-
Notifications
You must be signed in to change notification settings - Fork 0
/
torch_bidirectional_LSTM.py
100 lines (81 loc) · 3.23 KB
/
torch_bidirectional_LSTM.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
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# Using MNIST dataset and interpreting the data as having 28 sequences and each sequence having 28 features.
# Hyperparameters
input_size = 28
sequence_length = 28
num_layers = 2
hidden_size = 256
num_classes = 10
learning_rate = 0.001
batch_size = 64
num_epochs = 3
# input_size => The number of expected features in the input x.
# hidden_size => The number of features in the hidden state a.
# In LSTM => a<t> != c<t>
class BRNN(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(BRNN, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)
self.fc = nn.Linear(hidden_size*2, num_classes)
def forward(self,x):
h0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size)
c0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size)
out, _ = self.lstm(x, (h0,c0))
out = self.fc(out[:, -1, :])
return out
# Load data
train_dataset = datasets.MNIST(
root='dataset/', train=True, transform=transforms.ToTensor(), download=True)
train_loader = DataLoader(dataset=train_dataset,
batch_size=batch_size, shuffle=True)
test_dataset = datasets.MNIST(
root='dataset/', train=False, transform=transforms.ToTensor(), download=True)
test_loader = DataLoader(dataset=test_dataset,
batch_size=batch_size, shuffle=True)
# Initialize Network
model = BRNN(input_size, hidden_size, num_layers, num_classes)
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# Train Network
for epoch in range(num_epochs):
for (data, targets) in train_loader:
data = data.squeeze(1) # The data from which we need to predict thing
targets = targets # The target value
# forward part
scores = model(data) # Prediction of the model
# Loss, that is cross entropy loss which is calculated given two args: 'predicted value' &'target value'
loss = criterion(scores, targets)
# Backward part
optimizer.zero_grad() # Setting the optimized GD to zero
loss.backward()
# Gradient descent or adam step
optimizer.step()
# Checking the model accuracy:
def check_accuracy(loader, model):
if loader.dataset.train:
print('Checking accuracy on training data')
else:
print('Checking accuracy on test data')
num_correct = 0
num_samples = 0
model.eval()
with torch.no_grad():
for x, y in loader:
x = x.squeeze(1)
scores = model(x)
_, predictions = scores.max(1)
num_correct += (predictions == y).sum()
num_samples += predictions.size(0)
print(
f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}')
check_accuracy(train_loader, model)
check_accuracy(test_loader, model)