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 " 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()