-
Notifications
You must be signed in to change notification settings - Fork 0
/
cifar_train.py
183 lines (154 loc) · 6.33 KB
/
cifar_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
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
print("importing torch")
import torch
import torchvision
import torchvision.transforms as transforms
import sys
import torch.nn as nn
import torch.nn.functional as F
from net import Net
from datetime import datetime
import matplotlib.pyplot as plt
import torch.optim as optim
import os
def train(batch, learning_rate, epochs, batch_per):
#create a folder for graohs
folder = str(datetime.now())
os.mkdir(f'./graphs/{folder}')
#get, prepare, and split data
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
print("importing files")
loader = torch.utils.data.DataLoader(torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform),
batch_size=batch, shuffle=True, num_workers=0)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
trainloader = []
valloader = []
for i, data in enumerate(loader, 0):
if i <= (len(loader) / 10) * 8.5:
trainloader.append(data)
else:
valloader.append(data)
net = Net()
#def optim, loss, and init graph data
criterion = nn.CrossEntropyLoss()
# optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)
print("begin training")
x = []
y = []
valx = []
valy = []
corx = []
corvalx = []
cory = []
corvaly = []
#these go down, and random loss is ~2.303 so 15 will be replaced
best = 15
bestval = 15
for epoch in range(epochs): # loop over the dataset multiple times
## possibly change lr
# if epoch < 8:
# ler = ler/(epoch + 1)
# optimizer = optim.SGD(net.parameters(), lr=ler, momentum=0.9)
correct = 0
total = 0
running_loss = 0.0
# train mode
net.train()
for i, data in enumerate(trainloader, 0):
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
# print statistics
running_loss += loss.item()
if i % batch_per == batch_per - 1: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / batch_per))
x.append((epoch * len(trainloader)) + i)
y.append(running_loss/batch_per)
# PATH = f'./{folder}/net.pth'
# torch.save(net.state_dict(), PATH)
#possibly exit if loss goes up to much
# if ((running_loss/(len(trainloader) / update_checks)) - best) > (running_loss /(len(trainloader) / update_checks)) / (epoch + 1):
# running_loss = 0
# plt.plot(x, y, label = "train")
# plt.plot(valx, valy, label = "valid")
# plt.legend()
# plt.ylabel('Running Loss')
# plt.xlabel('Updates')
# plt.savefig(f'./graphs/{folder}/loss.png')
# return 0
best = min(best, running_loss / batch_per)
running_loss = 0
print('Accuracy of the network on the ' + str(batch_per) + 'th update: %d %%' % (
100 * correct / total))
cory.append(100 * correct / total)
corx.append((epoch * len(trainloader)) + i)
correct = 0
total = 0
running_loss = 0
net.eval()
correct = 0
total = 0
#check val set
for i, data in enumerate(valloader, 0):
inputs, labels = data
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
# total += labels.size(0)
# correct += (predicted == labels).sum().item()
running_loss += loss.item()
valx.append(((epoch + 1) * len(trainloader)))
valy.append(running_loss/len(valloader))
bestval = min(bestval, running_loss / len(valloader))
# corvaly.append(100 * correct / total)
# corvalx.append(((epoch + 1) * len(trainloader)))
# if val loss goes up to much exit and plot
if running_loss/len(valloader) - bestval > (running_loss/len(valloader)) / ((epoch + 1) * 1):
plt.plot(x, y, label = "train")
plt.plot(valx, valy, label = "valid")
plt.legend()
plt.ylabel('Running Loss')
plt.xlabel('Updates')
plt.savefig(f'./graphs/{folder}/loss.png')
plt.clf()
plt.plot(corx, cory, label = "train")
# plt.plot(corvalx, corvaly, label = "valid")
plt.legend()
plt.ylabel('Accuracy')
plt.xlabel('Updates')
plt.savefig(f'./graphs/{folder}/accuracy.png')
return [plt, net]
running_loss = 0
correct = 0
total = 0
# finish training. in future dont plot and save here just return them
print('Finished Training')
plt.plot(x, y, label = "train")
plt.plot(valx, valy, label = "valid")
plt.legend()
plt.ylabel('Running Loss')
plt.xlabel('Updates')
plt.savefig(f'./graphs/{folder}/loss.png')
plt.clf()
plt.plot(corx, cory, label = "train")
plt.plot(corvalx, corvaly, label = "valid")
plt.legend()
plt.ylabel('Accuracy')
plt.xlabel('Updates')
plt.savefig(f'./graphs/{folder}/accuracy.png')
return [plt, net]
# PATH = f'./{folder}/net.pth'
# torch.save(net.state_dict(), PATH)