tensorflow无法找到合适的算法来进行前向卷积

在TensorFlow website上运行PDE示例时

#Import libraries for simulation
import tensorflow as tf
import numpy as np

sess = tf.InteractiveSession()

def make_kernel(a):
  """Transform a 2D array into a convolution kernel"""
  a = np.asarray(a)
  a = a.reshape(list(a.shape) + [1,1])
  return tf.constant(a, dtype=1)

def simple_conv(x, k):
  """A simplified 2D convolution operation"""
  x = tf.expand_dims(tf.expand_dims(x, 0), -1)
  y = tf.nn.depthwise_conv2d(x, k, [1, 1, 1, 1], padding='SAME')
  return y[0, :, :, 0]

def laplace(x):
  """Compute the 2D laplacian of an array"""
  laplace_k = make_kernel([[0.5, 1.0, 0.5],
                           [1.0, -6., 1.0],
                           [0.5, 1.0, 0.5]])
  return simple_conv(x, laplace_k)



# Initial Conditions -- some rain drops hit a pond
N = 500

# Set everything to zero
u_init = np.zeros([N, N], dtype=np.float32)
ut_init = np.zeros([N, N], dtype=np.float32)

# Some rain drops hit a pond at random points
for n in range(40):
  a,b = np.random.randint(0, N, 2)
  u_init[a,b] = np.random.uniform()


# Parameters:
# eps -- time resolution
# damping -- wave damping
eps = tf.placeholder(tf.float32, shape=())
damping = tf.placeholder(tf.float32, shape=())

# Create variables for simulation state
U  = tf.Variable(u_init)
Ut = tf.Variable(ut_init)

# Discretized PDE update rules
U_ = U + eps * Ut
Ut_ = Ut + eps * (laplace(U) - damping * Ut)

# Operation to update the state
step = tf.group(
  U.assign(U_),
  Ut.assign(Ut_))

# Initialize state to initial conditions
tf.initialize_all_variables().run()

# Run 1000 steps of PDE
nsteps = 1000
for i in range(nsteps):
  # Step simulation
  step.run({eps: 0.03, damping: 0.04})
  # Visualize every 50 steps
  if i % 50 == 0:
    print("iter = %d, max(U) = %f, min(U) = %f" % \
        (i, np.max(U.eval()), np.min(U.eval())))

sess.close()

在我的本地机器上的GPU上，我在循环中收到以下错误 step.run({eps: 0.03, damping: 0.04})

I tensorflow / core / common_runtime / gpu / gpu_device.cc：755]创建TensorFlow设备（/ gpu：0） - >（设备：0，名称：GeForce GTX 750 Ti，pci总线ID：0000：01：00.0）F tensorflow / stream_executor / cuda / cuda_dnn.cc：675]检查失败：status == CUDNN_STATUS_SUCCESS（3 vs. 0）无法找到合适的算法进行前向卷积中止（核心转储）

当我使用CPU with tf.device('/cpu:0'): 运行代码时，它工作正常 . 另外，我已经使用GPU运行其他示例了 .

这是他们尚未实施的功能吗？或者我在某个地方犯了错误？

系统信息：

操作系统：Ubuntu 14.04 LTS

显卡：GeForce GTX 750 Ti

已安装的CUDA和cuDNN版本：CUDA 7.5，cuNN v5

我从GitHub拉出来安装了源代码 . 有关GitHub问题跟踪器的更多信息：https://github.com/tensorflow/tensorflow/issues/2174