I’m trying to implement a simple neural network and test how the neurons behave with a video as input. I am pretty new in Nengo and after a few weeks of research and a some attempts of different solutions I can not manage to feed the video to the neural network.
Below you will find my approach to the problem. I’m trying to feed a video (split in frames, so actually I’m feeding images) to a Neuron and connecting this one to other with a sinusoidal output to see how it behaves. I really don’t know if this is the best approach and for now on it’s not working, I got trouble with the array dimension in the “eval_points”…
I’m open to any suggestion, thank you in advance!
#Load images images_files = glob.glob("full-path/one_clip_frames/*.jpg") images =  for file in images_files: img = PIL.Image.open(file) img_array = np.array(img.getdata()).flatten() images.append(img_array) img_rows, img_cols = 640, 480 img_size = 640 * 480 #number of hidden units n_hid = 1000 ens_params = dict( eval_points=images, neuron_type=nengo.LIFRate(), intercepts=nengo.dists.Choice([0.1]), max_rates=nengo.dists.Choice(), ) with nengo.Network() as model: a = nengo.Ensemble(n_hid, img_size, **ens_params) v = nengo.Node(np.sin) conn = nengo.Connection( a, v, synapse=None, eval_points=images) encoders = rng.normal(size=(n_hid, img_size)) a.encoders = encoders tile(encoders.reshape(-1, img_rows, img_cols), rows=4, cols=6, grid=True)