feat: DeepNNLayer class

layers.py

@@ -22,8 +22,36 @@ class NNLayer:
         self.last_output = regularize(self.activation_func(res))
         return self.last_output
 
-    def backprop(self, error: np.ndarray):
+    def backprop(self, error: np.ndarray) -> np.ndarray:
         dW = np.outer(self.last_input, error)
         self.W -= self.lr * dW
         self.B -= self.lr * error
         return error @ self.W.T
+
+
+class DeepNNLayer:
+    def __init__(self,
+                 layers: list[int],
+                 lr: float,
+                 activation_func):
+        self.layers: list[NNLayer] = []
+        for i in range(len(layers) - 1):
+            self.layers.append(
+                    NNLayer(
+                        layers[i],
+                        layers[i+1],
+                        lr,
+                        activation_func)
+                )
+
+    def forward(self, v: np.ndarray) -> np.ndarray:
+        v_i = v
+        for layer in self.layers:
+            v_i = layer.forward(v_i)
+        return v_i
+
+    def backprop(self, error: np.ndarray) -> np.ndarray:
+        error_i = error
+        for layer in self.layers[::-1]:
+            error_i = layer.backprop(error_i)
+        return error_i