I have been trying to make the entire starting columns in the weight matrix to zero. But after the training, all weight matrices look same. Please help me with a better way in order to achieve this.
#My code::
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
input_size = 784
hidden_size = 500
num_classes = 10
num_epochs = 3
batch_size = 256
learning_rate = 0.01
a =
b =
c =
d =
e =
f =
x =
train_dataset = torchvision.datasets.MNIST(root=‘…/…/data’,
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = torchvision.datasets.MNIST(root=‘…/…/data’,
train=False,
transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
class NeuralNet(nn.Module):
def init(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).init()
self.fc1 = nn.Linear(input_size, hidden_size, bias=False)
nn.init.normal_(self.fc1.weight)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_classes,bias=False)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
model = NeuralNet(input_size, hidden_size, num_classes).to(device)
a=model.fc1.weight
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0)
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.reshape(-1, 28*28).to(device)
labels = labels.to(device)
if epoch == 0 & i==0:
with torch.no_grad():
d=model.fc1.weight
d[:,0:1]=0
model.fc1.weight.data=d
x=model.fc1.weight
if epoch == 1:
b=model.fc1.weight
if epoch == 2:
c=model.fc1.weight
if epoch == 3:
e=model.fc1.weight
if epoch == 4:
f=model.fc1.weight
outputs = model(images)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
b=model.fc1.weight.detach().numpy()
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.reshape(-1, 28*28).to(device)
labels = labels.to(device)
outputs = model(images)
loss = criterion(outputs, labels)
#b.append(loss.detach().numpy())
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))
torch.save(model.state_dict(), ‘model.ckpt’)
