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MNIST Binary Classifier
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from __future__ import print_function | |
import argparse | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.optim as optim | |
from torchvision import datasets, transforms | |
class SamNet(nn.Module): | |
def __init__(self): | |
super(SamNet, self).__init__() | |
# 1 input image, 10 output channels, 5x5 square convolution kernel | |
self.conv1 = nn.Conv2d(1, 10, kernel_size=5) | |
# 10 input channels, 10 output channels, 5x5 square convolution kernel | |
self.conv2 = nn.Conv2d(10, 10, kernel_size=5) | |
self.conv2_drop = nn.Dropout2d() | |
# Only need 2 neurons for output | |
self.fc1 = nn.Linear(160, 2) | |
def forward(self, x): | |
""" | |
You just have to define the forward function, and the backward function | |
(where gradients are computed) is automatically defined for you using | |
autograd. You can use any of the Tensor operations in the | |
forward function. | |
""" | |
# Max pooling over a 2x2 window | |
x = F.relu(F.max_pool2d(self.conv1(x), 2)) | |
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) | |
x = x.view(-1, 160) | |
x = self.fc1(x) | |
return F.log_softmax(x, dim=1) | |
class AlexNet(nn.Module): | |
def __init__(self): | |
super(AlexNet, self).__init__() | |
self.conv1 = nn.Conv2d(1, 10, kernel_size=5) | |
self.conv2 = nn.Conv2d(10, 20, kernel_size=5) | |
self.conv2_drop = nn.Dropout2d() | |
self.fc1 = nn.Linear(320, 50) | |
self.fc2 = nn.Linear(50, 10) | |
def forward(self, x): | |
x = F.relu(F.max_pool2d(self.conv1(x), 2)) | |
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) | |
x = x.view(-1, 320) | |
x = F.relu(self.fc1(x)) | |
x = F.dropout(x, training=self.training) | |
x = self.fc2(x) | |
return F.log_softmax(x, dim=1) | |
def train(args, model, device, train_loader, epoch): | |
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) | |
model.train() | |
for batch_idx, (data, target) in enumerate(train_loader): | |
for ind, y_val in enumerate(target): | |
target[ind] = 0 if y_val < 5 else 1 | |
data, target = data.to(device), target.to(device) | |
optimizer.zero_grad() | |
output = model(data) | |
# nll_loss = negative log likelihood loss | |
# output = tensor of N x C x H x W in this case of 2D loss | |
# target = tensor of ground truth | |
loss = F.nll_loss(output, target) | |
loss.backward() | |
optimizer.step() | |
if batch_idx % args.log_interval == 0: | |
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( | |
epoch, batch_idx * len(data), len(train_loader.dataset), | |
100. * batch_idx / len(train_loader), loss.item())) | |
def test(args, model, device, test_loader): | |
model.eval() | |
test_loss = 0 | |
correct = 0 | |
with torch.no_grad(): | |
for data, target in test_loader: | |
for ind, y_val in enumerate(target): | |
target[ind] = 0 if y_val < 5 else 1 | |
data, target = data.to(device), target.to(device) | |
output = model(data) | |
test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss | |
pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability | |
correct += pred.eq(target.view_as(pred)).sum().item() | |
test_loss /= len(test_loader.dataset) | |
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( | |
test_loss, correct, len(test_loader.dataset), | |
100. * correct / len(test_loader.dataset))) | |
def load_data(args, use_cuda): | |
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {} | |
train_loader = torch.utils.data.DataLoader( | |
datasets.MNIST('../data', train=True, download=True, | |
transform=transforms.Compose([ | |
transforms.ToTensor(), | |
transforms.Normalize((0.1307,), (0.3081,)) | |
])), | |
batch_size=args.batch_size, shuffle=True, **kwargs) | |
test_loader = torch.utils.data.DataLoader( | |
datasets.MNIST('../data', train=False, transform=transforms.Compose([ | |
transforms.ToTensor(), | |
transforms.Normalize((0.1307,), (0.3081,)) | |
])), | |
batch_size=args.test_batch_size, shuffle=True, **kwargs) | |
return train_loader, test_loader | |
def parse_args(): | |
# Training settings | |
parser = argparse.ArgumentParser(description='PyTorch MNIST Example') | |
parser.add_argument('--batch-size', type=int, default=64, metavar='N', | |
help='input batch size for training (default: 64)') | |
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', | |
help='input batch size for testing (default: 1000)') | |
parser.add_argument('--epochs', type=int, default=3, metavar='N', | |
help='number of epochs to train (default: 10)') | |
parser.add_argument('--lr', type=float, default=0.01, metavar='LR', | |
help='learning rate (default: 0.01)') | |
parser.add_argument('--momentum', type=float, default=0.5, metavar='M', | |
help='SGD momentum (default: 0.5)') | |
parser.add_argument('--seed', type=int, default=1, metavar='S', | |
help='random seed (default: 1)') | |
parser.add_argument('--log-interval', type=int, default=10, metavar='N', | |
help='how many batches to wait before logging training status') | |
return parser.parse_args() | |
def main(): | |
args = parse_args() | |
use_cuda = torch.cuda.is_available() | |
device = torch.device("cuda" if use_cuda else "cpu") | |
train_loader, test_loader = load_data(args, use_cuda) | |
torch.manual_seed(args.seed) | |
model = SamNet().to(device) | |
# model = AlexNet().to(device) | |
for epoch in range(1, args.epochs + 1): | |
train(args, model, device, train_loader, epoch) | |
test(args, model, device, test_loader) | |
if __name__ == '__main__': | |
main() |
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Hi @sordonia120446, thank you for sharing your code!
I want to learn from it. Could you please show me how to apply your code to the "Connectionist Bench (Sonar, Mines vs. Rocks) Data Set"?
https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)
Thank you!