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train.py
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train.py
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import argparse
import torch
from torch import nn
from torch import optim
from torch.autograd import Variable
from torchvision import datasets, transforms, models
from torchvision.datasets import ImageFolder
import torch.nn.functional as F
from PIL import Image
from collections import OrderedDict
import time
import numpy as np
import matplotlib.pyplot as plt
from utils import save_checkpoint, load_checkpoint
def parse_args():
parser=argparse.ArgumentParser(description="Training")
parser.add_argument('--data_dir',action='store')
parser.add_argument('--arch',dest='arch',default='densenet121',choices=['vgg13','densenet121'])
parser.add_argument('--learning_rate',dest='learning_rate',default='0.001')
parser.add_argument('--hidden_units',dest='hidden_units',default='512')
parser.add_argument('--epochs',dest='epochs',default='3')
parser.add_argument('--gpu',action='store',default='gpu')
parser.add_argument('--save_dir' ,dest="save_dir",action="store",default="checkpoint.pth")
return parser.parse_args()
def train(model,criterion,optimizer,dataloaders,epochs,gpu):
steps=0
print_every=10
for e in range(epochs):
running_loss = 0
for ii, (inputs, labels) in enumerate(dataloaders[0]): # 0 = train
steps += 1
#if torch.cuda.is_available(): # testing this out, uncomment later
# model.cuda()
if gpu == 'gpu':
model.cuda()
inputs, labels = inputs.to('cuda'), labels.to('cuda') # use cuda
else:
model.cpu() # use a CPU if user says anything other than "gpu"
#inputs, labels = inputs.to('cuda'), labels.to('cuda') # use cuda # uncomment later
optimizer.zero_grad()
# Forward and backward passes
outputs = model.forward(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if steps % print_every == 0:
model.eval()
valloss = 0
accuracy=0
for ii, (inputs2,labels2) in enumerate(dataloaders[1]): # 1 = validation
optimizer.zero_grad()
if gpu == 'gpu':
inputs2, labels2 = inputs2.to('cuda') , labels2.to('cuda') # use cuda
model.to('cuda:0') # use cuda
else:
with torch.no_grad():
outputs = model.forward(inputs2)
valloss = criterion(outputs,labels2)
ps = torch.exp(outputs).data
equality = (labels2.data == ps.max(1)[1])
accuracy += equality.type_as(torch.FloatTensor()).mean()
valloss = valloss / len(dataloaders[1])
accuracy = accuracy /len(dataloaders[1])
print("Epoch: {}/{}... ".format(e+1, epochs),
"Training Loss: {:.4f}".format(running_loss/print_every),
"Validation Loss {:.4f}".format(valloss),
"Accuracy: {:.4f}".format(accuracy),
)
running_loss = 0
def main():
print("hello") # just to make sure I can see something before it takes forever to trian
args = parse_args()
data_dir = 'flowers'
train_dir = data_dir + '/train'
val_dir = data_dir + '/valid'
test_dir = data_dir + '/test'
training_transforms = 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])])
validataion_transforms = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])]) # the spacing on this looks off but it is just the workspace, do not pay it any mind
testing_transforms = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
image_datasets = [ImageFolder(train_dir, transform=training_transforms),
ImageFolder(val_dir, transform=validataion_transforms),
ImageFolder(test_dir, transform=testing_transforms)]
dataloaders = [torch.utils.data.DataLoader(image_datasets[0], batch_size=64, shuffle=True),
torch.utils.data.DataLoader(image_datasets[1], batch_size=64, shuffle=True),
torch.utils.data.DataLoader(image_datasets[2], batch_size=64, shuffle=True)]
model = getattr(models, args.arch)(pretrained=True)
for param in model.parameters():
param.requires_grad = False
if args.arch == "vgg13":
feature_num = model.classifier[0].in_features
classifier = nn.Sequential(OrderedDict([
('fc1', nn.Linear(feature_num, 1024)),
('drop', nn.Dropout(p=0.5)),
('relu', nn.ReLU()),
('fc2', nn.Linear(1024, 102)),
('output', nn.LogSoftmax(dim=1))]))
elif args.arch == "densenet121":
classifier = nn.Sequential(OrderedDict([
('fc1', nn.Linear(1024, 500)),
('drop', nn.Dropout(p=0.6)),
('relu', nn.ReLU()),
('fc2', nn.Linear(500, 102)),
('output', nn.LogSoftmax(dim=1))]))
model.classifier = classifier
criterion = nn.NLLLoss() # using criterion and optimizer similar to pytorch lectures (densenet)
optimizer = optim.Adam(model.classifier.parameters(), lr=float(args.learning_rate))
epochs = int(args.epochs)
class_index = image_datasets[0].class_to_idx
gpu = args.gpu # get the gpu settings
train(model, criterion, optimizer, dataloaders, epochs, gpu)
model.class_to_idx = class_index
path = args.save_dir # get the new save location
save_checkpoint(path, model, optimizer, args, classifier)
if __name__ == "__main__":
main()