Created
February 16, 2019 08:14
-
-
Save InnovArul/ca0db578769186bbaf094c5f8416a8bc to your computer and use it in GitHub Desktop.
pytorch forum (Model parameters are not being updated?)
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
from __future__ import print_function | |
import torch.nn.functional as F | |
# from torch.autograd import Variable | |
# import copy | |
import torch | |
import torch.nn as nn | |
import torch.optim as optim | |
from torch.utils.data import DataLoader | |
from torchvision import datasets, transforms | |
class ConvNet(nn.Module): | |
def __init__(self): | |
super(ConvNet, self).__init__() | |
filter = torch.Tensor( | |
[[[0.06, 0, 0], | |
[0.1, 0, 0.2], | |
[0.06, 0.1, 0]], | |
[[0.1, 0, 0], | |
[0.2, 0, 0], | |
[0.1, 0, 0]]]) | |
self.register_buffer("filter", filter) | |
self.weight = nn.Parameter(torch.Tensor(1, 1, 2)) | |
self.bias = nn.Parameter(torch.Tensor(1)) | |
# init params | |
nn.init.xavier_uniform_(self.weight) | |
self.bias.data.uniform_(-1, 1) | |
self.bn1 = nn.BatchNorm2d(1) | |
self.fc = nn.Linear(1*13*13, 10) | |
def forward(self, x): | |
self.kernel = torch.einsum("ijk, klm -> ijlm", self.weight, self.filter) | |
out = F.conv2d(input=x, weight=self.kernel, bias=self.bias) | |
out = self.bn1(out) | |
out = nn.ReLU()(out) | |
out = nn.MaxPool2d(kernel_size=2, stride=2)(out) | |
out = out.reshape(out.size(0), -1) | |
out = self.fc(out) | |
return out | |
def main(argv=None): | |
# Device configuration | |
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') | |
# Hyper parameters | |
num_epochs = 100 | |
batch_size = 1024 | |
learning_rate = 0.001 | |
log_interval = 10 | |
# MNIST dataset | |
train_dataset = datasets.MNIST( | |
root='mnist_data/', | |
train=True, | |
transform=transforms.ToTensor(), | |
download=True) | |
train_loader = torch.utils.data.DataLoader( | |
dataset=train_dataset, | |
batch_size=batch_size, | |
shuffle=True) | |
model = ConvNet() | |
model.to(device) | |
for name, param in model.named_parameters(): | |
print(name, '\t\t', param.shape) | |
optimizer = optim.SGD(model.parameters(), lr=learning_rate) | |
loss_fn = nn.CrossEntropyLoss() | |
for epoch in range(1, num_epochs + 1): | |
model.train() | |
for batch_idx, (data, target) in enumerate(train_loader): | |
data, target = data.to(device), target.to(device) | |
optimizer.zero_grad() | |
output = model(data) | |
loss = loss_fn(output, target) | |
# print(model.weight) | |
# print(model.kernel) | |
# print(model.weight.grad) | |
a = list(model.parameters())[0].clone() | |
loss.backward() | |
optimizer.step() | |
b = list(model.parameters())[0].clone() | |
print(torch.equal(a.data, b.data)) | |
if batch_idx % log_interval == 0: | |
_, predicted = torch.max(output.data, 1) | |
total = target.size(0) | |
correct = (predicted == target).sum().item() | |
print('batch Accuracy: {} %'.format(100 * correct / total)) | |
if __name__ == '__main__': | |
main() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment