modified: neural_network/artificial_neural_network.py

Author: AHuzail

neural_network/artificial_neural_network.py

@@ -6,17 +6,13 @@ class SimpleANN:
     Simple Artificial Neural Network (ANN)
 
     - Feedforward Neural Network with 1 hidden layer and Sigmoid activation.
-    - Uses Gradient Descent for backpropagation and Mean Squared Error (MSE) as the loss function.
+    - Uses Gradient Descent for backpropagation and Mean Squared Error (MSE) 
+      as the loss function.  
     - Example demonstrates solving the XOR problem.
     """
 
-    def __init__(
-        self,
-        input_size: int,
-        hidden_size: int,
-        output_size: int,
-        learning_rate: float = 0.1,
-    ) -> None:
+    def __init__(self, input_size: int, hidden_size: int, output_size: int, 
+                 learning_rate: float = 0.1) -> None:
         """
         Initialize the neural network with random weights and biases.
 
@@ -89,17 +85,16 @@ def feedforward(self, inputs: np.ndarray) -> np.ndarray:
         >>> ann.feedforward(inputs).shape
         (2, 1)
         """
-        self.hidden_input = np.dot(inputs, self.weights_input_hidden) + self.bias_hidden
+        self.hidden_input = (np.dot(inputs, self.weights_input_hidden) + 
+                             self.bias_hidden)
         self.hidden_output = self.sigmoid(self.hidden_input)
-        self.final_input = (
-            np.dot(self.hidden_output, self.weights_hidden_output) + self.bias_output
-        )
+        self.final_input = (np.dot(self.hidden_output, self.weights_hidden_output) + 
+                            self.bias_output)
         self.final_output = self.sigmoid(self.final_input)
         return self.final_output
 
-    def backpropagation(
-        self, inputs: np.ndarray, targets: np.ndarray, outputs: np.ndarray
-    ) -> None:
+    def backpropagation(self, inputs: np.ndarray, targets: np.ndarray, 
+                        outputs: np.ndarray) -> None:
         """
         Perform backpropagation to adjust the weights and biases.
 
@@ -120,21 +115,18 @@ def backpropagation(
         hidden_error = output_gradient.dot(self.weights_hidden_output.T)
         hidden_gradient = hidden_error * self.sigmoid_derivative(self.hidden_output)
 
-        self.weights_hidden_output += (
-            self.hidden_output.T.dot(output_gradient) * self.learning_rate
-        )
-        self.bias_output += (
-            np.sum(output_gradient, axis=0, keepdims=True) * self.learning_rate
-        )
-
-        self.weights_input_hidden += inputs.T.dot(hidden_gradient) * self.learning_rate
-        self.bias_hidden += (
-            np.sum(hidden_gradient, axis=0, keepdims=True) * self.learning_rate
-        )
-
-    def train(
-        self, inputs: np.ndarray, targets: np.ndarray, epochs: int = 10000
-    ) -> None:
+        self.weights_hidden_output += (self.hidden_output.T.dot(output_gradient) * 
+                                        self.learning_rate)
+        self.bias_output += (np.sum(output_gradient, axis=0, keepdims=True) * 
+                             self.learning_rate)
+
+        self.weights_input_hidden += (inputs.T.dot(hidden_gradient) * 
+                                       self.learning_rate)
+        self.bias_hidden += (np.sum(hidden_gradient, axis=0, keepdims=True) * 
+                             self.learning_rate)
+
+    def train(self, inputs: np.ndarray, targets: np.ndarray, 
+              epochs: int = 10000) -> None:
         """
         Train the neural network on the given input and target data.