-
Notifications
You must be signed in to change notification settings - Fork 1
/
fashion_train_bayes_models.py
143 lines (101 loc) · 4.99 KB
/
fashion_train_bayes_models.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
import sys
import os
from tqdm import tqdm
from torchvision import transforms, datasets
import torch
import torch.nn as nn
from simulationmodel import BayesFFNN, BayesLinear
from sampling_utils import *
from fast_mnist import *
# Create FFNN models with the given dimensions
input_size = 28 * 28
num_hidden = 3
output_size = 10
epochs = 50
p_list = [500, 1000, 2000]
device = 'cuda'
# Loading / Dowloading MNIST data
#train = datasets.MNIST("../", train=True, download=True, transform=transforms.Compose([transforms.ToTensor()]))
#trainset = torch.utils.data.DataLoader(train, batch_size=100, shuffle=True)
#test = datasets.MNIST("../", train=False, download=True, transform=transforms.Compose([transforms.ToTensor()]))
#testset = torch.utils.data.DataLoader(test, batch_size=100, shuffle=True)
train = FastFashionMNIST("", train=True, download=True, device=device)
trainset = torch.utils.data.DataLoader(train, batch_size=100, shuffle=True)
test = FastFashionMNIST("", train=False, download=True, device=device)
testset = torch.utils.data.DataLoader(test, batch_size=100)
path = 'trained_models/fashion_results_h_3_corrected'
if not os.path.isdir(path):
os.makedirs(path)
for r in range(1, 6):
for p in p_list:
normal_ffnn = BayesFFNN(input_size, num_hidden, p, output_size)
iid_rvs = lam_dist(p, 'iid')
# this initialises the weights of the nn
normal_ffnn.init_weights(iid_rvs, kappa=1, sigma_b=1)
horseshoe_ffnn = BayesFFNN(input_size, num_hidden, p, output_size)
horseshoe_rvs = lam_dist(p, 'horseshoe')
# this initialises the weights of the nn
horseshoe_ffnn.init_weights(iid_rvs, kappa=1, sigma_b=1)
horseshoe_ffnn.set_prior(horseshoe_rvs)
gbfry_light_ffnn = BayesFFNN(input_size, num_hidden, p, output_size)
light_rvs = GBFRYInit(alpha=0.2, tau=5)
# this initialises the weights of the nn
gbfry_light_ffnn.init_weights(iid_rvs, kappa=1, sigma_b=1)
gbfry_light_ffnn.set_prior(light_rvs)
gbfry_mid_ffnn = BayesFFNN(input_size, num_hidden, p, output_size)
mid_rvs = GBFRYInit(alpha=0.5, tau=5)
# this initialises the weights of the nn
gbfry_mid_ffnn.init_weights(iid_rvs, kappa=1, sigma_b=1)
gbfry_mid_ffnn.set_prior(mid_rvs)
gbfry_heavy_ffnn = BayesFFNN(input_size, num_hidden, p, output_size)
heavy_rvs = GBFRYInit(alpha=0.8, tau=5)
# this initialises the weights of the nn
gbfry_heavy_ffnn.init_weights(iid_rvs, kappa=1, sigma_b=1)
gbfry_heavy_ffnn.set_prior(heavy_rvs)
# Train the models
def train_model(simple_net, epochs, trainset, testset):
optimizer = torch.optim.Adam(params=simple_net.parameters(), lr=1e-2)
simple_net.to(device)
simple_net.double()
for e in tqdm(range(epochs)):
for g in optimizer.param_groups:
if e < 10:
g['lr'] = 1e-2
elif e < 20:
g['lr'] = 5e-3
else:
g['lr'] = 1e-3
simple_net.train()
for X, y in trainset:
X = X.to(device)
y = y.to(device)
simple_net.zero_grad()
pred_dist = simple_net(X.view(-1, 28 * 28))
l = nn.CrossEntropyLoss(reduction="mean")
log_prior_term = simple_net.log_prior()/60000/5
loss = l(pred_dist, y)-log_prior_term
loss.backward()
optimizer.step()
print("Training loss = ", loss)
simple_net.eval()
accurate = 0
total = 0
with torch.no_grad():
for X, y in testset:
X = X.to(device)
y = y.to(device)
pred_dist = simple_net(X.view(-1, 28 * 28))
pred_y = torch.argmax(pred_dist, dim=-1)
accurate += torch.sum(pred_y == y).to('cpu').numpy()
total += y.shape[0]
print("Accuracy on test set: ", round(accurate / total * 100, 1))
#train_model(normal_ffnn, epochs, trainset, testset)
#torch.save(normal_ffnn, '{}/normal_ffnn_{}_run_{}.net'.format(path, p, r))
#train_model(gbfry_light_ffnn, epochs, trainset, testset)
#torch.save(gbfry_light_ffnn, '{}/gbfry_light_ffnn_{}_run_{}.net'.format(path, p, r))
train_model(horseshoe_ffnn, epochs, trainset, testset)
torch.save(horseshoe_ffnn, '{}/horseshoe2_ffnn_{}_run_{}.net'.format(path, p, r))
#train_model(gbfry_mid_ffnn, epochs, trainset, testset)
#torch.save(gbfry_mid_ffnn, '{}/gbfry_mid_ffnn_{}_run_{}.net'.format(path, p, r))
#train_model(gbfry_heavy_ffnn, epochs, trainset, testset)
#torch.save(gbfry_heavy_ffnn, '{}/gbfry_heavy_ffnn_{}_run_{}.net'.format(path, p, r))