MNISTClassifier_05.py

    def forward(self,x):
        '''Forward pass, it is equal to PyTorch forward method. Here network computational graph is built
        Parameters:
        x (Tensor): A Tensor containing the input batch of the network
        Returns: 
        An one dimensional Tensor with probability array for each input image
        '''
        x=self.conv_layer_1(x)
        x=self.conv_layer_2(x)
        x=self.dropout1(x)
        x=torch.relu(self.fully_connected_1(x.view(x.size(0),-1)))
        x=F.leaky_relu(self.dropout2(x))
        return F.softmax(self.fully_connected_2(x), dim=1)
    
    def configure_optimizers(self):
        '''
        Returns:
        (Optimizer): Adam optimizer tuned wit model parameters
        '''
        return torch.optim.Adam(self.parameters())