-
Notifications
You must be signed in to change notification settings - Fork 15
/
train.py
68 lines (59 loc) · 2.64 KB
/
train.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
import numpy as np
from sklearn.datasets import fetch_openml
import matplotlib.pyplot as plt
import time
import argparse
from model import DeepNeuralNetwork
# Settings
parser = argparse.ArgumentParser(description='Neural Networks from Scratch')
parser.add_argument('--activation', action='store', dest='activation', required=False, default='sigmoid', help='activation function: sigmoid/relu')
parser.add_argument('--batch_size', action='store', dest='batch_size', required=False, default=128)
parser.add_argument('--optimizer', action='store', dest='optimizer', required=False, default='momentum', help='optimizer: sgd/momentum')
parser.add_argument('--l_rate', action='store', dest='l_rate', required=False, default=1e-3, help='learning rate')
parser.add_argument('--beta', action='store', dest='beta', required=False, default=.9, help='beta in momentum optimizer')
args = parser.parse_args()
# Helper function
def show_images(image, num_row=2, num_col=5):
# plot images
image_size = int(np.sqrt(image.shape[-1]))
image = np.reshape(image, (image.shape[0], image_size, image_size))
fig, axes = plt.subplots(num_row, num_col, figsize=(1.5*num_col,2*num_row))
for i in range(num_row*num_col):
ax = axes[i//num_col, i%num_col]
ax.imshow(image[i], cmap='gray', vmin=0, vmax=1)
ax.axis('off')
plt.tight_layout()
plt.show()
def one_hot(x, k, dtype=np.float32):
"""Create a one-hot encoding of x of size k."""
return np.array(x[:, None] == np.arange(k), dtype)
def main():
# Load data
print("Loading data...")
mnist_data = fetch_openml("mnist_784")
x = mnist_data["data"]
y = mnist_data["target"]
# Normalize
print("Preprocessing data...")
x /= 255.0
# One-hot encode labels
num_labels = 10
examples = y.shape[0]
y_new = one_hot(y.astype('int32'), num_labels)
# Split, reshape, shuffle
train_size = 60000
test_size = x.shape[0] - train_size
x_train, x_test = x[:train_size], x[train_size:]
y_train, y_test = y_new[:train_size], y_new[train_size:]
shuffle_index = np.random.permutation(train_size)
x_train, y_train = x_train[shuffle_index], y_train[shuffle_index]
print("Training data: {} {}".format(x_train.shape, y_train.shape))
print("Test data: {} {}".format(x_test.shape, y_test.shape))
# show_images(x_train)
# Train
print("Start training!")
dnn = DeepNeuralNetwork(sizes=[784, 64, 10], activation=args.activation)
dnn.train(x_train, y_train, x_test, y_test,
batch_size=int(args.batch_size), optimizer=args.optimizer, l_rate=float(args.l_rate), beta=float(args.beta))
if __name__ == '__main__':
main()