Cannot reproduce example outputs about neural-style-transfer HOT 6 CLOSED

{
    "floatx": "float32",
    "backend": "tensorflow",
    "epsilon": 1e-07,
    "image_dim_ordering": "tf"
}

$ python Network.py images/inputs/content/sagano_bamboo_forest.jpg images/inputs/style/patterned_leaves.jpg images/output/test4

Using TensorFlow backend.
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties: 
name: GeForce GTX 670
major: 3 minor: 0 memoryClockRate (GHz) 0.98
pciBusID 0000:01:00.0
Total memory: 2.00GiB
Free memory: 1.78GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:839] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 670, pci bus id: 0000:01:00.0)
Model loaded.
Start of iteration 1
W tensorflow/core/common_runtime/bfc_allocator.cc:213] Ran out of memory trying to allocate 1.11GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:213] Ran out of memory trying to allocate 1.11GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:213] Ran out of memory trying to allocate 2.15GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:213] Ran out of memory trying to allocate 1.09GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:213] Ran out of memory trying to allocate 2.15GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:213] Ran out of memory trying to allocate 1.09GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:213] Ran out of memory trying to allocate 2.15GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:213] Ran out of memory trying to allocate 1.09GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:213] Ran out of memory trying to allocate 2.15GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W tensorflow/core/common_runtime/bfc_allocator.cc:213] Ran out of memory trying to allocate 1.09GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
Network.py:352: RuntimeWarning: invalid value encountered in double_scalars
  improvement = (prev_min_val - min_val) / prev_min_val * 100
Current loss value: 1.00896e+08  Improvement : nan %
Rescaling Image to (1080, 1920)
Image saved as images/output/test4_at_iteration_1.png
Iteration 1 completed in 23s
Start of iteration 2
Current loss value: 3.24734e+07  Improvement : 67.815 %
Rescaling Image to (1080, 1920)
Image saved as images/output/test4_at_iteration_2.png
Iteration 2 completed in 21s
Start of iteration 3
Current loss value: 1.9547e+07  Improvement : 39.806 %
Rescaling Image to (1080, 1920)
Image saved as images/output/test4_at_iteration_3.png
Iteration 3 completed in 20s
Start of iteration 4
Current loss value: 1.44546e+07  Improvement : 26.052 %
Rescaling Image to (1080, 1920)
Image saved as images/output/test4_at_iteration_4.png
Iteration 4 completed in 21s
Start of iteration 5
I tensorflow/core/common_runtime/gpu/pool_allocator.cc:244] PoolAllocator: After 21978 get requests, put_count=21977 evicted_count=1000 eviction_rate=0.0455021 and unsatisfied allocation rate=0.0500956
I tensorflow/core/common_runtime/gpu/pool_allocator.cc:256] Raising pool_size_limit_ from 100 to 110
Current loss value: 1.18752e+07  Improvement : 17.845 %
Rescaling Image to (1080, 1920)
Image saved as images/output/test4_at_iteration_5.png
Iteration 5 completed in 20s
Start of iteration 6
Current loss value: 9.70512e+06  Improvement : 18.274 %
Rescaling Image to (1080, 1920)
Image saved as images/output/test4_at_iteration_6.png
Iteration 6 completed in 21s
Start of iteration 7
Current loss value: 8.33909e+06  Improvement : 14.075 %
Rescaling Image to (1080, 1920)
Image saved as images/output/test4_at_iteration_7.png
Iteration 7 completed in 20s
Start of iteration 8
Current loss value: 7.34698e+06  Improvement : 11.897 %
Rescaling Image to (1080, 1920)
Image saved as images/output/test4_at_iteration_8.png
Iteration 8 completed in 21s
Start of iteration 9
Current loss value: 6.64953e+06  Improvement : 9.493 %
Rescaling Image to (1080, 1920)
Image saved as images/output/test4_at_iteration_9.png
Iteration 9 completed in 21s
Start of iteration 10
Current loss value: 5.85107e+06  Improvement : 12.008 %
Rescaling Image to (1080, 1920)
Image saved as images/output/test4_at_iteration_10.png
Iteration 10 completed in 21s