diff --git a/main.py b/main.py
index 3b5912f..0663d61 100644
--- a/main.py
+++ b/main.py
@@ -1,24 +1,26 @@
 import math
 import random
 
+def sigmoid(x: float) -> float:
+    return 1/(1 + math.exp(-x))
+
+
 class Neuron:
     def __init__(self, isize: int) -> None:
         self.isize = isize
-        self.weight = [random.uniform(0, 1) for _ in range(self.isize)]
-        self.bias = random.uniform(0, 1)
+        self.weight = [random.uniform(-1, 1) for _ in range(self.isize)]
+        self.bias = random.uniform(-1, 1)
 
-    def forward(self, inputs: list) -> float:
+    def forward(self, inputs: list[float]) -> float:
         assert len(inputs) == self.isize, "error: incorrect inputs number"
         total = sum(self.weight[i] * inputs[i] for i in range(self.isize)) + self.bias
-        return self.sigmoid(total)
-    
-    def sigmoid(self, x: float) -> float:
-        return 1/(1 + math.exp(-x))
+        return sigmoid(total)
 
-    # target needs to be between 0 and 1
-    def train(self, inputs: list, target: float, learning_rate: float = 0.1):
+    def train(self, inputs: list[float], target: float, learning_rate: float = 0.1):
+        assert len(inputs) == self.isize, "error: incorrect inputs number"
+        
         z = sum(self.weight[i] * inputs[i] for i in range(self.isize)) + self.bias
-        output = self.sigmoid(z)
+        output = sigmoid(z)
 
         error = output - target
         d_sigmoid = output * (1 - output)
@@ -28,3 +30,19 @@ class Neuron:
             self.weight[i] -= learning_rate * dz * inputs[i]
 
         self.bias -= learning_rate * dz
+
+class Layer:
+    def __init__(self, input_size, output_size):
+        self.size = output_size
+        self.neurons = [Neuron(output_size) for _ in range(input_size)]
+    
+    def forward(self, inputs):
+        return [n.forward(inputs) for n in self.neurons]
+
+    def train(self, inputs: list[float], targets: list[float], learning_rate: float = 0.1):
+        outputs = self.forward(inputs)
+
+        errors = [outputs[i] - targets[i] for i in range(self.size)]
+
+        for i in range(self.neurons):
+            self.neurons[i].train(inputs, errors[i], learning_rate)