Issue 1:
With the tensorflow/model/image/cifar10 example. While I run cifar10_train.py,

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.

I see Alexnet benchmark performance gets improved, but this actually use case gets worse performance.

Issue 2:
hi everyone:

I tried to run an examples of MNIST with cnn and when i only use cpu the code can work well but when i use gpu it is not working well and it has very low accuracy.

environment like this

GPU:Geforce GTX1070

Cuda toolkit version：7.5

cuDNN version：7.0

tensorflow version：0.9r

code like this:

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("MNIST_data/", one_hot=True)
print("Download Done!")

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("step %d, training accuracy %g"%(i, train_accuacy))
train_step.run(feed_dict = {x: batch[0], y_: batch[1], keep_prob: 0.5})

# accuacy on test
print("test accuracy %g"%(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})))

on cpu

step 0, training accuracy 0.06

step 100, training accuracy 0.8

step 200, training accuracy 0.94

step 300, training accuracy 0.9

step 400, training accuracy 0.92

step 500, training accuracy 0.84

step 600, training accuracy 1

step 700, training accuracy 0.96

step 800, training accuracy 0.94

step 900, training accuracy 1

step 1000, training accuracy 0.98

step 1100, training accuracy 0.92

step 1200, training accuracy 0.96

step 1300, training accuracy 1

step 1400, training accuracy 1

step 1500, training accuracy 1

......

test accuracy 0.9671

on gpu

step 0, training accuracy 0.1

step 100, training accuracy 0.08

step 200, training accuracy 0.02

step 300, training accuracy 0.12

step 400, training accuracy 0.1

step 500, training accuracy 0.1

step 600, training accuracy 0.12

step 700, training accuracy 0.12

step 800, training accuracy 0.1

step 900, training accuracy 0.04

step 1000, training accuracy 0.12

step 1100, training accuracy 0.3

step 1200, training accuracy 0.1

step 1300, training accuracy 0.08

step 1400, training accuracy 0.12

step 1500, training accuracy 0.2

......

test accuracy 0.1160