47 lines
1.2 KiB
Python
47 lines
1.2 KiB
Python
import torch
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import pytest
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from pina.model import FeedForward
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data = torch.rand((20, 3))
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input_vars = 3
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output_vars = 4
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def test_constructor():
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FeedForward(input_vars, output_vars)
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FeedForward(input_vars, output_vars, inner_size=10, n_layers=20)
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FeedForward(input_vars, output_vars, layers=[10, 20, 5, 2])
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FeedForward(input_vars,
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output_vars,
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layers=[10, 20, 5, 2],
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func=torch.nn.ReLU)
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FeedForward(input_vars,
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output_vars,
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layers=[10, 20, 5, 2],
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func=[torch.nn.ReLU, torch.nn.ReLU, None, torch.nn.Tanh])
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def test_constructor_wrong():
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with pytest.raises(RuntimeError):
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FeedForward(input_vars,
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output_vars,
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layers=[10, 20, 5, 2],
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func=[torch.nn.ReLU, torch.nn.ReLU])
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def test_forward():
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dim_in, dim_out = 3, 2
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fnn = FeedForward(dim_in, dim_out)
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output_ = fnn(data)
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assert output_.shape == (data.shape[0], dim_out)
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def test_backward():
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dim_in, dim_out = 3, 2
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fnn = FeedForward(dim_in, dim_out)
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data.requires_grad = True
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output_ = fnn(data)
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l=torch.mean(output_)
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l.backward()
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assert data._grad.shape == torch.Size([20,3])
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