Jtdylktuy

I'm having a compile issue. 
The code I'm executing is from Udacity's deep learning tutorial assignment #4. 
This leads me to believe that the problem does not lie within the code but within the software tools that I'm using. I didn't have any issues with the previous three assignments, but now I'm using TensorFlow conv2d member. My system details and error output are listed below. Any help would be greatly appreciated. 
If you need the code, let me know and I'll post it.

System Details:

• System: Windows 10 home 64-bit, x64-based processor


• Cuda: v 9.0.176


• CUDNN: v 9.0 win10x64 7.3.1.2


• tf-gpu: v 1.5.0 via PIP


• NVIDIA: GTX 1060 6 GiB


• NVIDIA DRIVER VERSION: 417.35


• python v: 3.6.7

Output:

~DocumentsUdacityDeep LearningAssignment 4 (CNN's)> python main.py

Training set (200000, 28, 28) (200000,)

Validation set (10000, 28, 28) (10000,)

Test set (10000, 28, 28) (10000,)

Training set (200000, 28, 28, 1) (200000, 10)

Validation set (10000, 28, 28, 1) (10000, 10)

Test set (10000, 28, 28, 1) (10000, 10)

2019-01-04 15:40:09.714793: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcoreplatformcpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2

2019-01-04 15:40:10.003545: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorecommon_runtimegpugpu_device.cc:1105] Found device 0 with properties:

name: GeForce GTX 1060 major: 6 minor: 1 memoryClockRate(GHz): 1.6705

pciBusID: 0000:01:00.0

totalMemory: 6.00GiB freeMemory: 4.97GiB

2019-01-04 15:40:10.013346: I C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorecommon_runtimegpugpu_device.cc:1195] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1060, pci bus

id: 0000:01:00.0, compute capability: 6.1)

Initialized

2019-01-04 15:40:12.584016: E C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowstream_executorcudacuda_dnn.cc:378] Loaded runtime CuDNN library: 7301 (compatibility version 7300) but source was compiled with 7003 (compatibility version 7000). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.

2019-01-04 15:40:12.601433: F C:tf_jenkinsworkspacerel-winMwindows-gpuPY36tensorflowcorekernelsconv_ops.cc:717] Check failed: stream->parent()->GetConvolveAlgorithms( conv_parameters.ShouldIncludeWinogradNonfusedAlgo(), &algorithms)