I am trying to implement a simple autoencoder using PyTorch
. My dataset consists of 256 x 256 x 3 images. I have built a torch.utils.data.dataloader.DataLoader
object which has the image stored as tensor. When I run the autoencoder, I get a runtime error:
size mismatch, m1: [76800 x 256], m2: [784 x 128] at /Users/soumith/minicondabuild3/conda-bld/pytorch_1518371252923/work/torch/lib/TH/generic/THTensorMath.c:1434
These are my hyperparameters:
batch_size=100,
learning_rate = 1e-3,
num_epochs = 100
Following is the architecture of my auto-encoder:
class autoencoder(nn.Module):
def __init__(self):
super(autoencoder, self).__init__()
self.encoder = nn.Sequential(
nn.Linear(3*256*256, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(True),
nn.Linear(64, 12),
nn.ReLU(True),
nn.Linear(12, 3))
self.decoder = nn.Sequential(
nn.Linear(3, 12),
nn.ReLU(True),
nn.Linear(12, 64),
nn.ReLU(True),
nn.Linear(64, 128),
nn.Linear(128, 3*256*256),
nn.ReLU())
def forward(self, x):
x = self.encoder(x)
#x = self.decoder(x)
return x
This is the code I used to run the model:
for epoch in range(num_epochs):
for data in dataloader:
img = data['image']
img = Variable(img)
# ===================forward=====================
output = model(img)
loss = criterion(output, img)
# ===================backward====================
optimizer.zero_grad()
loss.backward()
optimizer.step()
# ===================log========================
print('epoch [{}/{}], loss:{:.4f}'
.format(epoch+1, num_epochs, loss.data[0]))
if epoch % 10 == 0:
pic = show_img(output.cpu().data)
save_image(pic, './dc_img/image_{}.jpg'.format(epoch))
Whenever you have:
RuntimeError: size mismatch, m1: [a x b], m2: [c x d]
all you have to care is b=c
and you are done:
m1
is [a x b]
which is [batch size x in features]
m2
is [c x d]
which is [in features x out features]