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Why I can't overfit a single batch (the loss keeps oscilating)?

I am training simple feedforward neural network for regression task and I am just trying to overfit single batch of 32 examples (with 9 features) to see if the implementation is ok, however, the loss keeps oscilating no matter the learning rate and hidden size I try, it looks like this:
The data is standard scaled. The network is just having one hidden layer with ReLU:
BATCH_SIZE = 32
LR = 0.0001
NUM_EPOCHS = 100
HIDDEN_SIZE = 512
class FullyConnected(nn.Module):
def __init__(self, hidden_size=HIDDEN_SIZE):
super().__init__()
self.fc1 = nn.Linear(in_features=in_features, out_features=hidden_size)
self.fc2 = nn.Linear(in_features=hidden_size, out_features=1)
def forward(self, x):
out = self.fc1(x)
out = F.relu(out)
out = self.fc2(out)
return out
And the training loop is the usual one, except the fact that I am just overfitting for now a single batch:
model = FullyConnected().to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=LR)
model.train()
X, y = next(iter(train_dataloader))
for epoch in range(NUM_EPOCHS):
y_pred = model(X)
loss = criterion(y_pred, y)
loss.backward()
optimizer.step()
optimizer.zero_grad()
Why I can't overfit a single batch and the loss is oscilating in a weird way despite the learning rate being small?

MNIST overfitting

I am currently working on the MNIST dataset. My model has overfit the training data and I want to reduce the overfitting by using weight_decay. I am currently using 0.1 as the value for weight_decay which is giving me bad results as my validation loss and training loss are not decreasing. However, I want to experiment with different values for weight_decay. So that i can plot the different amounts of weight_decay on the x-axis and the performance of validation set on the y-axis. How do i do that? store the values in a list and use a for loop to iterate through? Below is the code that i have tried until now.
class NN(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.Sequential(
nn.Flatten(),
nn.Linear(784,4096),
nn.ReLU(),
nn.Linear(4096,2048),
nn.ReLU(),
nn.Linear(2048,1024),
nn.ReLU(),
nn.Linear(1024,512),
nn.ReLU(),
nn.Linear(512,256),
nn.ReLU(),
nn.Linear(256,128),
nn.ReLU(),
nn.Linear(128,64),
nn.ReLU(),
nn.Linear(64,32),
nn.ReLU(),
nn.Linear(32,16),
nn.ReLU(),
nn.Linear(16,10))
def forward(self,x):
return self.layers(x)
def accuracy_and_loss(model, loss_function, dataloader):
total_correct = 0
total_loss = 0
total_examples = 0
n_batches = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = model(images)
batch_loss = loss_function(outputs,labels)
n_batches += 1
total_loss += batch_loss.item()
_, predicted = torch.max(outputs, dim=1)
total_examples += labels.size(0)
total_correct += (predicted == labels).sum().item()
accuracy = total_correct / total_examples
mean_loss = total_loss / n_batches
return (accuracy, mean_loss)
def define_and_train(model,dataset_training, dataset_test):
trainloader = torch.utils.data.DataLoader( small_trainset, batch_size=500, shuffle=True)
testloader = torch.utils.data.DataLoader( dataset_test, batch_size=500, shuffle=True)
values = [1e-8,1e-7,1e-6,1e-5]
model = NN()
for params in values:
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay = params)
train_acc = []
val_acc = []
train_loss = []
val_loss = []
for epoch in range(100):
total_loss = 0
total_correct = 0
total_examples = 0
n_mini_batches = 0
for i,mini_batch in enumerate(trainloader,0):
images,labels = mini_batch
optimizer.zero_grad()
outputs = model(images)
loss = loss_function(outputs,labels)
loss.backward()
optimizer.step()
n_mini_batches += 1
total_loss += loss.item()
_, predicted = torch.max(outputs, dim=1)
total_examples += labels.size(0)
total_correct += (predicted == labels).sum().item()
epoch_training_accuracy = total_correct / total_examples
epoch_training_loss = total_loss / n_mini_batches
epoch_val_accuracy, epoch_val_loss = accuracy_and_loss( model, loss_function, testloader )
print('Params %f Epoch %d loss: %.3f acc: %.3f val_loss: %.3f val_acc: %.3f'
%(params, epoch+1, epoch_training_loss, epoch_training_accuracy, epoch_val_loss, epoch_val_accuracy))
train_loss.append( epoch_training_loss )
train_acc.append( epoch_training_accuracy )
val_loss.append( epoch_val_loss )
val_acc.append( epoch_val_accuracy )
history = { 'train_loss': train_loss,
'train_acc': train_acc,
'val_loss': val_loss,
'val_acc': val_acc }
return ( history, model )
This is the plot that I am getting. Where am I going wrong?

I cannot know any information. (Such as loss function, dataset size, dataset content (training and validation), results of 100 or 200 epochs, your scope of the question)
However, the overfitted model may classify the validation dataset. Because the MNIST dataset is not that hard with deep learning (compared to other image classifications).
How about adding white noise to the validation dataset? You may get a large loss on validation.
Or if you want to use your validation dataset, train the model for more at least 1000 epochs. But, as I said above, the overfitted model may classify the validation dataset.

Validation losses increasing after a few epochs

I'm building a small CNN model to predict plant crop disease with the Plant Village Dataset. It consists of 39 classes of different species with and without diseases.
CNN model
class CropDetectCNN(nn.Module):
# initialize the class and the parameters
def __init__(self):
super(CropDetectCNN, self).__init__()
# convolutional layer 1 & max pool layer 1
self.layer1 = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3),
nn.MaxPool2d(kernel_size=2))
# convolutional layer 2 & max pool layer 2
self.layer2 = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=3, padding=1, stride=2),
nn.MaxPool2d(kernel_size=2))
#Fully connected layer
self.fc = nn.Linear(32*28*28, 39)
# Feed forward the network
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.reshape(out.size(0), -1)
out = self.fc(out)
return out
model = CropDetectCNN()
Training
criterion = nn.CrossEntropyLoss() # this include softmax + cross entropy loss
optimizer = torch.optim.Adam(model.parameters())
def batch_gd(model, criterion, train_loader, validation_loader, epochs):
train_losses = np.zeros(epochs)
test_losses = np.zeros(epochs)
validation_losses = np.zeros(epochs)
for e in range(epochs):
t0 = datetime.now()
train_loss = []
model.train()
for inputs, targets in train_loader:
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
output = model(inputs)
loss = criterion(output, targets)
train_loss.append(loss.item()) # torch to numpy world
loss.backward()
optimizer.step()
train_loss = np.mean(train_loss)
validation_loss = []
for inputs, targets in validation_loader:
model.eval()
inputs, targets = inputs.to(device), targets.to(device)
output = model(inputs)
loss = criterion(output, targets)
validation_loss.append(loss.item()) # torch to numpy world
validation_loss = np.mean(validation_loss)
train_losses[e] = train_loss
validation_losses[e] = validation_loss
dt = datetime.now() - t0
print(
f"Epoch : {e+1}/{epochs} Train_loss: {train_loss:.3f} Validation_loss: {validation_loss:.3f} Duration: {dt}"
)
return train_losses, validation_losses
# Running the function
train_losses, validation_losses = batch_gd(
model, criterion, train_loader, validation_loader, 5
)
# And theses are results:
Epoch : 1/5 Train_loss: 1.164 Validation_loss: 0.861 Duration: 0:10:59.968168
Epoch : 2/5 Train_loss: 0.515 Validation_loss: 0.816 Duration: 0:10:49.199842
Epoch : 3/5 Train_loss: 0.241 Validation_loss: 1.007 Duration: 0:09:56.334155
Epoch : 4/5 Train_loss: 0.156 Validation_loss: 1.147 Duration: 0:10:12.625819
Epoch : 5/5 Train_loss: 0.135 Validation_loss: 1.603 Duration: 0:09:56.746308
Isn't the validation loss suppose to decrease with epochs ? So why is it first decreasing and then increasing ?
How should I set the number of epochs, and why ?
Any help is really appreciated !

You are facing the phenomenon of "overfitting" when your validation loss goes up after decreasing. You should stop training at that point and try to use some tricks to avoid overfitting.
Getting different predictions might happen when your gradients keep updating during inference so try explicitly "stop" them from updating with torch.no_grad()

Very high validation loss/small train loss in Pytorch, while finetuning resnet 50

I am training model to classify 2 types of images. I have decided to take a transfer-learning approach, freeze every part of resnet50 and new layer and start finetuning process. My dataset is not perfectly balanced but i used weights for that purpose.Please take a look at validation loss vs training loss graph. It seems to be extremely inconsitent. Could you please take a look at my code? I am new to Pytorch, maybe there is something wrong with my method and code. Final accuracy tested on test set is 86%. Thank you!
learning_rate = 1e-1
num_epochs = 100
patience = 10
batch_size = 100
weights = [4, 1]
model = models.resnet50(pretrained=True)
# Replace last layer
num_features = model.fc.in_features
model.fc = nn.Sequential(
nn.Linear(num_features, 512),
nn.ReLU(inplace=True),
nn.Linear(512, 64),
nn.Dropout(0.5, inplace=True),
nn.Linear(64, 2))
class_weights = torch.FloatTensor(weights).cuda()
criterion = nn.CrossEntropyLoss(weight=class_weights)
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
running_loss = 0
losses = []
# To freeze the residual layers
for param in model.parameters():
param.requires_grad = False
for param in model.fc.parameters():
param.requires_grad = True
# Find total parameters and trainable parameters
total_params = sum(p.numel() for p in model.parameters())
print(f'{total_params:,} total parameters.')
total_trainable_params = sum(
p.numel() for p in model.parameters() if p.requires_grad)
print(f'{total_trainable_params:,} training parameters.')
24,590,082 total parameters.
1,082,050 training parameters.
# initialize the early_stopping object
early_stopping = pytorchtools.EarlyStopping(patience=patience, verbose=True)
for epoch in range(num_epochs):
##########################
#######TRAIN MODEL########
##########################
epochs_loss=0
##Switch to train mode
model.train()
for i, (images, labels) in enumerate(train_dl):
# Move tensors to the configured device
images = images.to(device)
labels = labels.to(device)
# Forward pass
# Backprpagation and optimization
optimizer.zero_grad()
outputs = model(images).to(device)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
#calculate train_loss
train_losses.append(loss.item())
##########################
#####VALIDATE MODEL#######
##########################
model.eval()
for images, labels in val_dl:
images = images.to(device)
labels = labels.to(device)
outputs = model(images).to(device)
loss = criterion(outputs,labels)
valid_losses.append(loss.item())
# print training/validation statistics
# calculate average loss over an epoch
train_loss = np.average(train_losses)
valid_loss = np.average(valid_losses)
# print(train_loss)
avg_train_losses.append(train_loss)
avg_valid_losses.append(valid_loss)
print_msg = (f'train_loss: {train_loss:.5f} ' + f'valid_loss: {valid_loss:.5f}')
print(print_msg)
# clear lists to track next epoch
train_losses = []
valid_losses = []
early_stopping(valid_loss, model)
print(epoch)
if early_stopping.early_stop:
print("Early stopping")
break

Pytorch vs. Keras: Pytorch model overfits heavily

For several days now, I'm trying to replicate my keras training results with pytorch. Whatever I do, the pytorch model will overfit far earlier and stronger to the validation set then in keras. For pytorch I use the same XCeption Code from https://github.com/Cadene/pretrained-models.pytorch.
The dataloading, the augmentation, the validation, the training schedule etc. are equivalent. Am I missing something obvious? There must be a general problem somewhere. I tried thousands of different module constellations, but nothing seems to come even close to the keras training. Can somebody help?
Keras model: val accuracy > 90%
# base model
base_model = applications.Xception(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3))
# top model
x = base_model.output
x = GlobalMaxPooling2D()(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.5)(x)
predictions = Dense(4, activation='softmax')(x)
# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)
# Compile model
from keras import optimizers
adam = optimizers.Adam(lr=0.0001)
model.compile(loss='categorical_crossentropy',
optimizer=adam, metrics=['accuracy'])
# LROnPlateau etc. with equivalent settings as pytorch
Pytorch model: val accuracy ~81%
from xception import xception
import torch.nn.functional as F
# modified from https://github.com/Cadene/pretrained-models.pytorch
class XCeption(nn.Module):
def __init__(self, num_classes):
super(XCeption, self).__init__()
original_model = xception(pretrained="imagenet")
self.features=nn.Sequential(*list(original_model.children())[:-1])
self.last_linear = nn.Sequential(
nn.Linear(original_model.last_linear.in_features, 512),
nn.ReLU(),
nn.Dropout(p=0.5),
nn.Linear(512, num_classes)
)
def logits(self, features):
x = F.relu(features)
x = F.adaptive_max_pool2d(x, (1, 1))
x = x.view(x.size(0), -1)
x = self.last_linear(x)
return x
def forward(self, input):
x = self.features(input)
x = self.logits(x)
return x
device = torch.device("cuda")
model=XCeption(len(class_names))
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
model = nn.DataParallel(model)
model.to(device)
criterion = nn.CrossEntropyLoss(size_average=False)
optimizer = optim.Adam(model.parameters(), lr=0.0001)
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.2, patience=5, cooldown=5)
Thank you very much!
Update:
Settings:
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.2, patience=5, cooldown=5)
model = train_model(model, train_loader, val_loader,
criterion, optimizer, scheduler,
batch_size, trainmult=8, valmult=10,
num_epochs=200, epochs_top=0)
Cleaned training function:
def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, batch_size, trainmult=1, valmult=1, num_epochs=None, epochs_top=0):
for epoch in range(num_epochs):
for phase in ['train', 'val']:
running_loss = 0.0
running_acc = 0
total = 0
# Iterate over data.
if phase=="train":
model.train(True) # Set model to training mode
for i in range(trainmult):
for data in train_loader:
# get the inputs
inputs, labels = data
inputs, labels = inputs.to(torch.device("cuda")), labels.to(torch.device("cuda"))
# zero the parameter gradients
optimizer.zero_grad()
# forward
outputs = model(inputs) # notinception
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
loss.backward()
optimizer.step()
# statistics
total += labels.size(0)
running_loss += loss.item()*labels.size(0)
running_acc += torch.sum(preds == labels)
train_loss=(running_loss/total)
train_acc=(running_acc.double()/total)
else:
model.train(False) # Set model to evaluate mode
with torch.no_grad():
for i in range(valmult):
for data in val_loader:
# get the inputs
inputs, labels = data
inputs, labels = inputs.to(torch.device("cuda")), labels.to(torch.device("cuda"))
# zero the parameter gradients
optimizer.zero_grad()
# forward
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels.data)
# statistics
total += labels.size(0)
running_loss += loss.item()*labels.size(0)
running_acc += torch.sum(preds == labels)
val_loss=(running_loss/total)
val_acc=(running_acc.double()/total)
scheduler.step(val_loss)
return model

it may be because type of weight initialization you are using
otherwise this should not happen
try with same initializer in both the models

self.features=nn.Sequential(*list(original_model.children())[:-1])
Are you sure that this line re-instantiates your model in exactly the same way? You're using a NN.Sequential instead of the original XCeption model's forward function. If there's anything in that forward function that isn't the exact same as using a nn.Sequential, it will not reproduce the same performance.
Instead of wrapping it in a Sequential, you could just change this
my_model = Xception()
# load weights before you change the architecture
my_model = load_weights(path_to_weights)
# overwrite the original's last_linear with your own
my_model.last_linear = nn.Sequential(
nn.Linear(original_model.last_linear.in_features, 512),
nn.ReLU(),
nn.Dropout(p=0.5),
nn.Linear(512, num_classes)
)