Hello,
I've tried adapting your approach during training to some pre-existing code of mine,
however I am constantly met with the ValueError. My model is different from yours, but essentially does the same procedure. My original training approach had a check for while not done, but as the first episode quits early (?) There would always be some error, and hence I wanted to try your approach.
https://gist.github.com/EXJUSTICE/0df29caedee2a72a7e5faf7aa88cbd03
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-159-aebc713aaeac> in <module>()
63 next_obs=np.zeros(obs.shape)
64
---> 65 next_obs,stacked_frames= stack_frames(stacked_frames,next_obs,False)
66 step = max_steps
67 history.append(episodic_reward)
3 frames
/usr/local/lib/python3.6/dist-packages/skimage/transform/_warps.py in warp(image, inverse_map, map_args, output_shape, order, mode, cval, clip, preserve_range)
805
806 if image.size == 0:
--> 807 raise ValueError("Cannot warp empty image with dimensions", image.shape)
808
809 image = convert_to_float(image, preserve_range)
ValueError: ('Cannot warp empty image with dimensions', (0, 24))
After investigating, it's clear to me that the error is from the preprocessing function, where you call to transform the preprocessed image to size 84,84 using sci-image. I changed your code to ensure that grayscaling happened as well. The transformation cannot occur with an empty array of zeros?
def preprocess_observation(frame):
# Crop and resize the image into a square, as we don't need the excess information
cropped = frame[60:-60,30:-30]
normalized = cropped/255.0
# Improve image contrast See if works
#img[img==color] = 0
# Next we normalize the image from -1 to +1 See if works
#img = (img - 128) / 128 - 1
img_gray = rgb2gray(normalized)
preprocessed_frame = transform.resize(img_gray, [84,84])
return preprocessed_frame
Hello,
I've tried adapting your approach during training to some pre-existing code of mine,
however I am constantly met with the ValueError. My model is different from yours, but essentially does the same procedure. My original training approach had a check for while not done, but as the first episode quits early (?) There would always be some error, and hence I wanted to try your approach.
https://gist.github.com/EXJUSTICE/0df29caedee2a72a7e5faf7aa88cbd03
After investigating, it's clear to me that the error is from the preprocessing function, where you call to transform the preprocessed image to size 84,84 using sci-image. I changed your code to ensure that grayscaling happened as well. The transformation cannot occur with an empty array of zeros?