import model

.gitignore

@@ -11,6 +11,6 @@ ipython_config.py
 
 /train/
 /test/
-# Remove previous ipynb_checkpoints
+
-#   git rm -r .ipynb_checkpoints/
+__pycache__/
 

test.py

@@ -1,36 +1,11 @@
 import csv
 import torch
-import torch.nn.functional as F
 
-from torch import nn
 from torchvision import transforms, datasets
 from torch.utils.data import DataLoader
+from train import Model
 
     
-class Model(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv1 = nn.Conv2d(3, 32, 3, 1)
-        self.batchnorm1 = nn.BatchNorm2d(32)
-        self.conv2 = nn.Conv2d(32, 64, 3, 1)
-        self.batchnorm2 = nn.BatchNorm2d(64)
-        self.conv3 = nn.Conv2d(64, 128, 3, 1)
-        self.fc1 = nn.Linear(128*26*26, 128)
-        self.fc2 = nn.Linear(128, 2)
-
-    def forward(self, x):
-        x = F.relu(self.batchnorm1(self.conv1(x)))
-        x = F.max_pool2d(x, 2, 2)
-        x = F.relu(self.batchnorm2(self.conv2(x)))
-        x = F.max_pool2d(x, 2, 2)
-        x = F.relu(self.conv3(x))
-        x = F.max_pool2d(x, 2, 2)
-        x = x.view(-1, 128*26*26)
-        x = F.relu(self.fc1(x))
-        x = self.fc2(x)
-        
-        return F.log_softmax(x, dim=1)
-    
 def get_data(IMG_SIZE: int, BATCH_SIZE: int):
     testTransformer = transforms.Compose([
         transforms.Resize(size = (IMG_SIZE, IMG_SIZE), antialias = True),