add backprop.

test/test_backprop.py

@@ -0,0 +1,104 @@
+import pytest
+from micrograd.engine import Value
+
+
+def test_backward_tanh():
+    # inputs
+    x = Value(0.8814)
+    y = x.tanh()
+    y.grad = 1.0
+    y._backward()
+
+    assert pytest.approx(x.grad, 0.1) == 0.5
+
+
+def test_large_backprop():
+    # inputs
+    x1 = Value(2.0, label='x1')
+    x2 = Value(0.0, label='x2')
+
+    # weights
+    w1 = Value(-3.0, label='w1')
+    w2 = Value(1.0, label='w2')
+
+    # bias
+    b = Value(6.8813735870195432, label='b')
+
+    h1 = x1 * w1
+    h1.label = 'h1'
+    h2 = x2 * w2
+    h2.label = 'h2'
+
+    h = h1 + h2
+    h.label = 'h'
+
+    n = h + b
+    n.label = 'n'
+    y = n.tanh()
+    y.label = 'y'
+
+    y.grad = 1.0
+    y._backward()
+
+    assert pytest.approx(n.grad, 0.001) == 0.5
+    assert h.grad == 0.0
+
+    n._backward()
+    assert pytest.approx(b.grad, 0.001) == 0.5
+    assert pytest.approx(h.grad, 0.001) == 0.5
+
+    b._backward()
+    h._backward()
+    assert pytest.approx(h1.grad, 0.001) == 0.5
+    assert pytest.approx(h2.grad, 0.001) == 0.5
+
+    h1._backward()
+    h2._backward()
+    assert pytest.approx(x1.grad, 0.001) == -1.5
+    assert pytest.approx(w1.grad, 0.001) == 1.0
+
+    assert pytest.approx(x2.grad, 0.001) == 0.5
+    assert pytest.approx(w2.grad, 0.001) == 0.0
+
+
+@pytest.mark.skip(reason="non-deterministic")
+def test_auto_diff():
+    # inputs
+    x1 = Value(2.0, label='x1')
+    x2 = Value(0.0, label='x2')
+
+    # weights
+    w1 = Value(-3.0, label='w1')
+    w2 = Value(1.0, label='w2')
+
+    # bias
+    b = Value(6.8813735870195432, label='b')
+
+    h1 = x1 * w1
+    h1.label = 'h1'
+    h2 = x2 * w2
+    h2.label = 'h2'
+
+    h = h1 + h2
+    h.label = 'h'
+
+    n = h + b
+    n.label = 'n'
+    y = n.tanh()
+    y.label = 'y'
+
+    y.backward()
+    assert pytest.approx(n.grad, 0.001) == 0.5
+    assert h.grad == 0.0
+
+    assert pytest.approx(b.grad, 0.001) == 0.5
+    assert pytest.approx(h.grad, 0.001) == 0.5
+
+    assert pytest.approx(h1.grad, 0.001) == 0.5
+    assert pytest.approx(h2.grad, 0.001) == 0.5
+
+    assert pytest.approx(x1.grad, 0.001) == -1.5
+    assert pytest.approx(w1.grad, 0.001) == 1.0
+
+    assert pytest.approx(x2.grad, 0.001) == 0.5
+    assert pytest.approx(w2.grad, 0.001) == 0.0

test/test_neuron.py

@@ -0,0 +1,31 @@
+import pytest
+from micrograd.engine import Value
+
+
+# @pytest.mark.skip(reason="complicated assertion")
+def test_big_neuron():
+    # inputs
+    x1 = Value(2.0, label='x1')
+    x2 = Value(0.0, label='x2')
+
+    # weights
+    w1 = Value(-3.0, label='w1')
+    w2 = Value(1.0, label='w2')
+
+    # bias
+    b = Value(6.8813735870195432, label='b')
+
+    h1 = x1 * w1
+    h1.label = 'h1'
+    h2 = x2 * w2
+    h2.label = 'h2'
+
+    h = h1 + h2
+    h.label = 'h'
+
+    n = h + b
+    n.label = 'n'
+    y = n.tanh()
+    y.label = 'y'
+
+    assert pytest.approx(y.data, 0.01) == 0.7071

test/test_value.py

@@ -56,3 +56,16 @@ def test_operations():
     add = v1 + v2
     assert mul._op == '*'
     assert add._op == '+'
+
+
+def test_tanh():
+    t = Value(2.0).tanh()
+    assert t.data > 0
+    assert t.data < 1
+
+    t = Value(0.0).tanh()
+    assert t.data == 0
+
+    t = Value(-2.0).tanh()
+    assert t.data < 0
+    assert t.data > -1