def test_ragged_input_pad_and_mask(self):
input_data = ragged_factory_ops.constant([[1, 2, 3, 4, 5], []])
expected_mask = np.array([True, False])
output = ToDense(pad_value=-1, mask=True)(input_data)
self.assertTrue(hasattr(output, "_keras_mask"))
self.assertIsNot(output._keras_mask, None)
self.assertAllEqual(K.get_value(output._keras_mask), expected_mask)
def test_sparse_input_pad_and_mask(self):
input_data = sparse_tensor.SparseTensor(indices=[[0, 0], [1, 2]],
values=[1, 2],
dense_shape=[3, 4])
expected_mask = np.array([True, True, False])
output = ToDense(pad_value=-1, mask=True)(input_data)
self.assertTrue(hasattr(output, "_keras_mask"))
self.assertIsNot(output._keras_mask, None)
self.assertAllEqual(K.get_value(output._keras_mask), expected_mask)
def SKIP_test_ragged_input_default_padding(self):
input_data = get_input_dataset(
ragged_factory_ops.constant([[1, 2, 3, 4, 5], [2, 3]]))
expected_output = np.array([[1, 2, 3, 4, 5], [2, 3, 0, 0, 0]])
layers = [ToDense(), Final()]
model = testing_utils.get_model_from_layers(layers,
input_shape=(None, ),
input_ragged=True,
input_dtype=dtypes.int32)
model.compile(optimizer="sgd",
loss="mse",
metrics=["accuracy"],
run_eagerly=testing_utils.should_run_eagerly())
output = model.predict(input_data)
self.assertAllEqual(output, expected_output)
def test_sparse_input_shape(self):
input_data = get_input_dataset(
sparse_tensor.SparseTensor(indices=[[0, 0], [1, 2]],
values=[1, 2],
dense_shape=[3, 4]))
expected_output = np.array([[1., 0., 0., 0.], [0., 0., 2., 0.],
[0., 0., 0., 0.]])
layers = [ToDense(shape=[3, 4]), Final()]
model = testing_utils.get_model_from_layers(layers,
input_shape=(None, ),
input_sparse=True,
input_dtype=dtypes.int32)
model.compile(optimizer="sgd",
loss="mse",
metrics=["accuracy"],
run_eagerly=testing_utils.should_run_eagerly())
output = model.predict(input_data)
self.assertAllEqual(output, expected_output)
def SKIP_test_ragged_input_with_padding(self):
input_data = get_input_dataset(
ragged_factory_ops.constant([[[1, 2, 3, 4, 5]], [[2], [3]]]))
expected_output = np.array([[[1., 2., 3., 4., 5.],
[-1., -1., -1., -1., -1.]],
[[2., -1., -1., -1., -1.],
[3., -1., -1., -1., -1.]]])
layers = [ToDense(pad_value=-1), Final()]
model = testing_utils.get_model_from_layers(layers,
input_shape=(None, None),
input_ragged=True,
input_dtype=dtypes.int32)
model.compile(optimizer="sgd",
loss="mse",
metrics=["accuracy"],
run_eagerly=testing_utils.should_run_eagerly(),
experimental_run_tf_function=testing_utils.
should_run_tf_function())
output = model.predict(input_data)
self.assertAllEqual(output, expected_output)
def SKIP_test_sparse_input_with_padding(self):
input_data = get_input_dataset(
sparse_tensor.SparseTensor(indices=[[0, 0], [1, 2]],
values=[1, 2],
dense_shape=[3, 4]))
expected_output = np.array([[1., -1., -1., -1.], [-1., -1., 2., -1.],
[-1., -1., -1., -1.]])
layers = [ToDense(pad_value=-1, trainable=False), Final()]
model = testing_utils.get_model_from_layers(layers,
input_shape=(None, ),
input_sparse=True,
input_dtype=dtypes.int32)
model.compile(optimizer="sgd",
loss="mse",
metrics=["accuracy"],
run_eagerly=testing_utils.should_run_eagerly(),
experimental_run_tf_function=testing_utils.
should_run_tf_function())
output = model.predict(input_data)
self.assertAllEqual(output, expected_output)
def SKIP_test_ragged_input_RNN_layer(self, layer):
input_data = get_input_dataset(
ragged_factory_ops.constant([[1, 2, 3, 4, 5], [5, 6]]))
layers = [
ToDense(pad_value=7, mask=True),
keras.layers.Embedding(8, 16),
layer(16),
keras.layers.Dense(3, activation="softmax"),
keras.layers.Dense(1, activation="sigmoid")
]
model = testing_utils.get_model_from_layers(layers,
input_shape=(None, ),
input_ragged=True,
input_dtype=dtypes.int32)
model.compile(optimizer="rmsprop",
loss="binary_crossentropy",
metrics=["accuracy"],
run_eagerly=testing_utils.should_run_eagerly())
output = model.predict(input_data)
self.assertAllEqual(np.zeros((2, 1)).shape, output.shape)
