Hello, I’m new at pytroch and I’m currently working on my first cnn for image recognition. My images have the size 28*28 and are gray, so they have a channel of 1. I get a message every time that i have a size mismatch. I can’t figure out what is wrong. Can anyone help me? I
Error Code:
Traceback (most recent call last):
File “C:\Users\jessi\Downloads\Code (1)\BA\main1.py”, line 238, in
train(model, device, train_loader, optimizer, epoch)
File “C:\Users\jessi\Downloads\Code (1)\BA\main1.py”, line 183, in train
output = model(data)
File “C:\Users\jessi\anaconda3\lib\site-packages\torch\nn\modules\module.py”, line 722, in _call_impl
result = self.forward(*input, **kwargs)
File “C:\Users\jessi\Downloads\Code (1)\BA\main1.py”, line 62, in forward
x = self.fc1(x)
File “C:\Users\jessi\anaconda3\lib\site-packages\torch\nn\modules\module.py”, line 722, in _call_impl
result = self.forward(*input, **kwargs)
File “C:\Users\jessi\anaconda3\lib\site-packages\torch\nn\modules\linear.py”, line 91, in forward
return F.linear(input, self.weight, self.bias)
File “C:\Users\jessi\anaconda3\lib\site-packages\torch\nn\functional.py”, line 1674, in linear
ret = torch.addmm(bias, input, weight.t())
RuntimeError: size mismatch, m1: [1 x 512], m2: [256 x 128] at …\aten\src\TH/generic/THTensorMath.cpp:41
from __future__ import print_function
import numpy as np
import pandas as pd
import torch
from PIL import Image
from torch.utils.data.dataset import Dataset
from torchvision import transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
class Net(nn.Module):
def __init__(self):
# init is the constructor for a class.The self parameter refers to the instance of the object
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=(5,5))
#3 Channels da buntes Bild
torch.nn.init.ones_(self.conv1.weight)
self.pool1 = nn.MaxPool2d(2,2)
self.conv2 = nn.Conv2d(32, 64, kernel_size=(5,5))
self.pool2 = nn.MaxPool2d(2,2)
self.conv3 = nn.Conv2d(64, 128,kernel_size=(5,5))
self.conv4 = nn.Conv2d(128, 64,kernel_size=(5,5))
self.conv5 = nn.Conv2d(64, 32, kernel_size=(5,5))
self.conv6 = nn.Conv2d(32, 16, kernel_size=(5,5))
self.pool3 = nn.MaxPool2d(2,2)
self.conv7 = nn.Conv2d(16, 32, kernel_size=(5,5))
self.conv8 = nn.Conv2d(32, 64, kernel_size=(5,5))
#self.dropout1 = nn.Dropout2d(0.25)
#self.dropout2 = nn.Dropout2d(0.5)
# ((I - K + 2*P) / S) + 1
self.fc1 = nn.Linear(64*2*2, 128)
self.fc2 = nn.Linear(128, 64)
self.fc3= nn.Linear(64, 32)
self.fc4= nn.Linear(32, 16)
self.fc5= nn.Linear(16, 8)
self.fc6= nn.Linear(8, 4)
self.fc7= nn.Linear(4, 2)
self.fc8= nn.Linear(2, 1)
def forward(self, x):
x = self.conv1(x)
x = F.max_pool2d(x, 2)
x = F.relu(x)
x = self.conv2(x)
x = F.max_pool2d(x, 2)
x = F.relu(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
x = F.relu(x)
x = self.fc3(x)
x = F.relu(x)
x = self.fc4(x)
x = F.relu(x)
x = self.fc5(x)
x = F.relu(x)
x = self.fc6(x)
x = F.relu(x)
x = self.fc7(x)
x = F.relu(x)
x = self.fc8(x)
'''
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
'''
#output = F.log_softmax(x, dim=1)
return x
