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study.py
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study.py
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import argparse
import tensorflow as tf
import datasetops as do
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from models import *
from utils import *
def train(args, model, device, train_loader, optimizer, epoch):
model.train()
losses = []
correct = 0
total = 0
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
pred = output.max(1)[1]
loss = F.nll_loss(output, target)
losses.append(loss)
loss.backward()
optimizer.step()
correct += pred.eq(target).sum().item()
total += target.size(0)
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()))
if args.dry_run:
break
acc = 100. * correct / total
avg_loss = sum(losses) / len(train_loader)
print(acc)
print(avg_loss)
return acc, avg_loss
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
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.argmax(dim=1, keepdim=True) # 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)))
return (100. * correct / len(test_loader.dataset)), test_loss
def main():
parser = argparse.ArgumentParser(description='NNs for USPS Handwritten Dataset')
parser.add_argument('--net', type=int, default=1, metavar='N',
help='choice of NN to use (default: 1): \
\n\t1 - Fully Connected Network \
\n\t2 - Locally Connected Network \
\n\t3 - Convolutional Network')
parser.add_argument('--init', type=int, default=1, metavar='N',
help='choice of weight initialization (default: 1): \
\n\t1 - Effective Learning \
\n\t2 - Too fast \
\n\t3 - Too slow \
\n\t4 - Default of model')
parser.add_argument('--batch-size', type=int, default=32, metavar='N',
help='input batch size for training (default: 32)')
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=10, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=5e-2, metavar='LR',
help='learning rate (default: 5e-2)')
parser.add_argument('--dropout', type=float, default=0.0, metavar='D',
help='dropout (default: 0)')
parser.add_argument('--gamma', type=float, default=0.85, metavar='G',
help='Learning rate step gamma (default: 0.85)')
parser.add_argument('--momentum', type=float, default=0.0, metavar='M',
help='momentum (default: 0.0)')
parser.add_argument('--viz-filters', action='store_true', default=False,
help='visualize maps each layer in MNIST CNN')
parser.add_argument('--viz-acts', action='store_true', default=False,
help='visualize 3rd layer activations for 0 and 8 in MNIST CNN')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--dry-run', action='store_true', default=False,
help='quickly check a single pass')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=25, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model', action='store_true', default=False,
help='For Saving the current Model')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
train_kwargs = {'batch_size': args.batch_size}
test_kwargs = {'batch_size': args.test_batch_size}
if use_cuda:
cuda_kwargs = {'num_workers': 1,
'pin_memory': True,
'shuffle': True}
train_kwargs.update(cuda_kwargs)
test_kwargs.update(cuda_kwargs)
data_train, train_pixels = buildUSPSDataset('data/zip_train.txt', args.net)
data_test, test_pixels = buildUSPSDataset('data/zip_test.txt', args.net)
modelChoice = {'1': FCNet, '3': ConvNet}
if args.net != 2:
model = modelChoice[str(args.net)](args).to(device)
optim = torch.optim.SGD(model.parameters(), lr=args.lr)
count_parameters(model)
train_loader = DataLoader(dataset=data_train, shuffle=True, **train_kwargs)
test_loader = DataLoader(dataset=data_test, shuffle=False, **test_kwargs)
writer = SummaryWriter('runs/'
+ model._get_name()
+ '/train'
+ '/init' + str(args.init)
+ '/lr' + format(args.lr, '.1e').replace('0','').replace('.',''))
writer2 = SummaryWriter('runs/'
+ model._get_name()
+ '/test'
+ '/init' + str(args.init)
+ '/lr' + format(args.lr, '.1e').replace('0','').replace('.',''))
scheduler = StepLR(optim, step_size=1)
for epoch in range(1, args.epochs + 1):
train_acc, train_loss = train(args, model, device, train_loader, optim, epoch)
test_acc, test_loss = test(model, device, test_loader)
scheduler.step()
writer.add_scalar('loss', train_loss, epoch)
writer2.add_scalar('loss', test_loss, epoch)
writer.add_scalar('accuracy', train_acc, epoch)
writer2.add_scalar('accuracy', test_acc, epoch)
if args.save_model:
torch.save(model.state_dict(),
model._get_name()
+'_init' + str(args.init)
+ '_lr' + format(args.lr, '.1e').replace('0','').replace('.',''))
else:
model = LCNet(args)
model.summary()
model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=args.lr),
loss=tf.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
data_train = do.from_pytorch(data_train).to_tensorflow().batch(args.batch_size)
data_test = do.from_pytorch(data_test).to_tensorflow().batch(args.test_batch_size)
model.fit(data_train, epochs=args.epochs, validation_data=data_test)
model.evaluate(data_test)
if __name__ == '__main__':
main()