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Bug fix and change of defaults #11

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Bug fix and change of defaults #11

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a2dedda
bug fix and change in defaults
comsaint Apr 13, 2021
130770c
change in defaults
comsaint Apr 13, 2021
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21 changes: 9 additions & 12 deletions net.py
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Original file line number Diff line number Diff line change
@@ -1,12 +1,9 @@
from __future__ import print_function

import torch
import torch.nn as nn
import torch.nn.functional as F
import math

from torch.optim import lr_scheduler
from torch.autograd import Variable


def squash(x):
Expand Down Expand Up @@ -165,14 +162,14 @@ def forward(self, lengths, targets, size_average=True):

# Training settings
parser = argparse.ArgumentParser(description='CapsNet with MNIST')
parser.add_argument('--batch-size', type=int, default=128, metavar='N',
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=250, metavar='N',
parser.add_argument('--test-batch-size', type=int, default=64, metavar='N',
help='input batch size for testing (default: 64)')
parser.add_argument('--epochs', type=int, default=10, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
help='learning rate (default: 0.01)')
help='learning rate (default: 0.001)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
Expand Down Expand Up @@ -241,7 +238,7 @@ def train(epoch):
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.data[0]))
100. * batch_idx / len(train_loader), loss.item()))

def test():
model.eval()
Expand All @@ -254,12 +251,12 @@ def test():

if args.with_reconstruction:
output, probs = model(data, target)
reconstruction_loss = F.mse_loss(output, data.view(-1, 784), size_average=False).data[0]
test_loss += loss_fn(probs, target, size_average=False).data[0]
reconstruction_loss = F.mse_loss(output, data.view(-1, 784), size_average=False).item()
test_loss += loss_fn(probs, target, size_average=False).item()
test_loss += reconstruction_alpha * reconstruction_loss
else:
output, probs = model(data)
test_loss += loss_fn(probs, target, size_average=False).data[0]
test_loss += loss_fn(probs, target, size_average=False).item()

pred = probs.data.max(1, keepdim=True)[1] # get the index of the max probability
correct += pred.eq(target.data.view_as(pred)).cpu().sum()
Expand Down