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KAN_vs_MLP.py
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import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
# from torchsummary import summary
import torch
from efficientkan import KAN as efficientKAN
from fastkan import FastKAN as fastKAN
from trainer import Trainer
# CNN model for CIFAR-10 with KANLinear
class EfficientKAN(nn.Module):
def __init__(self, num_classes, dataset_name):
super(EfficientKAN, self).__init__()
if dataset_name == 'CIFAR10':
self.input_size = 3072
elif dataset_name == 'MNIST':
self.input_size = 784
self.efficientKAN = efficientKAN([self.input_size, 256, num_classes])
def forward(self, x):
print(x.shape)
x = x.view(-1, self.input_size)
x = self.efficientKAN(x)
return x
# CNN model for CIFAR-10 with KANLinear
class MLP(nn.Module):
def __init__(self, num_classes, dataset_name):
super(MLP, self).__init__()
if dataset_name == 'CIFAR10':
self.input_size = 3072
elif dataset_name == 'MNIST':
self.input_size = 784
self.mlp = nn.Sequential(
nn.Linear(self.input_size, 256),
nn.SELU(),
nn.Linear(256, num_classes)
)
def forward(self, x):
x = x.view(-1, self.input_size)
x = self.mlp(x)
return x
# CNN model for CIFAR-10 with fastKAN
class FastKAN(nn.Module):
def __init__(self, num_classes, dataset_name):
super(FastKAN, self).__init__()
if dataset_name == 'CIFAR10':
self.input_size = 3072
elif dataset_name == 'MNIST':
self.input_size = 784
self.fastKAN = fastKAN([self.input_size, 256, num_classes])
def forward(self, x):
x = x.view(-1, self.input_size)
x = self.fastKAN(x)
return x
if __name__ == '__main__':
## device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
## batch size
batch_size = 64
## code_version_flag
isMNIST = True
## dataset transform
transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
## dataset and dataloader
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.CIFAR10(root='./data', train=False, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
## MNIST dataset and dataloader
transform = transforms.Compose([
transforms.Resize((28, 28)),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
train_dataset_MNIST = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_dataset_MNIST = datasets.MNIST(root='./data', train=False, transform=transform)
train_loader_MNIST = DataLoader(train_dataset_MNIST, batch_size=batch_size, shuffle=True)
test_loader_MNIST = DataLoader(test_dataset_MNIST, batch_size=batch_size, shuffle=False)
## define models
MLP_model_1 = MLP(num_classes=10, dataset_name='CIFAR10').to(device)
EfficientKAN_model_1 = EfficientKAN(num_classes=10, dataset_name='CIFAR10').to(device)
FastKAN_model_1 = FastKAN(num_classes=10, dataset_name='CIFAR10').to(device)
MLP_model_2 = MLP(num_classes=10, dataset_name='MNIST').to(device)
EfficientKAN_model_2 = EfficientKAN(num_classes=10, dataset_name='MNIST').to(device)
FastKAN_model_2 = FastKAN(num_classes=10, dataset_name='MNIST').to(device)
## define optimizer
MLP_optimizer_1 = optim.AdamW(MLP_model_1.parameters(), lr=1e-3, weight_decay=1e-4)
EfficientKAN_optimizer_1 = optim.AdamW(EfficientKAN_model_1.parameters(), lr=1e-3, weight_decay=1e-4)
FastKAN_optimizer_1 = optim.AdamW(FastKAN_model_1.parameters(), lr=1e-3, weight_decay=1e-4)
MLP_optimizer_2 = optim.AdamW(MLP_model_2.parameters(), lr=1e-3, weight_decay=1e-4)
EfficientKAN_optimizer_2 = optim.AdamW(EfficientKAN_model_2.parameters(), lr=1e-3, weight_decay=1e-4)
FastKAN_optimizer_2 = optim.AdamW(FastKAN_model_2.parameters(), lr=1e-3, weight_decay=1e-4)
## define loss function
criterion = nn.CrossEntropyLoss()
## training schedular
schedular_MLP_1 = optim.lr_scheduler.ExponentialLR(MLP_optimizer_1, gamma=0.8)
schedular_EfficientKAN_1 = optim.lr_scheduler.ExponentialLR(EfficientKAN_optimizer_1, gamma=0.8)
schedular_FastKAN_1 = optim.lr_scheduler.ExponentialLR(FastKAN_optimizer_1, gamma=0.8)
schedular_MLP_2 = optim.lr_scheduler.ExponentialLR(MLP_optimizer_2, gamma=0.8)
schedular_EfficientKAN_2 = optim.lr_scheduler.ExponentialLR(EfficientKAN_optimizer_2, gamma=0.8)
schedular_FastKAN_2 = optim.lr_scheduler.ExponentialLR(FastKAN_optimizer_2, gamma=0.8)
file_path = 'saved_models\\KAN_vs_MLP.txt'
if isMNIST:
models = [ MLP_model_2, EfficientKAN_model_2, FastKAN_model_2]
model_names = ['MLP', 'EfficientKAN', 'FastKAN']
dataset_name = ['MNIST']
train_dataset_loader = [train_loader_MNIST]
test_dataset_loader = [test_loader_MNIST]
optimizers = [MLP_optimizer_2, EfficientKAN_optimizer_2, FastKAN_optimizer_2]
schedulars = [schedular_MLP_2, schedular_EfficientKAN_2, schedular_FastKAN_2]
else:
models = [MLP_model_1, EfficientKAN_model_1, FastKAN_model_1]
model_names = ['MLP', 'EfficientKAN', 'FastKAN']
dataset_name = ['CIFAR10']
train_dataset_loader = [train_loader]
test_dataset_loader = [test_loader]
optimizers = [MLP_optimizer_1, EfficientKAN_optimizer_1, FastKAN_optimizer_1]
schedulars = [schedular_MLP_1, schedular_EfficientKAN_1, schedular_FastKAN_1]
epochs = 10
args_dict = {}
args_dict['num_models'] = len(models)
args_dict['num_datasets'] = 1
for index in range(args_dict['num_datasets']):
args_dict[('dataset_name', index)] = dataset_name[index]
args_dict[('trainloader', index)] = train_dataset_loader[index]
args_dict[('testloader', index)] = test_dataset_loader[index]
args_dict['record_save_path'] = file_path
args_dict['epochs'] = epochs
args_dict['device'] = device
args_dict['loss_function'] = criterion
args_dict['weights_save_path'] = 'saved_models'
for m in range(args_dict['num_models']):
args_dict[('model', m)] = models[m]
args_dict[('model_name', m)] = model_names[m]
args_dict[('optimizers', m)] = optimizers[m]
args_dict[('schedulers', m)] = schedulars[m]
trainer = Trainer(args_dict)
trainer.train_models()