Fix inheritance bug

micrograd/engine.py

@@ -11,8 +11,8 @@ def __init__(self, data, _children=(), _op=''):
         self._op = _op # the op that produced this node, for graphviz / debugging / etc
 
     def __add__(self, other):
-        other = other if isinstance(other, Value) else Value(other)
-        out = Value(self.data + other.data, (self, other), '+')
+        other = other if isinstance(other, self.__class__) else self.__class__(other)
+        out = self.__class__(self.data + other.data, (self, other), '+')
 
         def _backward():
             self.grad += out.grad
@@ -22,8 +22,8 @@ def _backward():
         return out
 
     def __mul__(self, other):
-        other = other if isinstance(other, Value) else Value(other)
-        out = Value(self.data * other.data, (self, other), '*')
+        other = other if isinstance(other, self.__class__) else self.__class__(other)
+        out = self.__class__(self.data * other.data, (self, other), '*')
 
         def _backward():
             self.grad += other.data * out.grad
@@ -34,7 +34,7 @@ def _backward():
 
     def __pow__(self, other):
         assert isinstance(other, (int, float)), "only supporting int/float powers for now"
-        out = Value(self.data**other, (self,), f'**{other}')
+        out = self.__class__(self.data**other, (self,), f'**{other}')
 
         def _backward():
             self.grad += (other * self.data**(other-1)) * out.grad
@@ -43,7 +43,7 @@ def _backward():
         return out
 
     def relu(self):
-        out = Value(0 if self.data < 0 else self.data, (self,), 'ReLU')
+        out = self.__class__(0 if self.data < 0 else self.data, (self,), 'ReLU')
 
         def _backward():
             self.grad += (out.data > 0) * out.grad

test/test_inheritance.py

@@ -0,0 +1,27 @@
+import micrograd
+import math
+
+def test_new_method():
+    """
+    Creates a new class that inherits with 
+        same parameters
+        new method
+    """
+    class Value(micrograd.Value):
+
+        def tanh(self):
+            x = self.data
+            temp = math.exp(2*x)
+            t = (temp - 1) / (temp + 1)
+            out = self.__class__(data=t, _children = (self,), label = "tanh")
+            return out
+
+    n = Value(data = 0.6, label = "neuron")
+    is_successful = True
+    try:
+        n.tanh()
+    except AttributeError as error:
+        is_successful = False
+        raise(error)
+
+    assert is_successful