finish migrograd main functions.

test/test_backprop.py

@@ -2,16 +2,6 @@ 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')
@@ -107,3 +97,80 @@ def test_accumulation():
     b = a + a
     b.backward()
     assert a.grad == 2.0
+
+
+def test_backward_tanh():
+    # inputs
+    x = Value(0.8814)
+    y = x.tanh()
+    y.backward()
+
+    assert pytest.approx(x.grad, 0.1) == 0.5
+
+
+def test_backward_exp():
+    # inputs
+    x = Value(1.0)
+    y = x.exp()
+    y.backward()
+
+    assert pytest.approx(x.grad, 0.1) == 2.7
+
+
+def test_backward_pow():
+    # inputs
+    x = Value(1.0)
+    y = x ** 2
+    y.backward()
+
+    assert x.grad == 2.0
+
+
+def test_backward_div():
+    a = Value(4.0)
+    b = Value(2.0)
+    c = a / b
+    c.backward()
+    assert a.grad == 0.5
+
+
+def test_auto_diff_replace_tan_with_exp():
+    # 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'
+    e = (2*n).exp()
+    y = (e - 1) / (e + 1)
+    y.label = 'y'
+
+    y.backward()
+    assert pytest.approx(n.grad, 0.001) == 0.5
+
+    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_nn.py

@@ -0,0 +1,66 @@
+from micrograd.nn import Neuron, Layer, MLP
+from micrograd.engine import Value
+import pytest
+
+def test_init_neuron():
+    inputs = 2
+    x = [1.0, 0.0]
+    n = Neuron(inputs)
+    assert len(n.w) == inputs
+    y = n(x)
+    assert isinstance(y, Value)
+
+def test_mismatch_number():
+    with pytest.raises(ValueError):
+        x = [1.0, 0.0]
+        n = Neuron(7)
+        n(x)
+
+def test_large_n_in():
+    n_in = 100
+    x = [1.0] * n_in
+    n = Neuron(n_in)
+    y = n(x)
+    assert isinstance(y, Value)
+
+def test_well_known_weights():
+    x = [1.0, 0.0]
+    w = [Value(0.0), Value(0.0)]
+    b = Value(0.0)
+
+    n = Neuron(2)
+    n.w = w
+    n.b = b
+    y = n(x)
+
+    assert y.data == sum([x[0] * w[0], x[1] * w[1], b]).tanh().data
+
+
+
+def test_mlp():
+    x = [2.0, 3.0, -1.0]
+    n = MLP(3, [4, 4, 4])
+    y = n(x)
+    assert len(y) == 4
+
+def test_mlp_single_out():
+    x = [2.0, 3.0, -1.0]
+    n = MLP(3, [4, 4, 1])
+    y = n(x)
+    assert isinstance(y, Value)
+
+def test_sample_mlp():
+    n = MLP(3, [4, 4, 1])
+    xs = [
+        [2.0,  3.0, -1.0],
+        [3.0, -1.0,  0.5],
+        [0.5,  1.0,  1.0],
+        [1.0,  1.0,  1.0],
+    ]
+    y_true = [1.0, -1.0, -1.0, 1.0]
+    y_pred = [n(x) for x in xs]
+    mse = sum([(y_p - y_t)**2 for y_p, y_t in zip(y_pred, y_true)])
+
+def test_mlp_parameters():
+    n = MLP(3, [4, 4, 1])
+    assert len(n.parameters()) == 41

test/test_value.py

@@ -1,3 +1,4 @@
+import pytest
 from micrograd.engine import Value
 
 
@@ -11,21 +12,21 @@ def test_value_repr():
     assert "Value(data=2.0)" == repr(v)
 
 
-def test_value_add():
+def test_value_add_opt():
     v1 = Value(2.0)
     v2 = Value(4.0)
     assert (v1 + v2).data == 6.0
     assert "Value(data=6.0)" == repr(v1 + v2)
 
 
-def test_value_sub():
+def test_value_sub_opt():
     v1 = Value(2.0)
     v2 = Value(4.0)
     assert (v1 - v2).data == -2.0
     assert "Value(data=-2.0)" == repr(v1 - v2)
 
 
-def test_value_mul():
+def test_value_mul_opt():
     v1 = Value(2.0)
     v2 = Value(4.0)
     v3 = Value(-1.0)
@@ -33,6 +34,36 @@ def test_value_mul():
     assert (v1 * v3).data == -2.0
 
 
+def test_value_rmul_opt():
+    a = Value(2.0)
+    b = 2 * a
+    assert b.data == 4.0
+
+
+def test_value_pow_opt():
+    a = Value(2.0)
+    b = a ** 2
+    assert b.data == 4.0
+
+
+def test_value_exp_opt():
+    a = Value(1.0)
+    b = a.exp()
+    assert pytest.approx(b.data, 0.1) == 2.7
+
+
+def test_value_int_opt():
+    a = Value(2.0)
+    b = a - 1
+    assert b.data == 1.0
+
+
+def test_value_div_opt():
+    a = Value(2.0)
+    b = a / 2
+    assert b.data == 1.0
+
+
 def test_value_mul_add():
     v1 = Value(2.0)
     v2 = Value(4.0)