Hello, I want to get two outputs from my network after train,
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
transform = transforms.ToTensor()
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)
#Define the Convolutional Autoencoder
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))
x1 = F.relu(self.conv5(x))
x = F.relu(self.conv6(x1))
#out = self.out(x)
return x,x1
#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)
but my code have this error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-30-a6c530203f22> in <module>()
3 model = ConvAutoencoder()
4 max_epochs =10
----> 5 outputs = train(model, num_epochs=max_epochs)
3 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py in mse_loss(input, target, size_average, reduce, reduction)
3077 mse_loss, (input, target), input, target, size_average=size_average, reduce=reduce, reduction=reduction
3078 )
-> 3079 if not (target.size() == input.size()):
3080 warnings.warn(
3081 "Using a target size ({}) that is different to the input size ({}). "
AttributeError: 'tuple' object has no attribute 'size'
please help me for solve this problem