✌🏻 add model script and saved weights

models/labels.txt

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+alif
+alif mad aa
+ayn
+baa
+bari yaa
+cheey
+choti yaa
+daal
+dhaal
+faa
+gaaf
+ghain
+haa1
+haa2
+haa3
+hamza
+jeem
+kaaf
+khaa
+laam
+meem
+noon
+noonghunna

models/model.py

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+# Import PyTorch
+import torch
+from torch import nn
+
+
+# Import matplotlib for visualization
+import matplotlib.pyplot as plt
+
+torch.manual_seed(42)
+
+class TinyVGG(nn.Module):
+    """
+    Model architecture copying TinyVGG from: 
+    https://poloclub.github.io/cnn-explainer/
+    """
+    def __init__(self, input_shape: int, hidden_units: int, output_shape: int) -> None:
+        super().__init__()
+        self.conv_block_1 = nn.Sequential(
+            nn.Conv2d(in_channels=input_shape, 
+                      out_channels=hidden_units, 
+                      kernel_size=3, # how big is the square that's going over the image?
+                      stride=1, # default
+                      padding=1), # options = "valid" (no padding) or "same" (output has same shape as input) or int for specific number 
+            nn.ReLU(),
+            nn.Conv2d(in_channels=hidden_units, 
+                      out_channels=hidden_units,
+                      kernel_size=3,
+                      stride=1,
+                      padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2,
+                         stride=2) # default stride value is same as kernel_size
+        )
+        self.conv_block_2 = nn.Sequential(
+            nn.Conv2d(hidden_units, hidden_units, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(hidden_units, hidden_units, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2)
+        )
+        self.classifier = nn.Sequential(
+            nn.Flatten(),
+            # Where did this in_features shape come from? 
+            # It's because each layer of our network compresses and changes the shape of our inputs data.
+            nn.Linear(in_features=hidden_units*16*16,
+                      out_features=output_shape)
+        )
+    
+    def forward(self, x: torch.Tensor):
+        x = self.conv_block_1(x)
+        # print(x.shape)
+        x = self.conv_block_2(x)
+        # print(x.shape)
+        x = self.classifier(x)
+        # print(x.shape)
+        return x
+        # return self.classifier(self.conv_block_2(self.conv_block_1(x))) # <- leverage the benefits of operator fusion
+