def main():
inputs, targets = preProcessing('konel_egg_train.csv')
if check_point == "new":
network = NeuralNetwork(34, 2, 15, 3, 0.01)
elif check_point == "last":
network = NeuralNetwork(34, 2, 15, 3, 0.01)
network.load_model("../data/egg_model_weights.csv")
print("Starting training eggnet:")
time.sleep(1)
for e in range(EPOCH):
print("====================================")
print("Epoch:", e, '/', EPOCH)
for i in range(len(targets)):
cost = network.train(inputs[i], targets[i])
print("Training done!")
print("Saving weights file to ../data/egg_model_weights.csv")
network.save_model('../data/egg_model_weights.csv')
print("Done!")
_test = input("Press Y for testing model / N to quit: ")
if _test == "Y" or _test == "y":
network = NeuralNetwork(34, 2, 15, 3)
network.load_model('../data/egg_model_weights.csv')
guess = [None] * len(inputs)
score = 0
for i in range(len(inputs)):
result = network.feedFoward(inputs[i]).T
index = np.where(result == np.amax(result))
result = np.zeros(3) + 0.01
result[index[1]] = 0.99
guess[i] = result
for i in range(len(targets)):
if np.array_equal(guess[i], targets[i]):
score += 1
print("Testing done:", "Score: ", score, " ",
score * 100 / len(targets))