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train_nn.py
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train_nn.py
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import numpy as np
import sys, os
from nn import NeuralNetwork
def main(args):
if args[0] == '-numbers':
run_training('mnist', '_', args, 10)
elif args[0] == '-letters':
run_training('emnist-letters', '-', args, 26)
def run_training(set, sep, args, num_classes):
exist_tr = os.path.isfile("training_images/" + set + "/" + set + sep + "train.pkl")
exist_test = os.path.isfile("training_images/" + set + "/" + set + sep +"test.pkl")
dataset_train = None
dataset_test = None
if exist_tr and exist_test:
dataset_train = load_from_pickle(set, sep + "train")
dataset_test = load_from_pickle(set, sep + "test")
else:
dataset_train = load_from_csv(set, sep + "train")
dataset_test = load_from_csv(set, sep + "test")
img_size = 28
img_pixels = img_size * img_size
print("Training neural network...")
iter_limit = 100
for i, arg in zip(range(len(args)), args):
if '-it=' in arg:
iter_limit = int(arg.split("=")[1])
nodes = 100
for i, arg in zip(range(len(args)), args):
if '-n=' in arg:
nodes = int(arg.split("=")[1])
acc_limit = 0.85
for i, arg in zip(range(len(args)), args):
if '-a=' in arg:
acc_limit = float(arg.split("=")[1])
if acc_limit >= 1.0:
print("Accuracy threshold is too high. Reset to 0.85...")
acc_limit = 0.85
nn = NeuralNetwork(img_pixels, num_classes, nodes)
params = None
if '-r' in args:
print("Resuming from previous training...")
params = np.load("trained/" + set + "_params.npy")
acc = 0.0
iter = 0
while acc < acc_limit and iter < iter_limit:
data_train, labels_train = build_dataset(dataset_train, num_classes)
data_test, labels_test = build_dataset(dataset_test, num_classes)
params = nn.train(data_train, labels_train, params)
np.save("trained/" + set + "_params", params)
acc = nn.test(data_test, labels_test)
print("Iteration {} completed with {}% accuracy".format(iter, round(acc*100,4)))
iter += 1
def load_from_pickle(set, type):
print("Loading mnist " + set + " from pickle...")
with open("training_images/" + set + "/" + set + type + ".pkl", "br") as fh:
data= np.load(fh)
return data
def load_from_csv(set, type):
print("Loading " + set + type + " from csv...")
dataset = np.loadtxt("training_images/" + set + "/" + set + type + ".csv", skiprows=1, delimiter=",")
print("Pickling data...")
with open("training_images/" + set + "/" + set + type + ".pkl", "bw") as fh:
np.save(fh, dataset)
return dataset
def build_dataset(dataset, num_classes):
np.random.shuffle(dataset)
data = dataset[:,1:]
shape = data.shape
data = data.reshape((shape[0], shape[1], 1))
data = np.asfarray(data) * 0.99 / 255 + 0.01
labels = np.zeros((len(dataset), num_classes, 1))
for i in range(dataset.shape[0]):
index = int(dataset[i,0])
labels[i, index - 1, 0] = 1
labels[labels==0] = 0.01
labels[labels==1] = 0.99
return data, labels
if __name__ == '__main__':
main(sys.argv[1:])