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