hello and regards…
I can not in test phase after changing one layer, to giving data for changed model and taking the images in output of test phase. please guide me…
my code:
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

#Converting data to torch.FloatTensor
transform = transforms.ToTensor()

# Download the training and test datasets
train_data = datasets.MNIST(root='data', train=True, download=True, transform=transform)

test_data = datasets.MNIST(root='data', train=False, download=True, transform=transform)

#Prepare data loaders
train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, num_workers=0)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, num_workers=0)

class ConvAutoencoder(nn.Module):
    def __init__(self):
        super(ConvAutoencoder, self).__init__()
       
        #Encoder
        self.conv1 = nn.Conv2d(1, 16, 3, stride=2, padding=1)
        self.conv2 = nn.Conv2d(16, 8, 3, stride=2, padding=1)
        self.conv3 = nn.Conv2d(8,8,3)
    
        #Decoder
        self.conv4 = nn.ConvTranspose2d(8, 8, 3)
        self.conv5 = nn.ConvTranspose2d(8, 16, 3, stride=2, padding=1, output_padding=1)
        self.conv6 = nn.ConvTranspose2d(16, 1, 3, stride=2, padding=1, output_padding=1)

    def forward(self, x):
        x = F.relu(self.conv1(x))      
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))  

        x = F.relu(self.conv4(x))
        x = F.relu(self.conv5(x))
        x = F.relu(self.conv6(x))

        return x

#Instantiate the model
model = ConvAutoencoder()
print(model)

def train(model, num_epochs=20, batch_size=64, learning_rate=1e-3):
    torch.manual_seed(42)
    criterion = nn.MSELoss() # mean square error loss
    optimizer = torch.optim.Adam(model.parameters(),
                                 lr=learning_rate, 
                                 weight_decay=1e-5) # <--
   # train_loader =train_loader;

    outputs = []
    for epoch in range(num_epochs):
        for data in train_loader:
            img, _ = data
            recon = model(img)
            loss = criterion(recon, img)
            loss.backward()
            optimizer.step()
            optimizer.zero_grad()

        print('Epoch:{}, Loss:{:.4f}'.format(epoch+1, float(loss)))
        outputs.append((epoch, img, recon),)
    return outputs

model =  ConvAutoencoder()
max_epochs =10
outputs = train(model, num_epochs=max_epochs)

for k in range(0, max_epochs, 9):
    plt.figure(figsize=(9, 2))
    imgs = outputs[k][1].detach().numpy()
    recon = outputs[k][2].detach().numpy()
    for i, item in enumerate(imgs):
        if i >= 9: break
        plt.subplot(2, 9, i+1)
        plt.imshow(item[0])
        
    for i, item in enumerate(recon):
        if i >= 9: break
        plt.subplot(2, 9, 9+i+1)
        plt.imshow(item[0])

a=(ConvAutoencoder().conv3.weight)
a0=a[:,0,:,:]
a1=a[:,1,:,:]
a2=a[:,2,:,:]
a3=a[:,3,:,:]
a4=a[:,4,:,:]
a5=a[:,5,:,:]
a6=a[:,6,:,:]
a7=a[:,7,:,:]
a0=a1
a1=a2
a2=a3
a3=a4
a4=a5
a5=a6
a6=a7
a7=a0

a = torch.cat((a0, a1, a2, a3, a4, a5, a6, a7))
               
model = ConvAutoencoder()
a = a.reshape(8, 8, 3, 3)

model.conv3.weight = nn.Parameter(a)

print(model.conv3.weight)

def test(model,test_loader):

    with torch.no_grad():
     for data in test_loader:
      output = model(data)
     return output 
output.view(1, 28, 28)   

error of my code:
<ipython-input-23-b8f79c214741> in <module>()
     25   #      plt.imshow(item)
     26 
---> 27 output.view(1, 28, 28)

NameError: name 'output' is not defined