implemented pooling layers

Author: devshah21

little-book-of-deep-learning/pooling_layers.py

@@ -0,0 +1,42 @@
+import numpy as np
+
+def max_pooling(input_matrix, size=2, stride=2):
+    input_height, input_width = input_matrix.shape
+    output_height = (input_height - size) // stride + 1
+    output_width = (input_width - size) // stride + 1
+    output = np.zeros((output_height, output_width))
+    
+    for y in range(0, output_height):
+        for x in range(0, output_width):
+            region = input_matrix[y*stride:y*stride+size, x*stride:x*stride+size]
+            output[y, x] = np.max(region)
+    
+    return output
+
+def average_pooling(input_matrix, size=2, stride=2):
+    input_height, input_width = input_matrix.shape
+    output_height = (input_height - size) // stride + 1
+    output_width = (input_width - size) // stride + 1
+    output = np.zeros((output_height, output_width))
+    
+    for y in range(0, output_height):
+        for x in range(0, output_width):
+            region = input_matrix[y*stride:y*stride+size, x*stride:x*stride+size]
+            output[y, x] = np.mean(region)
+    
+    return output
+
+# Example usage
+if __name__ == "__main__":
+    input_matrix = np.array([
+        [1, 3, 2, 4],
+        [5, 6, 7, 8],
+        [9, 1, 2, 0],
+        [3, 4, 5, 6]
+    ])
+    
+    max_pooled = max_pooling(input_matrix)
+    average_pooled = average_pooling(input_matrix)
+    
+    print("Max Pooled Output:\n", max_pooled)
+    print("Average Pooled Output:\n", average_pooled)