@ptrblck he overall purpose of the “project” is to just help me get a better understanding of PyTorch. I’ve done a few courses on Udacity and YouTube but it didn’t really “click” with me. I started replicating projects myself like this one since I was able to get a better understanding of it. I’m doing gender and age of people so that I can learn more about data preprocessing and training.
UPDATE: the model accuracy is capping at 50% and I’m not even sure why. Can you take a look and lmk what I can do to increase it to at least 80%? @ptrblck
neural_network(
  (layer1): Sequential(
    (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1))
    (1): ReLU()
    (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
    (3): Dropout(p=0.1, inplace=False)
    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (layer2): Sequential(
    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1))
    (1): ReLU()
    (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
    (3): Dropout(p=0.1, inplace=False)
    (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (layer3): Sequential(
    (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))
    (1): ReLU()
    (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
    (3): Dropout(p=0.1, inplace=False)
    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (layer4): Sequential(
    (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1))
    (1): ReLU()
    (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
    (3): Dropout(p=0.1, inplace=False)
    (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (flatten): Flatten()
  (dropout): Dropout(p=0.5, inplace=False)
  (output): Linear(in_features=512, out_features=2, bias=True)
)