batch_size=batch_size)
test_loader = torch.utils.data.DataLoader(dataset=test_data,
batch_size=batch_size)
### Define Model and Load Params
model = ConvNet().to(device)
print("========================== Original Model =============================", "\n", model)
model.load_state_dict(torch.load('./pths/cifar10_pre_model.pth', map_location=device))
### User pre-trained model and Only change last layer
for param in model.parameters():
param.requires_grad = False
model.fc2 = nn.Linear(120, 50)
modle = model.to(device)
print("========================== Modified Model =============================", "\n", model)
### Define Loss and Optim
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
### Train
if __name__ == '__main__':
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.to(device)
labels = labels.to(device)
'val':
DataLoader(data['val'], batch_size=batch_size, shuffle=True, num_workers=4)
}
#############
### MODEL ###
#############
from torch import nn, optim
from cnn import ConvNet
num_epochs = 10
learning_rate = 0.001
net = ConvNet()
net.to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(net.parameters(), lr=learning_rate, betas=(0.5, 0.999))
################
### TRAINING ###
################
import matplotlib.pyplot as plt
n_batches = len(loader['train'])
for epoch in range(num_epochs):
for i, sample in enumerate(loader['train']):
sample['image'] = sample['image'].to(device)