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hand-gesture-recognizer's Issues

unable to run demo

cv@cv:~/Deeplearning/Hand-Gesture-Recognizer-master$ python ./main.py 
/usr/local/lib/python2.7/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
2018-02-19 17:29:16.369754: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA
2018-02-19 17:29:16.611149: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties: 
name: TITAN Xp major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:17:00.0
totalMemory: 11.90GiB freeMemory: 11.66GiB
2018-02-19 17:29:16.611178: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: TITAN Xp, pci bus id: 0000:17:00.0, compute capability: 6.1)
Model successfully loaded from disk.
cv@cv:~/Deeplearning/Hand-Gesture-Recognizer-master$ 

It loads the model successfully but didńt show anything. I am using Python 2.7 ,Keras 2.1.4, Tensorflow 1.4.0 , OpenCV 3.1, numpy 1.14.0

Demo not running

Using TensorFlow backend.
/home/pi/.virtualenvs/cv/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.4 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5
  return f(*args, **kwds)
/home/pi/.virtualenvs/cv/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: builtins.type size changed, may indicate binary incompatibility. Expected 432, got 412
  return f(*args, **kwds)

** (main.py:5280): WARNING **: Error retrieving accessibility bus address: org.freedesktop.DBus.Error.ServiceUnknown: The name org.a11y.Bus was not provided by any .service files
Model successfully loaded from disk.
Do you want to plot a realtime histogram as well? (slower) y/n
n

Nothing happens after this.

If I answer yes, I get a lot of this:
SystemError: null argument to internal routine

Is there a reason to specify the input shape twice.

Please correct me if I am wrong, in the second hidden layer, you resize the input back to original size after max pulling it. Is there a particular reason for this?

model.add(Convolution2D(32,3,3, input_shape=(200,200,1),activation='relu'))

Also, did you just train the network for 10 epochs to get around 90% accuracy? If the weights are randomly initialized, that's pretty amazing efficiency...

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