Hi,

I am doing an implementation the exercises for Machine Learning Lecture from Coursera by Andrew Ng.

In ex02, my mission is predicting the result of someone’s admission to a school.

As a dataset, there given 2 exam score as x1 and x2, and 1 or 0 as a result of admission.

I correctly implemented Logistic Regression for this problem in Octave with same dataset.

But It doesn’t work at all in Pytorch. Doing this simple work whole day, I ask your help.

As simple code, I am uploading my code in Pytorch.

The problems are below.

- The loss goes down somewhat, but it is always above 0.6 but It was down as 0.2 when I did in Octave.
- As a result, it doesn’t predict well… actually never.

```
import pandas as pd
import numpy as np
import torch
import matplotlib.pyplot as plt
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
class LogisticRegression(nn.Module):
def __init__(self, input_size, num_class):
super(LogisticRegression, self).__init__()
self.linear = nn.Linear(input_size, num_class)
def forward(self, x):
out = self.linear(x)
return F.sigmoid(out)
data = pd.read_csv('data/ex2data1.txt', header=None)
X = Variable(torch.from_numpy(data.iloc[:, :2].as_matrix()).float())
Y = Variable(np.reshape(torch.from_numpy(data.iloc[:, 2].as_matrix()).float(),
(-1, 1)))
input_size = 2
num_class = 1
total_epoch = 1000
learning_rate = 0.0001
model = LogisticRegression(input_size, num_class)
criterion = nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
loss_history = []
for epoch in range(total_epoch):
output = model(X)
optimizer.zero_grad()
loss = criterion(output, Y)
loss.backward()
optimizer.step()
if (epoch + 1) % 5 == 0:
print('Epoch: [%d/%d], Loss: %.4f' %(epoch+1, total_epoch, loss.data[0]))
loss_history.append(loss.data[0])
input = Variable(torch.Tensor([20, 30]))
print("predict", (20, 30), model(input).data)
input = Variable(torch.Tensor([80, 30]))
print("predict", (80, 30), model(input).data)
input = Variable(torch.Tensor([10, 10]))
print("predict", (10, 10), model(input).data)
input = Variable(torch.Tensor([45, 85]))
print("predict", (45, 85), model(input).data)
def plotLossHistory(loss_history):
plt.figure()
plt.plot(loss_history)
plt.show()
plotLossHistory(loss_history)
```