--- a
+++ b/src/cnn/cnn.py
@@ -0,0 +1,72 @@
+import torch 
+import torch.nn as nn 
+
+class character_cnn(nn.Module):
+    def __init__(self, vocabulary, sequence_length, number_classes = 10):
+        super().__init__()
+
+        self.conv1 = nn.Sequential(nn.Conv1d(len(vocabulary)+1, 256, kernel_size = 7, padding = 0),
+                                   nn.ReLU(),
+                                   nn.MaxPool1d(3)
+                                   )
+
+        self.conv2 = nn.Sequential(nn.Conv1d(256, 256, kernel_size=7, padding=0),
+                                   nn.ReLU(),
+                                   nn.MaxPool1d(3)
+                                   )
+        
+        self.conv3 = nn.Sequential(nn.Conv1d(256, 256, kernel_size=3, padding=0),
+                                   nn.ReLU()
+                                   )
+
+        self.conv4 = nn.Sequential(nn.Conv1d(256, 256, kernel_size=3, padding=0),
+                                   nn.ReLU()
+                                   )
+        
+        input_shape = (1, len(vocabulary)+1, sequence_length)
+        self.output_dimension = self._get_conv_output(input_shape)
+
+        self.fc1 = nn.Sequential(
+            nn.Linear(self.output_dimension, 1024),
+            nn.ReLU(),
+            nn.Dropout(0.5)
+        )
+
+        self.fc2 = nn.Sequential(
+            nn.Linear(1024, 1024),
+            nn.ReLU(),
+            nn.Dropout(0.5)
+        )
+
+        self.fc3 = nn.Linear(1024, number_classes)
+
+
+        self.act = nn.Sigmoid()
+        
+    def _get_conv_output(self, shape):
+        x = torch.rand(shape)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = x.view(x.size(0), -1)
+        output_dimension = x.size(1)
+        return output_dimension
+
+    
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc1(x)
+        x = self.fc2(x)
+        x = self.fc3(x)
+        x = self.act(x)
+        return x
+
+
+
+
+