def test_invalid_pad(self, DummyInputLayer):
from lasagne.layers.conv import Conv1DLayer
input_layer = DummyInputLayer((1, 2, 3))
with pytest.raises(TypeError) as exc:
layer = Conv1DLayer(input_layer, num_filters=16, filter_size=(3,),
pad='_nonexistent_mode')
assert "iterable of int" in exc.value.args[0]
with pytest.raises(NotImplementedError) as exc:
layer = Conv1DLayer(input_layer, num_filters=16, filter_size=(4,),
pad='same')
assert "requires odd filter size" in exc.value.args[0]
def test_init_none_nonlinearity_bias(self, DummyInputLayer):
from lasagne.layers.conv import Conv1DLayer
input_layer = DummyInputLayer((1, 2, 3))
layer = Conv1DLayer(input_layer, num_filters=16, filter_size=(3,),
nonlinearity=None, b=None)
assert layer.nonlinearity == lasagne.nonlinearities.identity
assert layer.b is None
def test_invalid_border_mode(self, DummyInputLayer):
from lasagne.layers.conv import Conv1DLayer
input_layer = DummyInputLayer((1, 2, 3))
with pytest.raises(RuntimeError) as exc:
layer = Conv1DLayer(input_layer,
num_filters=16,
filter_size=(3, ),
border_mode='_nonexistent_mode')
assert "Invalid border mode" in exc.value.args[0]
def test_defaults(self, DummyInputLayer, input, kernel, output, kwargs):
b, c, w = input.shape.eval()
input_layer = DummyInputLayer((b, c, w))
try:
from lasagne.layers.conv import Conv1DLayer
layer = Conv1DLayer(input_layer,
num_filters=kernel.shape[0],
filter_size=kernel.shape[2],
W=kernel,
**kwargs)
actual = layer.get_output_for(input).eval()
assert actual.shape == output.shape
assert actual.shape == layer.output_shape
assert np.allclose(actual, output)
except NotImplementedError:
pass