# A strange error result given by softmax function in CNN network

when I put an `image` inside a CNN network `net` by `net(image)`, it gives two different answers by two different ways, I wonder if it is a bug

``````result = net(image)

softmax_result1 = F.softmax(result, dim=1)
print(softmax_result1)
softmax_result2 = F.softmax(net(image), dim=1)
print(softmax_result2)
``````

and the result is

``````tensor([[0.5803, 0.0120, 0.0462, 0.0072, 0.0107, 0.0356, 0.0964, 0.1650, 0.0118,
tensor([[0.4298, 0.1368, 0.0622, 0.0018, 0.0571, 0.0178, 0.0075, 0.2660, 0.0081,
``````

however, this does not happen when I try with only linear network

here is the full code I used, where the `image` is a 448,448 image

``````import toruch
import torch.nn as nn
import torch.nn.functional as F
import cv2

torch.manual_seed(42)

def initialize_weights(m):
if type(m) == nn.Linear or type(m) == nn.Conv2d:
nn.init.xavier_uniform_(m.weight)
def VGG_block(num_convs, in_channels, out_channels):
blk = []
for _ in range(num_convs):
blk.append(nn.ReLU())
in_channels = out_channels
blk.append(nn.MaxPool2d(kernel_size=2, stride=2))

return nn.Sequential(*blk)

def VGG(conv_arch):
conv_blks = []
in_channels = 1
for (num_convs, out_channels) in conv_arch:
conv_blks.append(VGG_block(num_convs, in_channels, out_channels))
in_channels = out_channels

return nn.Sequential(
*conv_blks, nn.Flatten(),
nn.Linear(out_channels * 7 * 7, 4096), nn.ReLU(), nn.Dropout(0.5),
nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(0.5),
nn.Linear(4096, 10)
)
conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))
net = VGG(conv_arch)
net.apply(initialize_weights)

image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
image = 255 - image
image = cv2.resize(image, (224, 224))
image = torch.tensor(image, dtype=torch.float32).reshape(1, 1, 224, 224)

result = net(image)

softmax_result1 = F.softmax(result, dim=1)
print(softmax_result1)
softmax_result2 = F.softmax(net(image), dim=1)
print(softmax_result2)
``````

Hi Muling!

`Dropout` (when in training mode) pseudorandomly zeros out some elements
of the tensor passing through it, and does so differently on every forward call.

Try:

``````result = net(image)
softmax_result1 = F.softmax(result, dim=1)
print(softmax_result1)
softmax_result1 = F.softmax(result, dim=1)
print(softmax_result1)

softmax_result2 = F.softmax(net(image), dim=1)
print(softmax_result2)
softmax_result2 = F.softmax(net(image), dim=1)
print(softmax_result2)

net.eval()   # turns off Dropout`

result = net(image)
softmax_result1 = F.softmax(result, dim=1)
print(softmax_result1)

softmax_result2 = F.softmax(net(image), dim=1)
print(softmax_result2)
``````

Best.

K. Frank

You might try:

``````with torch.no_grad():
net.eval()
result=net(image)
...

``````

That will shut off any training modules, such as dropout or batchnorm.