text: ## TODO: define the convolutional neural network architecture
  
  import torch
  from torch.autograd import Variable
  import torch.nn as nn
  import torch.nn.functional as F
  # can use the below import should you choose to initialize the weights of your Net
  import torch.nn.init as I
  
  
  class Net(nn.Module):
  
      def __init__(self):
          super(Net, self).__init__()
          
          ## TODO: Define all the layers of this CNN, the only requirements are:
          ## 1. This network takes in a square (same width and height), grayscale image as input
          ## 2. It ends with a linear layer that represents the keypoints
          ## it's suggested that you make this last layer output 136 values, 2 for each of the 68 keypoint (x, y) pairs
          
          # As an example, you've been given a convolutional layer, which you may (but don't have to) change:
          # 1 input image channel (grayscale), 32 output channels/feature maps, 5x5 square convolution kernel
          #self.conv1 = nn.Conv2d(1, 32, 5)
  
          
          ## Note that among the layers to add, consider including:
          # maxpooling layers, multiple conv layers, fully-connected layers, and other layers (such as dropout or batch normalization) to avoid overfitting
          #self.conv2 = nn.Conv2d(32, 64, 3)
          self.conv1 = nn.Conv2d(1, 16, 3)
          self.conv2 = nn.Conv2d(16, 32, 3)
          self.conv3 = nn.Conv2d(32, 64, 3)
          
          self.mp1 = nn.MaxPool2d(2, 2)
          self.mp2 = nn.MaxPool2d(2, 2)  
          self.mp3 = nn.MaxPool2d(2, 2)
          
          self.dropout = nn.Dropout(p=0.2)
          #self.fc1 = nn.Linear(64*53*53, 272)
          #self.fc1 = nn.Linear(64*54*54, 272)
          self.fc1 = nn.Linear(64*26*26, 272)
          self.fc2 = nn.Linear(272, 136)
              
          nn.init.xavier_uniform_(self.fc1.weight)
          nn.init.xavier_uniform_(self.fc2.weight)
          
          #self.bn1 = nn.BatchNorm1d(64*26*26)
          #self.bn2 = nn.BatchNorm1d(272)
          nn.init.xavier_uniform_(self.conv1.weight)
          nn.init.xavier_uniform_(self.conv2.weight)
          nn.init.xavier_uniform_(self.conv3.weight)
          
          #nn.init.kaiming_uniform_(self.conv1.weight.data)
          #nn.init.kaiming_uniform_(self.conv2.weight.data)   
          #nn.init.kaiming_uniform_(self.conv3.weight.data)
  
          
      def forward(self, x):
          ## TODO: Define the feedforward behavior of this model
          ## x is the input image and, as an example, here you may choose to include a pool/conv step:
          x = self.mp1(F.relu(self.conv1(x)))
          x = self.mp2(F.relu(self.conv2(x)))
          x = self.mp3(F.relu(self.conv3(x)))
          
          x = x.view(x.size(0), -1)
          
          #x = self.bn1(x)        
          x = F.relu(self.fc1(x))
          x = self.dropout(x)
          #ßßx = self.bn2(x)
          x = self.fc2(x)
  
                  
          # a modified x, having gone through all the layers of your model, should be returned
          return x
  
  