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train.py
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train.py
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import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms, models
from collections import OrderedDict
import argparse
import os
parser = argparse.ArgumentParser(description='Image Classifier Train')
parser.add_argument("data_directory", nargs="*", default=["flowers"], help="A directory containing a train and test folders with data for the nn")
parser.add_argument('--save-dir', dest="save_dir", default="checkpoints", help="The folder that will store the checkpoints")
parser.add_argument('--arch', dest="arch", default="vgg16", help="Any architecture available in torchvision models")
parser.add_argument('--learning_rate', dest="learning_rate", type=float, default="0.001")
parser.add_argument('--hidden_units', type=int, default="4096")
parser.add_argument('--epochs', dest="epochs", type=int, default="5")
parser.add_argument('--gpu', action='store_true', default=False, dest='gpu', help="If set, the gpu will be used instead of the cpu")
args = parser.parse_args()
data_dir = args.data_directory[0]
data_transforms = {
"train": transforms.Compose([transforms.RandomRotation(30),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])]),
"test": transforms.Compose([transforms.Resize(255),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
}
image_datasets = {
"train": datasets.ImageFolder(data_dir + '/train', transform=data_transforms["train"]),
"test": datasets.ImageFolder(data_dir + '/test', transform=data_transforms["test"]),
}
dataloaders = {
"train": torch.utils.data.DataLoader(image_datasets["train"], batch_size=64, shuffle=True),
"test": torch.utils.data.DataLoader(image_datasets["test"], batch_size=64)
}
model = getattr(models, args.arch)
model = model(pretrained=True)
device = torch.device("cuda" if args.gpu else "cpu")
for param in model.parameters():
param.requires_grad = False
classifier = nn.Sequential(
nn.Dropout(0.2),
nn.Linear(25088, args.hidden_units),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(args.hidden_units, 102),
nn.LogSoftmax(dim=1))
model.classifier = classifier
model.to(device)
criterion = nn.NLLLoss()
optimizer = optim.Adam(model.classifier.parameters(), lr=args.learning_rate)
epochs = int(args.epochs)
train_losses, test_losses = [], []
for e in range(epochs):
print("Starting epoch {}".format(e + 1))
running_loss = 0
for inputs, labels in dataloaders["train"]:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
log_ps = model(inputs)
loss = criterion(log_ps, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
else:
test_loss = 0
accuracy = 0
model.eval()
# Turn off gradients for validation, saves memory and computations
with torch.no_grad():
for inputs, labels in dataloaders["test"]:
inputs, labels = inputs.to(device), labels.to(device)
log_ps = model(inputs)
test_loss += criterion(log_ps, labels).item()
ps = torch.exp(log_ps)
top_p, top_class = ps.topk(1, dim=1)
equals = top_class == labels.view(*top_class.shape)
accuracy += torch.mean(equals.type(torch.FloatTensor)).item()
train_losses.append(running_loss/len(dataloaders["train"]))
test_losses.append(test_loss/len(dataloaders["test"]))
print("Epoch: {}/{}.. ".format(e+1, epochs),
"Training Loss: {:.3f}.. ".format(running_loss/len(dataloaders["train"])),
"Test Loss: {:.3f}.. ".format(test_loss/len(dataloaders["test"])),
"Test Accuracy: {:.3f}".format(accuracy/len(dataloaders["test"])))
model.train()
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
checkpoint = {'input_size': 25088,
'output_size': 102,
'hidden_layer_size': args.hidden_units,
'learning_rate': args.learning_rate,
'dropout': 0.2,
'epochs': args.epochs,
'state_dict': model.state_dict(),
'classes_list': image_datasets['train'].classes
}
torch.save(checkpoint, args.save_dir + '/imageclassifiercheckpoint.pth')