def test_dim_None(self, RandomizedRectifierLayer):
import lasagne
l_in = lasagne.layers.input.InputLayer((None, 3, 28, 28))
layer = RandomizedRectifierLayer(l_in)
input = np.ones((3, 3, 28, 28)).astype(theano.config.floatX)
out = layer.get_output_for(input).eval()
assert np.allclose(out, 1.0)
def test_dim_None(self, RandomizedRectifierLayer):
import lasagne
l_in = lasagne.layers.input.InputLayer((None, 3, 28, 28))
layer = RandomizedRectifierLayer(l_in)
input = np.ones((3, 3, 28, 28)).astype(theano.config.floatX)
out = layer.get_output_for(input).eval()
assert np.allclose(out, 1.0)
def test_get_output_for(self, RandomizedRectifierLayer):
input_shape = (3, 3, 28, 28)
# ensure slope never exceeds [lower,upper)
input = np.random.randn(*input_shape).astype(theano.config.floatX)
layer = RandomizedRectifierLayer(input_shape, shared_axes=0)
self.assert_between(layer, input, layer.get_output_for(input).eval())
# from here on, we want to check parameter sharing
# this is easier to check if the input is all ones
input = np.ones(input_shape).astype(theano.config.floatX) * -1
# default: parameters shared along all but 2nd axis
layer = RandomizedRectifierLayer(input_shape)
out = layer.get_output_for(input).eval()
assert [
np.allclose(out.var(axis=a), 0)
for a in range(4)
] == [True, False, True, True]
# share across all axes (single slope)
layer = RandomizedRectifierLayer(input_shape, shared_axes='all')
out = layer.get_output_for(input).eval()
assert [
np.allclose(out.var(axis=a), 0)
for a in range(4)
] == [True, True, True, True]
# share across 1st axis
layer = RandomizedRectifierLayer(input_shape, shared_axes=0)
out = layer.get_output_for(input).eval()
assert [
np.allclose(out.var(axis=a), 0)
for a in range(4)
] == [True, False, False, False]
# share across 1st and 4th axes
layer = RandomizedRectifierLayer(input_shape, shared_axes=(0, 3))
out = layer.get_output_for(input).eval()
assert [
np.allclose(out.var(axis=a), 0)
for a in range(4)
] == [True, False, False, True]
def test_get_output_for(self, RandomizedRectifierLayer):
input_shape = (3, 3, 28, 28)
# ensure slope never exceeds [lower,upper)
input = np.random.randn(*input_shape).astype(theano.config.floatX)
layer = RandomizedRectifierLayer(input_shape, shared_axes=0)
self.assert_between(layer, input, layer.get_output_for(input).eval())
# from here on, we want to check parameter sharing
# this is easier to check if the input is all ones
input = np.ones(input_shape).astype(theano.config.floatX) * -1
# default: parameters shared along all but 2nd axis
layer = RandomizedRectifierLayer(input_shape)
out = layer.get_output_for(input).eval()
assert [np.allclose(out.var(axis=a), 0)
for a in range(4)] == [True, False, True, True]
# share across all axes (single slope)
layer = RandomizedRectifierLayer(input_shape, shared_axes='all')
out = layer.get_output_for(input).eval()
assert [np.allclose(out.var(axis=a), 0)
for a in range(4)] == [True, True, True, True]
# share across 1st axis
layer = RandomizedRectifierLayer(input_shape, shared_axes=0)
out = layer.get_output_for(input).eval()
assert [np.allclose(out.var(axis=a), 0)
for a in range(4)] == [True, False, False, False]
# share across 1st and 4th axes
layer = RandomizedRectifierLayer(input_shape, shared_axes=(0, 3))
out = layer.get_output_for(input).eval()
assert [np.allclose(out.var(axis=a), 0)
for a in range(4)] == [True, False, False, True]
def test_deterministic(self, RandomizedRectifierLayer):
input = np.ones((3, 3, 28, 28)).astype(theano.config.floatX) * -1
layer = RandomizedRectifierLayer(input.shape, lower=0.4, upper=0.6)
out = layer.get_output_for(input, deterministic=True)
assert np.allclose(out, -0.5)
def test_low_eq_high(self, RandomizedRectifierLayer):
input = np.ones((3, 3, 28, 28)).astype(theano.config.floatX) * -1
layer = RandomizedRectifierLayer(input.shape, lower=0.5, upper=0.5)
out = layer.get_output_for(input)
assert np.allclose(out, -0.5)
def test_nomod_positive(self, RandomizedRectifierLayer):
input = np.ones((3, 3, 28, 28)).astype(theano.config.floatX)
layer = RandomizedRectifierLayer(input.shape)
out = layer.get_output_for(input).eval()
assert np.allclose(out, 1.0)
def test_high_low(self, RandomizedRectifierLayer):
with pytest.raises(ValueError):
RandomizedRectifierLayer((None, 3, 28, 28), lower=0.9, upper=0.1)
def test_deterministic(self, RandomizedRectifierLayer):
input = np.ones((3, 3, 28, 28)).astype(theano.config.floatX) * -1
layer = RandomizedRectifierLayer(input.shape, lower=0.4, upper=0.6)
out = layer.get_output_for(theano.tensor.constant(input),
deterministic=True).eval()
assert np.allclose(out, -0.5)
def test_low_eq_high(self, RandomizedRectifierLayer):
input = np.ones((3, 3, 28, 28)).astype(theano.config.floatX) * -1
layer = RandomizedRectifierLayer(input.shape, lower=0.5, upper=0.5)
out = layer.get_output_for(theano.tensor.constant(input)).eval()
assert np.allclose(out, -0.5)
def test_nomod_positive(self, RandomizedRectifierLayer):
input = np.ones((3, 3, 28, 28)).astype(theano.config.floatX)
layer = RandomizedRectifierLayer(input.shape)
out = layer.get_output_for(input).eval()
assert np.allclose(out, 1.0)