Firstly, a bunch of data is classified by the CNN model. Then, I’m trying to make prediction on correctly classified data from first step, which is expected to give an accuracy of 100%. However, I found the result is unstable, sometimes 99+%, but not 100%. Is there anybody know what is the problem with my code? Thank you very much in advance, it has troubled me several days ~ ~
torch.version
‘0.3.1.post2’
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
n = 2000
data = np.random.randn(n, 1, 10, 10)
label = np.random.randint(2, size=(n, ))
def test_pred(model, data_test, label_test):
data_batch = data_test
labels_batch = label_test
images = torch.autograd.Variable(torch.FloatTensor(data_batch))
labels = torch.autograd.Variable(torch.FloatTensor(labels_batch))
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
correct = (np.array(predicted) == labels_batch).sum()
label_pred = np.array(predicted)
acc = correct/len(label_test)
print(" acc:", acc)
return acc, label_pred
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(1, 16, kernel_size=5, padding=2),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(2))
self.layer2 = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=5, padding=2),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(2))
self.fc = nn.Linear(128, 2)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
cnn = CNN()
[_, label_pred] = test_pred(cnn, data, label)
print("Acc:", np.mean(label_pred==label))
# Given the correctly classified data in previous step, expect to get 100% accuracy
# Why it sometimes doesn't give a 100% accuracy ?
print("Using selected data size {}:".format(data[label_pred==label].shape))
_, _ = test_pred(cnn, data[label_pred==label], label[label_pred==label])
