from dataparser import train_test_split
from preprocess import preprocess
import time
input_size, output_size, train_data, test_data = train_test_split(
False, False, 0.1, True)
#train_data = train_data[len(train_data) // 2:]
print("Parsed data")
assert (input_size in [784, 785, 288, 289] and output_size == 10)
layers = [input_size, 128, 64, output_size]
print(layers)
# layers = [input_size, output_size]
lr = (0.001, 0.0005)
transfer = "logistic"
batch_size = 4
brain = NN(layers, lr, transfer, batch_size)
print("Error", brain.get_error(test_data))
print("Training")
print(len(train_data))
start = time.time()
brain.learn(train_data, test_data, 100, 5, "weights/weights12864_{}.npz")
print("Took:", time.time() - start)
print("Error", brain.get_error(test_data))
print("Accuracy", brain.get_correct(test_data))
brain.save("weights/weights12864_final.npz")
from nn import NN
from dataparser import train_test_split, submit
input_size, output_size, train_data, test_data = train_test_split(
False, False, 0.1, True)
layers = [input_size, 64, 128, output_size]
print(layers)
# layers = [input_size, output_size]
lr = (0.001, 0.001)
transfer = "logistic"
brain = NN(layers, lr, transfer, 32)
brain.load('weights/weights4_final.npz')
print("Error 1:", brain.get_error(train_data))
print("Accuracy 1:", brain.get_correct(train_data))
print("Error 2:", brain.get_error(test_data))
print("Accuracy 2:", brain.get_correct(test_data))
submit(brain, 'submission.csv', True)