text: '''
  Author: Jeremy King
  Version: 0.01a
  Change Log:
  *****0.01a*****
  Initial revision to establish basic CNN for image recognition
  '''
  import torch as pt
  import pandas as pd
  import numpy as np
  import pillow as pil
  import torchvision
  import torchvision.transforms as transforms
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.optim as optim
  
  class nueral_net(nn.Module):
  
  
      def __init__(self):
          super(Net, self).__init__()
          self.conv1 = nn.Conv2d(3, 6, 5)
          self.pool = nn.MaxPool2d(2, 2)
          self.conv2 = nn.Conv2d(6, 16, 5)
          self.fc1 = nn.Linear(16 * 5 * 5, 120)
          self.fc2 = nn.Linear(120, 84)
          self.fc3 = nn.Linear(84, 10)
  
      def forward(self, x):
          x = self.pool(F.relu(self.conv1(x)))
          x = self.pool(F.relu(self.conv2(x)))
          x = x.view(-1, 16 * 5 * 5)
          x = F.relu(self.fc1(x))
          x = F.relu(self.fc2(x))
          x = self.fc3(x)
          return x
  
      criterion = nn.CrossEntropyLoss()
      optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
      
  net = nueral_net
  
  def trainer(train_set, test_set):
      
      trainloader = torch.utils.data.DataLoader(train_set, batch_size=4)
      
      testloader = torch.utils.data.DataLoader(test_set, batch_size=4, shuffle=False, num_workers=2)
  
      for epoch in range(2):
          
          running_loss = 0.0
          for i, data in enumerate(trainloader):
              inputs, labels = data
  
              optimizer.zero_grad()
  
              outputs = net(inputs)
              loss = criterion(outputs, labels)
              loss.backward()
              optimizer.step()
  
              running_loss += loss.item()
              if i % 2000 == 1999:
                  print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
                  running_loss = 0.0
      print('Finished Traning!')
  
      PATH = '../models'
      torch,save(net.state_dict(), PATH)
  
  
  train_set = 
  test_set = 
  