def convert_images():
# convert the training data
for i in range(4):
# Open up the file to write in
path = "./test_data/" + cfg.emojis[i] + "/converted.csv"
f = open(path, 'w+')
w = csv.writer(f)
# Get the test cases
test_cases = glob.glob('./test_data/' + cfg.emojis[i] + '/*.png')
print 'Converting ' + cfg.emojis[i]
for test_case in test_cases:
# convert image
data = image.binary_image(test_case, cfg.RES)
data = image.convert_to_1d(data)
# Write data into file
w.writerow(data)
# close the file
f.close()
print("files converted!")
def get_testing_error(net):
# choose an emotion:
error = [0.0, 0.0, 0.0, 0.0]
for i, emotion in enumerate(cfg.emojis):
test_cases = glob.glob('./test_data/non-training/' + emotion + '/*.png')
for test_case in test_cases:
inp = image.convert_to_1d(image.binary_image(test_case, cfg.RES))
e = net.forward_pass(inp)
error[i] += nn.error(cfg.outputs[i], e)
error[i] /= len(test_cases)
return sum(error)
def test_image(path, net):
# Run the image
inp = image.convert_to_1d(image.binary_image(path, cfg.RES))
result = net.forward_pass(inp)
normalize(result)
# print out result
print(path + ":")
print(" heart eyes:" + str(format((result[0]*100), '.2f')) + "%")
print(" laugh:" + str(format((result[1]*100), '.2f')) + "%")
print(" sad:" + str(format((result[2]*100), '.2f')) + "%")
print(" smile:" + str(format((result[3]*100), '.2f')) + "%")