl-k">import</span> <span class="pl-s1">input_data</span>

<span class="pl-k">def</span> <span class="pl-en">weight_varible</span>(<span class="pl-s1">shape</span>):
    <span class="pl-s1">initial</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-en">truncated_normal</span>(<span class="pl-s1">shape</span>, <span class="pl-s1">stddev</span><span class="pl-c1">=</span><span class="pl-c1">0.1</span>)
    <span class="pl-k">return</span> <span class="pl-s1">tf</span>.<span class="pl-v">Variable</span>(<span class="pl-s1">initial</span>)

<span class="pl-k">def</span> <span class="pl-en">bias_variable</span>(<span class="pl-s1">shape</span>):
    <span class="pl-s1">initial</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-en">constant</span>(<span class="pl-c1">0.1</span>, <span class="pl-s1">shape</span><span class="pl-c1">=</span><span class="pl-s1">shape</span>)
    <span class="pl-k">return</span> <span class="pl-s1">tf</span>.<span class="pl-v">Variable</span>(<span class="pl-s1">initial</span>)

<span class="pl-k">def</span> <span class="pl-en">conv2d</span>(<span class="pl-s1">x</span>, <span class="pl-v">W</span>):
    <span class="pl-k">return</span> <span class="pl-s1">tf</span>.<span class="pl-s1">nn</span>.<span class="pl-en">conv2d</span>(<span class="pl-s1">x</span>, <span class="pl-v">W</span>, <span class="pl-s1">strides</span><span class="pl-c1">=</span>[<span class="pl-c1">1</span>, <span class="pl-c1">1</span>, <span class="pl-c1">1</span>, <span class="pl-c1">1</span>], <span class="pl-s1">padding</span><span class="pl-c1">=</span><span class="pl-s">'SAME'</span>)

<span class="pl-k">def</span> <span class="pl-en">max_pool_2x2</span>(<span class="pl-s1">x</span>):
    <span class="pl-k">return</span> <span class="pl-s1">tf</span>.<span class="pl-s1">nn</span>.<span class="pl-en">max_pool</span>(<span class="pl-s1">x</span>, <span class="pl-s1">ksize</span><span class="pl-c1">=</span>[<span class="pl-c1">1</span>, <span class="pl-c1">2</span>, <span class="pl-c1">2</span>, <span class="pl-c1">1</span>], <span class="pl-s1">strides</span><span class="pl-c1">=</span>[<span class="pl-c1">1</span>, <span class="pl-c1">2</span>, <span class="pl-c1">2</span>, <span class="pl-c1">1</span>], <span class="pl-s1">padding</span><span class="pl-c1">=</span><span class="pl-s">'SAME'</span>)


<span class="pl-s1">mnist</span> <span class="pl-c1">=</span> <span class="pl-s1">input_data</span>.<span class="pl-en">read_data_sets</span>(<span class="pl-s">"MNIST_data/"</span>, <span class="pl-s1">one_hot</span><span class="pl-c1">=</span><span class="pl-c1">True</span>)
<span class="pl-en">print</span>(<span class="pl-s">"Download Done!"</span>)

<span class="pl-s1">sess</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-v">InteractiveSession</span>()

<span class="pl-c"># paras</span>
<span class="pl-v">W_conv1</span> <span class="pl-c1">=</span> <span class="pl-en">weight_varible</span>([<span class="pl-c1">5</span>, <span class="pl-c1">5</span>, <span class="pl-c1">1</span>, <span class="pl-c1">32</span>])
<span class="pl-s1">b_conv1</span> <span class="pl-c1">=</span> <span class="pl-en">bias_variable</span>([<span class="pl-c1">32</span>])

<span class="pl-c"># conv layer-1</span>
<span class="pl-s1">x</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-en">placeholder</span>(<span class="pl-s1">tf</span>.<span class="pl-s1">float32</span>, [<span class="pl-c1">None</span>, <span class="pl-c1">784</span>])
<span class="pl-s1">x_image</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-en">reshape</span>(<span class="pl-s1">x</span>, [<span class="pl-c1">-</span><span class="pl-c1">1</span>, <span class="pl-c1">28</span>, <span class="pl-c1">28</span>, <span class="pl-c1">1</span>])

<span class="pl-s1">h_conv1</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-s1">nn</span>.<span class="pl-en">relu</span>(<span class="pl-en">conv2d</span>(<span class="pl-s1">x_image</span>, <span class="pl-v">W_conv1</span>) <span class="pl-c1">+</span> <span class="pl-s1">b_conv1</span>)
<span class="pl-s1">h_pool1</span> <span class="pl-c1">=</span> <span class="pl-en">max_pool_2x2</span>(<span class="pl-s1">h_conv1</span>)

<span class="pl-c"># conv layer-2</span>
<span class="pl-v">W_conv2</span> <span class="pl-c1">=</span> <span class="pl-en">weight_varible</span>([<span class="pl-c1">5</span>, <span class="pl-c1">5</span>, <span class="pl-c1">32</span>, <span class="pl-c1">64</span>])
<span class="pl-s1">b_conv2</span> <span class="pl-c1">=</span> <span class="pl-en">bias_variable</span>([<span class="pl-c1">64</span>])

<span class="pl-s1">h_conv2</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-s1">nn</span>.<span class="pl-en">relu</span>(<span class="pl-en">conv2d</span>(<span class="pl-s1">h_pool1</span>, <span class="pl-v">W_conv2</span>) <span class="pl-c1">+</span> <span class="pl-s1">b_conv2</span>)
<span class="pl-s1">h_pool2</span> <span class="pl-c1">=</span> <span class="pl-en">max_pool_2x2</span>(<span class="pl-s1">h_conv2</span>)

<span class="pl-c"># full connection</span>
<span class="pl-v">W_fc1</span> <span class="pl-c1">=</span> <span class="pl-en">weight_varible</span>([<span class="pl-c1">7</span> <span class="pl-c1">*</span> <span class="pl-c1">7</span> <span class="pl-c1">*</span> <span class="pl-c1">64</span>, <span class="pl-c1">1024</span>])
<span class="pl-s1">b_fc1</span> <span class="pl-c1">=</span> <span class="pl-en">bias_variable</span>([<span class="pl-c1">1024</span>])

<span class="pl-s1">h_pool2_flat</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-en">reshape</span>(<span class="pl-s1">h_pool2</span>, [<span class="pl-c1">-</span><span class="pl-c1">1</span>, <span class="pl-c1">7</span> <span class="pl-c1">*</span> <span class="pl-c1">7</span> <span class="pl-c1">*</span> <span class="pl-c1">64</span>])
<span class="pl-s1">h_fc1</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-s1">nn</span>.<span class="pl-en">relu</span>(<span class="pl-s1">tf</span>.<span class="pl-en">matmul</span>(<span class="pl-s1">h_pool2_flat</span>, <span class="pl-v">W_fc1</span>) <span class="pl-c1">+</span> <span class="pl-s1">b_fc1</span>)

<span class="pl-c"># dropout</span>
<span class="pl-s1">keep_prob</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-en">placeholder</span>(<span class="pl-s1">tf</span>.<span class="pl-s1">float32</span>)
<span class="pl-s1">h_fc1_drop</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-s1">nn</span>.<span class="pl-en">dropout</span>(<span class="pl-s1">h_fc1</span>, <span class="pl-s1">keep_prob</span>)

<span class="pl-c"># output layer: softmax</span>
<span class="pl-v">W_fc2</span> <span class="pl-c1">=</span> <span class="pl-en">weight_varible</span>([<span class="pl-c1">1024</span>, <span class="pl-c1">10</span>])
<span class="pl-s1">b_fc2</span> <span class="pl-c1">=</span> <span class="pl-en">bias_variable</span>([<span class="pl-c1">10</span>])

<span class="pl-s1">y_conv</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-s1">nn</span>.<span class="pl-en">softmax</span>(<span class="pl-s1">tf</span>.<span class="pl-en">matmul</span>(<span class="pl-s1">h_fc1_drop</span>, <span class="pl-v">W_fc2</span>) <span class="pl-c1">+</span> <span class="pl-s1">b_fc2</span>)
<span class="pl-s1">y_</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-en">placeholder</span>(<span class="pl-s1">tf</span>.<span class="pl-s1">float32</span>, [<span class="pl-c1">None</span>, <span class="pl-c1">10</span>])

<span class="pl-c"># model training</span>
<span class="pl-s1">cross_entropy</span> <span class="pl-c1">=</span> <span class="pl-c1">-</span><span class="pl-s1">tf</span>.<span class="pl-en">reduce_sum</span>(<span class="pl-s1">y_</span> <span class="pl-c1">*</span> <span class="pl-s1">tf</span>.<span class="pl-en">log</span>(<span class="pl-s1">y_conv</span>))
<span class="pl-s1">train_step</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-s1">train</span>.<span class="pl-v">AdamOptimizer</span>(<span class="pl-c1">1e-4</span>).<span class="pl-en">minimize</span>(<span class="pl-s1">cross_entropy</span>)

<span class="pl-s1">correct_prediction</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-en">equal</span>(<span class="pl-s1">tf</span>.<span class="pl-en">arg_max</span>(<span class="pl-s1">y_conv</span>, <span class="pl-c1">1</span>), <span class="pl-s1">tf</span>.<span class="pl-en">arg_max</span>(<span class="pl-s1">y_</span>, <span class="pl-c1">1</span>))
<span class="pl-s1">accuracy</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-en">reduce_mean</span>(<span class="pl-s1">tf</span>.<span class="pl-en">cast</span>(<span class="pl-s1">correct_prediction</span>, <span class="pl-s1">tf</span>.<span class="pl-s1">float32</span>))

<span class="pl-s1">sess</span>.<span class="pl-en">run</span>(<span class="pl-s1">tf</span>.<span class="pl-en">initialize_all_variables</span>())

<span class="pl-k">for</span> <span class="pl-s1">i</span> <span class="pl-c1">in</span> <span class="pl-en">range</span>(<span class="pl-c1">20000</span>):
    <span class="pl-s1">batch</span> <span class="pl-c1">=</span> <span class="pl-s1">mnist</span>.<span class="pl-s1">train</span>.<span class="pl-en">next_batch</span>(<span class="pl-c1">50</span>)

    <span class="pl-k">if</span> <span class="pl-s1">i</span> <span class="pl-c1">%</span> <span class="pl-c1">100</span> <span class="pl-c1">==</span> <span class="pl-c1">0</span>:
        <span class="pl-s1">train_accuacy</span> <span class="pl-c1">=</span> <span class="pl-s1">accuracy</span>.<span class="pl-en">eval</span>(<span class="pl-s1">feed_dict</span><span class="pl-c1">=</span>{<span class="pl-s1">x</span>: <span class="pl-s1">batch</span>[<span class="pl-c1">0</span>], <span class="pl-s1">y_</span>: <span class="pl-s1">batch</span>[<span class="pl-c1">1</span>], <span class="pl-s1">keep_prob</span>: <span class="pl-c1">1.0</span>})
        <span class="pl-en">print</span>(<span class="pl-s">"step %d, training accuracy %g"</span><span class="pl-c1">%</span>(<span class="pl-s1">i</span>, <span class="pl-s1">train_accuacy</span>))
    <span class="pl-s1">train_step</span>.<span class="pl-en">run</span>(<span class="pl-s1">feed_dict</span> <span class="pl-c1">=</span> {<span class="pl-s1">x</span>: <span class="pl-s1">batch</span>[<span class="pl-c1">0</span>], <span class="pl-s1">y_</span>: <span class="pl-s1">batch</span>[<span class="pl-c1">1</span>], <span class="pl-s1">keep_prob</span>: <span class="pl-c1">0.5</span>})

<span class="pl-c"># accuacy on test</span>
<span class="pl-en">print</span>(<span class="pl-s">"test accuracy %g"</span><span class="pl-c1">%</span>(<span class="pl-s1">accuracy</span>.<span class="pl-en">eval</span>(<span class="pl-s1">feed_dict</span><span class="pl-c1">=</span>{<span class="pl-s1">x</span>: <span class="pl-s1">mnist</span>.<span class="pl-s1">test</span>.<span class="pl-s1">images</span>, <span class="pl-s1">y_</span>: <span class="pl-s1">mnist</span>.<span class="pl-s1">test</span>.<span class="pl-s1">labels</span>, <span class="pl-s1">keep_prob</span>: <span class="pl-c1">1.0</span>})))</pre></div>
<h3 dir="auto">on cpu</h3>
<p dir="auto">step 0, training accuracy 0.06<br>
step 100, training accuracy 0.8<br>
step 200, training accuracy 0.94<br>
step 300, training accuracy 0.9<br>
step 400, training accuracy 0.92<br>
step 500, training accuracy 0.84<br>
step 600, training accuracy 1<br>
step 700, training accuracy 0.96<br>
step 800, training accuracy 0.94<br>
step 900, training accuracy 1<br>
step 1000, training accuracy 0.98<br>
step 1100, training accuracy 0.92<br>
step 1200, training accuracy 0.96<br>
step 1300, training accuracy 1<br>
step 1400, training accuracy 1<br>
step 1500, training accuracy 1<br>
......<br>
test accuracy 0.9671</p>
<h3 dir="auto">on gpu</h3>
<p dir="auto">step 0, training accuracy 0.1<br>
step 100, training accuracy 0.08<br>
step 200, training accuracy 0.02<br>
step 300, training accuracy 0.12<br>
step 400, training accuracy 0.1<br>
step 500, training accuracy 0.1<br>
step 600, training accuracy 0.12<br>
step 700, training accuracy 0.12<br>
step 800, training accuracy 0.1<br>
step 900, training accuracy 0.04<br>
step 1000, training accuracy 0.12<br>
step 1100, training accuracy 0.3<br>
step 1200, training accuracy 0.1<br>
step 1300, training accuracy 0.08<br>
step 1400, training accuracy 0.12<br>
step 1500, training accuracy 0.2<br>
......<br>
test accuracy 0.1160</p>