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