Hello- below is my code for an AlexNet assignment- If someone could lo.pdf

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Hello, below is my code for an AlexNet assignment. If someone could look over, it to ensure it is optimal or could be improved on. Thank You import torch import torch.nn as nn import torchvision.datasets as datasets import torch.optim as optim import torchvision.transforms as transforms from torchvision.models import AlexNet # define data transforms transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) # load the data trainset = datasets.CIFAR100(root='./data', train=True, download=True, transform=transform) testset = datasets.CIFAR100(root='./data', train=False, download=True, transform=transform) #define the network architecture class AlexNet(nn.Module): def __init__(self): super(AlexNet, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), nn.Conv2d(64, 192, kernel_size=5, padding=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), nn.Conv2d(192, 384, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), ) self.avgpool = nn.AdaptiveAvgPool2d((6, 6)) self.classifier = nn.Sequential( nn.Dropout(), nn.Linear(256 * 6 * 6, 4096), nn.ReLU(inplace=True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(inplace=True), nn.Linear(4096, 1000), ) def forward(self, x): x = self.features(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x # set device to GPU if available, otherwise use CPU device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # initialize network, loss function, and optimizer net = AlexNet(num_classes=1000) net.to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) # train the network num_epochs = 10 for epoch in range(num_epochs): running_loss = 0.0 for i, data in enumerate(trainset, 0): inputs, labels = data if not isinstance(inputs, torch.Tensor): inputs = torch.tensor(inputs) if not isinstance(labels, torch.Tensor): labels = torch.tensor(labels) inputs, labels = inputs.to(device), labels.to(device) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() if i % 2000 == 1999: print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000)) running_loss = 0.0.

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nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, 1000),
)
def forward(self, x):
x = self.features(x)

x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x
# set device to GPU if available, otherwise use CPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# initialize network, loss function, and optimizer
net = AlexNet(num_classes=1000)
net.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# train the network
num_epochs = 10
for epoch in range(num_epochs):
running_loss = 0.0
for i, data in enumerate(trainset, 0):
inputs, labels = data
if not isinstance(inputs, torch.Tensor):
inputs = torch.tensor(inputs)
if not isinstance(labels, torch.Tensor):
labels = torch.tensor(labels)
inputs, labels = inputs.to(device), labels.to(device)
# zero the parameter gradients

optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0

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Hello below is my code for MPL image classification. When I try to run the bold segment, I am given the following error: "default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'PIL.Image.Image'>" if someone can help me I would appreciate it. import torchvision.datasets as datasets import torch.optim as optim import torch.utils.data as data import torch.nn as nn from torchvision import transforms train_data = datasets.CIFAR100(root='data', train=True, transform=None, download=True) test_data = datasets.CIFAR100(root='data', train=False, transform=None, download=True) class MLP(nn.Module): def __init__(self, input_size, hidden_size, num_classes): super(MLP, self).__init__() self.fc1 = nn.Linear(input_size, hidden_size) self.relu1 = nn.ReLU() self.fc2 = nn.Linear(hidden_size, hidden_size) self.relu2 = nn.ReLU() self.fc3 = nn.Linear(hidden_size, num_classes) def forward(self, x): out = self.fc1(x) out = self.relu1(out) out = self.fc2(out) out = self.relu2(out) out = self.fc3(out) return out input_size = 32 * 32 * 3 hidden_size = 512 num_classes = 100 learning_rate = 0.001 batch_size = 128 num_epochs = 10 model = MLP(input_size, hidden_size, num_classes) train_loader = data.DataLoader(train_data, batch_size=batch_size, shuffle=True) test_loader = data.DataLoader(test_data, batch_size=batch_size, shuffle=False) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) for epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): images = images.reshape(-1, input_size).to(device) labels = labels.to(device) optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() .

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