diff --git a/micrograd/activation_functions.py b/micrograd/activation_functions.py
index 3f682de6..ee31b4a1 100644
--- a/micrograd/activation_functions.py
+++ b/micrograd/activation_functions.py
@@ -17,18 +17,9 @@ def linear(value: "Value") -> "Value":
     @staticmethod
     def sigmoid(value: "Value") -> "Value":
         from .engine import Value
-        self = value
-        out = Value(1 / (1 + (-value).exp()), (self,), "Sigmoid")
-
-        # Value(0 if self.data < 0 else self.data, (self,), "Sigmoid")
-        def _backward():
-            self.grad += (out.data > 0) * out.grad
-
-        out._backward = _backward
-        return out
-
-        # return 1 / (1 + (-value).exp())
+        return value.sigmoid()
 
     @staticmethod
     def tanh(value: "Value") -> "Value":
-        return (value.exp() - (-value).exp()) / (value.exp() + (-value).exp())
+        from .engine import Value
+        return value.tanh()
diff --git a/micrograd/engine.py b/micrograd/engine.py
index 2c03e8cb..67b0d407 100644
--- a/micrograd/engine.py
+++ b/micrograd/engine.py
@@ -8,26 +8,13 @@ class Value:
     """stores a single scalar value and its gradient"""
 
     def __init__(self, data: Number | 'Value', _children: tuple = (), _op: str = ""):
-        if isinstance(data, Value):
-            data.copy(data, _children, _op)
-        else:
-            self.data: Number = data
-            self.grad: float = 0.0
-            # internal variables used for autograd graph construction
-            self._backward: Callable[[], None] = lambda: None
-            self._prev: Set["Value"] = set(_children)
-            self._op = _op  # the op that produced this node, for graphviz / debugging / etc
-
-    def copy(self, value: 'Value', _children: tuple = (), _op: str = ""):
-        self.data = value.data
-        self.grad = value.grad
-        self._backward = value._backward
-        self._prev = value._prev
-        self._op = value._op
-        if _children:
-            self._prev: Set["Value"] = set(_children)
-        if _op:
-            self._op = _op
+
+        self.data: Number = data
+        self.grad: float = 0.0
+        # internal variables used for autograd graph construction
+        self._backward: Callable[[], None] = lambda: None
+        self._prev: Set["Value"] = set(_children)
+        self._op = _op  # the op that produced this node, for graphviz / debugging / etc
 
     def __add__(self, other: Number | "Value") -> "Value":
         other = other if isinstance(other, Value) else Value(other)
@@ -41,6 +28,19 @@ def _backward():
 
         return out
 
+    def __abs__(self) -> "Value":
+        self.data = abs(self.data)
+
+        return self
+
+    def __lt__(self, other: Number | "Value") -> bool:
+        other = other if isinstance(other, Value) else Value(other)
+        return self.data < other.data
+
+    def __gt__(self, other: Number | "Value") -> bool:
+        other = other if isinstance(other, Value) else Value(other)
+        return self.data > other.data
+
     def __mul__(self, other: Number | "Value"):
         other = other if isinstance(other, Value) else Value(other)
         out = Value(self.data * other.data, (self, other), "*")
@@ -76,6 +76,26 @@ def _backward():
 
         return out
 
+    def sigmoid(self) -> "Value":
+        out = Value(1 / (1 + (-1 * self).exp()), (self,), "Sigmoid")
+
+        def _backward():
+            self.grad += (out.data > 0) * out.grad
+
+        out._backward = _backward
+
+        return out
+
+    def tanh(self) -> "Value":
+        out = Value((self.exp() - (-self).exp()) / (self.exp() + (-self).exp()), (self,), "tanh")
+
+        def _backward():
+            self.grad += (out.data > 0) * out.grad
+
+        out._backward = _backward
+
+        return out
+
     def backward(self) -> None:
 
         # topological order all the children in the graph
diff --git a/test/test_trainer.py b/test/test_trainer.py
index 64556f77..5d81d260 100644
--- a/test/test_trainer.py
+++ b/test/test_trainer.py
@@ -2,20 +2,22 @@
 
 
 def test_relu():
-    assert Activation.relu(Value(3.0)).data == 3.0
-    assert Activation.relu(Value(-2.0)).data == 0.0
+    assert Activation.relu(Value(3.0)).data == Value(3.0).data
+    assert Activation.relu(Value(-2.0)).data == Value(0.0).data
 
 
 def test_linear():
-    assert Activation.linear(Value(3.0)).data == 3.0
-    assert Activation.linear(Value(-2.0)).data == -2.0
-
-#
-# def test_sigmoid():
-#     sigmoid_value = Activation.sigmoid(Value(0.0)).data
-#     assert abs(sigmoid_value - 0.5) < 1e-6
-#
-#
-# def test_tanh():
-#     tanh_value = Activation.tanh(Value(0.0)).data
-#     assert abs(tanh_value - 0.0) < 1e-6
+    assert Activation.linear(Value(3.0)).data == Value(3.0).data
+    assert Activation.linear(Value(-2.0)).data == Value(-2.0).data
+
+
+def test_sigmoid():
+    sigmoid_value = Activation.sigmoid(Value(0.0))
+    assert isinstance(sigmoid_value, Value)
+    assert abs(sigmoid_value - 0.5) < 1e-6
+
+
+def test_tanh():
+    tanh_value = Activation.tanh(Value(0.0))
+    assert isinstance(tanh_value, Value)
+    assert abs(tanh_value) < 1e-6