model3dcnn_pytorch.py

num_classes = 10

# Create CNN Model
class CNNModel(nn.Module):
    def __init__(self):
        super(CNNModel, self).__init__()
        
        self.conv_layer1 = self._conv_layer_set(3, 32)
        self.conv_layer2 = self._conv_layer_set(32, 64)
        self.fc1 = nn.Linear(2**3*64, 128)
        self.fc2 = nn.Linear(128, num_classes)
        self.relu = nn.LeakyReLU()
        self.batch=nn.BatchNorm1d(128)
        self.drop=nn.Dropout(p=0.15)        
        
    def _conv_layer_set(self, in_c, out_c):
        conv_layer = nn.Sequential(
        nn.Conv3d(in_c, out_c, kernel_size=(3, 3, 3), padding=0),
        nn.LeakyReLU(),
        nn.MaxPool3d((2, 2, 2)),
        )
        return conv_layer
    

    def forward(self, x):
        # Set 1
        out = self.conv_layer1(x)
        out = self.conv_layer2(out)
        out = out.view(out.size(0), -1)
        out = self.fc1(out)
        out = self.relu(out)
        out = self.batch(out)
        out = self.drop(out)
        out = self.fc2(out)
        
        return out

#Definition of hyperparameters
n_iters = 4500
num_epochs = n_iters / (len(train_x) / batch_size)
num_epochs = int(num_epochs)

# Create CNN
model = CNNModel()
#model.cuda()
print(model)

# Cross Entropy Loss 
error = nn.CrossEntropyLoss()

# SGD Optimizer
learning_rate = 0.001
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)