am new in pytorch.
I got this error when i was try to train my model.Any one can help me please
Here is my code
import numpy as np
import pandas as pd
import os
import torchvision
from torchvision import transforms, datasets, models
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim, cuda
from torch.utils.data import sampler, DataLoader
import matplotlib.pyplot as plt
import seaborn as sn
import torch.optim as optim
import tqdm as tqdm
import cv2
from timeit import default_timer as timer
from PIL import Image
image_transforms = {
'train':
transforms.Compose([
transforms.RandomResizedCrop(size=256, scale=(0.8, 1.0)),
transforms.RandomRotation(degrees=15),
transforms.ColorJitter(),
transforms.RandomHorizontalFlip(),
transforms.CenterCrop(size=224), # Image net standards
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225]) # Imagenet standards
]),
'val':
transforms.Compose([
transforms.Resize(size=256),
transforms.CenterCrop(size=224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'test':
transforms.Compose([
transforms.Resize(size=256),
transforms.CenterCrop(size=224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
data = {
‘train’:
datasets.ImageFolder(root=traindir, transform=image_transforms[‘train’]),
‘val’:
datasets.ImageFolder(root=validdir, transform=image_transforms[‘val’]),
‘test’:
datasets.ImageFolder(root=testdir, transform=image_transforms[‘test’])
}
dataloaders = {
‘train’: DataLoader(data[‘train’], batch_size=128, shuffle=True),
‘val’: DataLoader(data[‘val’], batch_size=128, shuffle=True),
‘test’: DataLoader(data[‘test’], batch_size=128, shuffle=True)
}
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 5)
self.conv2 = nn.Conv2d(32, 64, 5)
self.conv3 = nn.Conv2d(64, 128, 5)
self.fc1 = nn.Linear(128 * 224 * 224, 512)
self.fc2 = nn.Linear(512, 2)
def forward(self, x):
# Max pooling over a (2, 2) window
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv3(x)), (2, 2))
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.softmax(x, dim=1)
def num_flat_features(self, x):
size = x.size()[1:] # all dimensions except the batch dimension
num_features = 1
for s in size:
num_features *= s
return num_features
net = Net()
print(net)
