cnn_models.py

#!/usr/bin/env python
# coding: utf-8

# In[ ]:


#!/usr/bin/env python
# coding: utf-8

# ### loading the label binarizer

# In[ ]:


import torch.nn as nn
import torch.nn.functional as F
import joblib
# load the binarized labels
print('Loading label binarizer...')
lb = joblib.load('ASL\output\lb.pkl')


# ### creating neural network model

# In[ ]:


class CustomCNN(nn.Module):
    def __init__(self):
        super(CustomCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, 5)
        self.conv2 = nn.Conv2d(16, 32, 5)
        self.conv3 = nn.Conv2d(32, 64, 3)
        self.conv4 = nn.Conv2d(64, 128, 5)
        self.fc1 = nn.Linear(128, 256)
        self.fc2 = nn.Linear(256, len(lb.classes_))
        self.pool = nn.MaxPool2d(2, 2)
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.pool(F.relu(self.conv3(x)))
        x = self.pool(F.relu(self.conv4(x)))
        bs, _, _, _ = x.shape
        x = F.adaptive_avg_pool2d(x, 1).reshape(bs, -1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x