def run_nnet(train_data, test_data, param):
net = NeuralNet(len(test_data[0].rgbs_merged), param)
train_data_ = [ (img.rgbs_merged, img.orientation) for img in train_data ]
print "Neural net: Training..."
net.train(train_data_, 5)
classified_0 = []
classified_90 = []
classified_180 = []
classified_270 = []
print "Neural net: Classification..."
for test in test_data:
ret = int(round(net.output(test.rgbs_merged)))
ret_df = [(abs(0 - ret), 0),
(abs(90 - ret), 90),
(abs(180 - ret), 180),
(abs(270 - ret), 270)]
ret = min(ret_df, key=lambda (x, _): x)[1]
ret_str = "(" + str(ret == test.orientation) + ")"
print test.name, "classified as", ret, ret_str
if ret == 0:
classified_0.append(test)
elif ret == 90:
classified_90.append(test)
elif ret == 180:
classified_180.append(test)
elif ret == 270:
classified_270.append(test)
else:
raise RuntimeError("Bug: nnet returned %s" % str(ret))
f = open("nnet_output.txt", "w")
print_confusion_matrix(f, classified_0, classified_90, classified_180, classified_270)
f.close()
