def test_non_zero():
'''Test masking with non-zero mask value'''
layer = core.Masking(5)
func = K.function([layer.input], [layer.get_output_mask()])
input_data = np.array([[[1, 1], [2, 1], [3, 1], [5, 5]],
[[1, 5], [5, 0], [0, 0], [0, 0]]],
dtype=np.int32)
output = func([input_data])[0]
expected = np.array([[1, 1, 1, 0], [1, 1, 1, 1]])
assert np.all(output == expected), 'Output not as expected'
def test_non_zero(self):
"""Test masking with non-zero mask value"""
layer = core.Masking(5)
func = theano.function([layer.input], layer.get_output_mask())
self.assertTrue(np.all(
# get mask for this input, if not all the values are 5, shouldn't masked
func(np.array([[[1, 1], [2, 1], [3, 1], [5, 5]],
[[1, 5], [5, 0], [0, 0], [0, 0]]], dtype=np.int32)) ==
# This is the expected output mask, one dimension less
np.array([[1, 1, 1, 0], [1, 1, 1, 1]])))
def test_non_zero_output(self):
"""Test output of masking layer with non-zero mask value"""
layer = core.Masking(5)
func = theano.function([layer.input], layer.get_output())
self.assertTrue(np.all(
# get output for this input, replace padding with 0
func(np.array([[[1, 1], [2, 1], [3, 1], [5, 5]],
[[1, 5], [5, 0], [0, 0], [0, 0]]], dtype=np.int32)) ==
# This is the expected output
np.array([[[1, 1], [2, 1], [3, 1], [0, 0]],
[[1, 5], [5, 0], [0, 0], [0, 0]]])))
def test_sequences(self):
"""Test masking sequences with zeroes as padding"""
# integer inputs, one per timestep, like embeddings
layer = core.Masking()
func = theano.function([layer.input], layer.get_output_mask())
self.assertTrue(np.all(
# get mask for this input
func(np.array([[[1], [2], [3], [0]],
[[0], [4], [5], [0]]], dtype=np.int32)) ==
# This is the expected output mask, one dimension less
np.array([[1, 1, 1, 0], [0, 1, 1, 0]])))
def test_non_zero(self):
"""Test masking with non-zero mask value"""
if K._BACKEND == "tensorflow":
return
layer = core.Masking(5)
func = K.function([layer.input], [layer.get_output_mask()])
input_data = np.array([[[1, 1], [2, 1], [3, 1], [5, 5]],
[[1, 5], [5, 0], [0, 0], [0, 0]]],
dtype=np.int32)
output = func([input_data])[0]
expected = np.array([[1, 1, 1, 0], [1, 1, 1, 1]])
assert (np.all(output == expected))
def test_non_zero_output():
"""Test output of masking layer with non-zero mask value"""
layer = core.Masking(5)
func = K.function([layer.input], [layer.get_output()])
input_data = np.array([[[1, 1], [2, 1], [3, 1], [5, 5]],
[[1, 5], [5, 0], [0, 0], [0, 0]]],
dtype=np.int32)
output = func([input_data])[0]
expected = np.array([[[1, 1], [2, 1], [3, 1], [0, 0]],
[[1, 5], [5, 0], [0, 0], [0, 0]]])
assert np.all(output == expected) , "Output not as expected"
def test_sequences():
'''Test masking sequences with zeroes as padding'''
# integer inputs, one per timestep, like embeddings
layer = core.Masking()
func = K.function([layer.input], [layer.get_output_mask()])
input_data = np.array([[[1], [2], [3], [0]],
[[0], [4], [5], [0]]], dtype=np.int32)
# This is the expected output mask, one dimension less
expected = np.array([[1, 1, 1, 0], [0, 1, 1, 0]])
# get mask for this input
output = func([input_data])[0]
assert np.all(output == expected), 'Output not as expected'
def test_sequences(self):
"""Test masking sequences with zeroes as padding"""
if K._BACKEND == "tensorflow":
return
# integer inputs, one per timestep, like embeddings
layer = core.Masking()
func = K.function([layer.input], [layer.get_output_mask()])
input_data = np.array([[[1], [2], [3], [0]], [[0], [4], [5], [0]]],
dtype=np.int32)
# This is the expected output mask, one dimension less
expected = np.array([[1, 1, 1, 0], [0, 1, 1, 0]])
# get mask for this input
output = func([input_data])[0]
assert (np.all(output == expected))
def test_implicit_mask(self):
attention_layer = dense_attention.Attention()
q = core.Masking(1.1)(np.array([[[1.1], [1]]], dtype=np.float32))
v = core.Masking(1.2)(np.array([[[1.2], [1]]], dtype=np.float32))
actual = attention_layer([q, v])
self.assertAllClose([[[0], [1]]], actual)
def test_override_mask(self):
attention_layer = dense_attention.Attention()
q = core.Masking()(np.array([[[1.1]]], dtype=np.float32))
mask = np.array([[False]], dtype=np.bool_)
actual = attention_layer([q, q], mask=[mask, mask])
self.assertAllClose([[[0]]], actual)
