I am implementing my own image classifier network. However I see for each epoch training accuracy is very good but validation accuracy is 0 till 5th epoch. I am using Adam optimizer and have learning rate .001. also resampling the whole data set after each epoch into training n validation set. Where am I going wrong? Here is my code: ### where is data? data_dir_train = '/home/sup/PycharmProjects/deep_learning/CNN_Data/training_set' data_dir_test = '/home/sup/PycharmProjects/deep_learning/CNN_Data/test_set' # Define your batch_size batch_size = 64 allData = datasets.ImageFolder(root=data_dir_train,transform=transformArr) # We need to further split our training dataset into training and validation sets. def split_train_validation(): # Define the indices num_train = len(allData) indices = list(range(num_train)) # start with all the indices in training set split = int(np.floor(0.2 * num_train)) # define the split size #train_idx, valid_idx = indices[split:], indices[:split] # Random, non-contiguous split validation_idx = np.random.choice(indices, size=split, replace=False) train_idx = list(set(indices) - set(validation_idx)) # define our samplers -- we use a SubsetRandomSampler because it will return # a random subset of the split defined by the given indices without replacement train_sampler = SubsetRandomSampler(train_idx) validation_sampler = SubsetRandomSampler(validation_idx) #train_loader = DataLoader(allData,batch_size=batch_size,sampler=train_sampler,shuffle=False,num_workers=4) #validation_loader = DataLoader(dataset=allData,batch_size=1, sampler=validation_sampler) return (train_sampler,validation_sampler) Training: from torch.optim import Adam import torch import createNN import torch.nn as nn import loadData as ld from torch.autograd import Variable from torch.utils.data import DataLoader # check if cuda - GPU support available cuda = torch.cuda.is_available() #create model, optimizer and loss function model = createNN.ConvNet(class_num=2) optimizer = Adam(model.parameters(),lr=.001,weight_decay=.0001) loss_func = nn.CrossEntropyLoss() if cuda: model.cuda() # function to save model def save_model(epoch): torch.save(model.load_state_dict(),'imageClassifier_{}.model'.format(epoch)) print('saved model at epoch',epoch) def exp_lr_scheduler ( epoch , init_lr = args.lr, weight_decay = args.weight_decay, lr_decay_epoch = cf.lr_decay_epoch): lr = init_lr * ( 0.5 ** (epoch // lr_decay_epoch)) def train(num_epochs): best_acc = 0.0 for epoch in range(num_epochs): print('\n\nEpoch {}'.format(epoch)) train_sampler, validation_sampler = ld.split_train_validation() train_loader = DataLoader(ld.allData, batch_size=30, sampler=train_sampler, shuffle=False) validation_loader = DataLoader(dataset=ld.allData, batch_size=1, sampler=validation_sampler) model.train() acc = 0.0 loss = 0.0 total = 0 # train model with training data for i,(images,labels) in enumerate(train_loader): # if cuda then move to GPU if cuda: images = images.cuda() labels = labels.cuda() # Variable class wraps a tensor and we can calculate grad images = Variable(images) labels = Variable(labels) # reset accumulated gradients for each batch optimizer.zero_grad() # pass images to model which returns preiction output = model(images) #calculate the loss based on prediction and actual loss = loss_func(output,labels) # backpropagate the loss and compute gradient loss.backward() # update weights as per the computed gradients optimizer.step() # prediction class predVal , predClass = torch.max(output.data, 1) acc += torch.sum(predClass == labels.data) loss += loss.cpu().data[0] total += labels.size(0) # print the statistics train_acc = acc/total train_loss = loss / total print('Mean train acc = {} over epoch = {}'.format(epoch,acc)) print('Mean train loss = {} over epoch = {}'.format(epoch, loss)) # Valid model with validataion data model.eval() acc = 0.0 loss = 0.0 total = 0 for i,(images,labels) in enumerate(validation_loader): # if cuda then move to GPU if cuda: images = images.cuda() labels = labels.cuda() # Variable class wraps a tensor and we can calculate grad images = Variable(images) labels = Variable(labels) # reset accumulated gradients for each batch optimizer.zero_grad() # pass images to model which returns preiction output = model(images) #calculate the loss based on prediction and actual loss = loss_func(output,labels) # backpropagate the loss and compute gradient loss.backward() # update weights as per the computed gradients optimizer.step() # prediction class predVal, predClass = torch.max(output.data, 1) acc += torch.sum(predClass == labels.data) loss += loss.cpu().data[0] total += labels.size(0) # print the statistics valid_acc = acc / total valid_loss = loss / total print('Mean train acc = {} over epoch = {}'.format(epoch, valid_acc)) print('Mean train loss = {} over epoch = {}'.format(epoch, valid_loss)) if(best_acc<valid_acc): best_acc = valid_acc save_model(epoch) # at 30th epoch we save the model if (epoch == 30): save_model(epoch) train(20) Continue reading...
