feat: add learning program

main.py

@@ -1 +1,53 @@
-import network
+from network import *
+
+def data(size:int, max_val: int):
+    def int_to_bits(n: int):
+        return [(n >> i) & 1 
+            for i in reversed(range(size))
+        ]
+   
+    return [(int_to_bits(i),[i / max_val]) 
+        for i in range(max_val + 1)
+    ]
+
+def train_network(network: NeuralNetwork, epochs=10000, learning_rate=0.1, 
+                  verbose: bool = False, size_data: int = 8, max_val: int = 255):
+    
+    train_data = data(size_data, max_val)
+    
+    for epoch in range(epochs):
+        for bits, target in train_data:
+            network.backward(bits, target, learning_rate)
+
+        if verbose and epoch % 100 == 0:
+            output = network.forward(bits)[0]
+            loss = (output - target[0]) ** 2
+
+            print(f"Epoch: {epoch}, Loss: {loss:.6f}")
+
+def main():
+    size = 8
+    max_val = (1 << size) - 1
+
+    network = NeuralNetwork([8, 16, 1])
+
+    print("Start training...")
+    train_network(network, verbose=True, size_data=size, epochs=45_000)
+    print("End training...")
+
+    while True:
+        string = input("Enter 8 bit number (ex: 01101001) or 'quit' to close: ") \
+            .strip().lower()
+        
+        if (string == 'quit'): break
+        if (len(string) != 8 or any (char not in '01' for char in string)):
+            print("Error: please enter exactly 8 bits (only 0 or 1).") 
+            continue
+        
+        bits_input = [int(char) for char in string]
+        output = network.forward(bits_input)[0] * max_val
+
+        print(f"Estimated value: {output} (approx: {round(output)})\n")
+
+if __name__ == "__main__":
+    main()

network.py

@@ -30,14 +30,16 @@ class Neuron:
         # last output sigmoid(z)
         self.last_output = 0
 
-    def forward(self, x):
+    def forward(self, x, activate=True):
         """
         x               : list of input values to the neuron
         """
         # computes the weighted sum of inputs and add the bias
         self.z = sum(w * xi for w, xi in zip(self.weight, x)) + self.bias
         # normalize the output between 0 and 1
-        self.last_output = sigmoid(self.z)
+        if activate: self.last_output = sigmoid(self.z)
+        else: self.last_output = self.z
+
         return self.last_output
     
     # adjust weight and bias of neuron
@@ -54,6 +56,9 @@ class Neuron:
         dy_dz = sigmoid_deriv(self.z)
         # dz/dw = x
         dz_dw = x
+
+        assert len(dz_dw) >= self.isize, "too many value for input size"
+
         # dz/db = 1
         dz_db = 1
 
@@ -78,10 +83,10 @@ class Layer:
         # list of neurons
         self.neurons = [Neuron(input_size) for _ in range(output_size)]
 
-    def forward(self, inputs):
+    def forward(self, inputs, activate=True):
         self.inputs = inputs
         #  give the same inputs to each neuron in the layer
-        return [neuron.forward(inputs) for neuron in self.neurons]
+        return [neuron.forward(inputs, activate) for neuron in self.neurons]
 
     # adjust weight and bias of the layer (all neurons)
     def backward(self, dcost_dy_list, learning_rate=0.1):
@@ -105,8 +110,9 @@ class NeuralNetwork:
 
     def forward(self, inputs):
         output = inputs
-        for layer in self.layers:
+        for i, layer in enumerate(self.layers):
-            output = layer.forward(output)
+            activate = (i != len(self.layers) - 1)  # deactivate sigmoid latest neuron
+            output = layer.forward(output, activate=activate)
         return output
 
     def backward(self, inputs, targets, learning_rate=0.1):
@@ -117,7 +123,7 @@ class NeuralNetwork:
         output = self.forward(inputs)
 
         # computes the initial gradient of the cost function for each neuron
-        # by using Mean Squared Error: dC/dy = 2 * (output - target)
+        # by using Mean Squared Error's derivate: dC/dy = 2 * (output - target)
         dcost_dy_list = [2 * (o - t) for o, t in zip(output, targets)]
 
         grad = dcost_dy_list