np.set_printoptions(precision=3)
mode = raw_input("1: Train\n2: Load\n3: Live\n")
exp = theanets.Experiment(
theanets.feedforward.Regressor,
layers=(config.IMG_W * config.IMG_H, 500, 1)
)
if mode == "1":
trainer.train(exp)
if mode == "2":
exp = exp.load(path="net.data")
print "Manual validation:"
for file in glob.glob("data/manual/*.jpg"):
image = util_image.load(file)
input = util_image.data(image)
input = input.reshape(1, len(input))
output = exp.predict(input)
print "Prediction for ", file, " = ", output
if mode == "3":
exp = exp.load(path="net.data")
session = util_guru.start()
while True:
image = util_guru.download(session)
print "Opening image..."
image.show()
input = util_image.get_single_input_data(image)
output = exp.predict(input)[0][0]
print "Crowded Level: ", output