File size: 2,523 Bytes
0768d0a |
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 |
# -*- coding: utf-8 -*-
"""main.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/#fileId=https%3A//huggingface.co/spaces/neelimapreeti297/panda_cat_dog_classification/blob/main/main.ipynb
"""
#import libraries
import torch
from torchvision import datasets, transforms
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
#define the data transforms
transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor(),
transforms.Normalize((0.485,0.456,0.406),(0.229,0.224,0.225))
])
#insert the datasets
train_dataset = ImageFolder('./data/train', transform=transform)
test_dataset =ImageFolder('./data/test', transform=transform)
# make cnn model
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(16 * 53 * 53, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 3)
def forward(self, x):
x = self.conv1(x)
x = self.pool(x)
x = self.conv2(x)
x = self.pool(x)
x = x.view(-1, 16 * 53 * 53)
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
return x
batch_size = 8
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
model = CNN()
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
#Train the model
for epoch in range(10):
for i, (images, labels) in enumerate(train_loader):
outputs = model(images)
loss = loss_function(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i % 200 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, 10, i + 1, len(train_loader), loss.item()))
#iterate over the test data
correct = 0
total = 0
for i, (images, labels) in enumerate(test_loader):
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
correct += (predicted == labels).sum().item()
total += labels.size(0)
#calculate the accuracy
accuracy = 100 * correct / total
print('Accuracy: {}%' .format(accuracy))
model_scripted = torch.jit.script(model)
model_scripted.save('./models/cat_dog_cnn.pt')
|