def testPaddingInvalidLengths(self):
with tf.Graph().as_default() as g, self.test_session(graph=g):
sequences = {"key_1": tf.constant([1, 2, 3]), # length 3
"key_2": tf.constant([1.5, 2.5])} # length 2
_, padded_seq = sqss._padding(sequences, 2)
with self.assertRaisesOpError(
".*All sequence lengths must match, but received lengths.*"):
padded_seq["key_1"].eval()
def testPaddingInvalidLengths(self):
with tf.Graph().as_default() as g, self.test_session(graph=g):
sequences = {"key_1": tf.constant([1, 2, 3]), # length 3
"key_2": tf.constant([1.5, 2.5])} # length 2
_, padded_seq = sqss._padding(sequences, 2)
with self.assertRaisesOpError(
".*All sequence lengths must match, but received lengths.*"):
padded_seq["key_1"].eval()
def testPadding(self):
with tf.Graph().as_default() as g, self.test_session(graph=g):
sequences = {"key_1": tf.constant([1, 2]),
"key_2": tf.constant([0.5, -1.0]),
"key_3": tf.constant(["a", "b"]), # padding strings
"key_4": tf.constant([[1, 2, 3], [4, 5, 6]])}
_, padded_seq = sqss._padding(sequences, 5)
expected_padded_seq = {
"key_1": [1, 2, 0, 0, 0],
"key_2": [0.5, -1.0, 0.0, 0.0, 0.0],
"key_3": ["a", "b", "", "", ""],
"key_4": [[1, 2, 3], [4, 5, 6], [0, 0, 0], [0, 0, 0], [0, 0, 0]]}
for key, val in expected_padded_seq.items():
self.assertTrue(tf.reduce_all(tf.equal(val, padded_seq[key])).eval())
def testPadding(self):
with tf.Graph().as_default() as g, self.test_session(graph=g):
sequences = {"key_1": tf.constant([1, 2]),
"key_2": tf.constant([0.5, -1.0]),
"key_3": tf.constant(["a", "b"]), # padding strings
"key_4": tf.constant([[1, 2, 3], [4, 5, 6]])}
_, padded_seq = sqss._padding(sequences, 5)
expected_padded_seq = {
"key_1": [1, 2, 0, 0, 0],
"key_2": [0.5, -1.0, 0.0, 0.0, 0.0],
"key_3": ["a", "b", "", "", ""],
"key_4": [[1, 2, 3], [4, 5, 6], [0, 0, 0], [0, 0, 0], [0, 0, 0]]}
for key, val in expected_padded_seq.items():
self.assertTrue(tf.reduce_all(tf.equal(val, padded_seq[key])).eval())
def testPaddingOnlySparse(self):
ind1 = np.array([[0], [2]])
val1 = np.array([3, 4])
shape1 = np.array([4])
ind2 = np.array([[1], [2]])
val2 = np.array([9, 12])
shape2 = np.array([5])
with ops.Graph().as_default() as g, self.test_session(graph=g):
sp_tensor1 = sparse_tensor.SparseTensor(
indices=array_ops.constant(ind1, dtypes.int64),
values=array_ops.constant(val1, dtypes.int64),
dense_shape=array_ops.constant(shape1, dtypes.int64))
sp_tensor2 = sparse_tensor.SparseTensor(
indices=array_ops.constant(ind2, dtypes.int64),
values=array_ops.constant(val2, dtypes.int64),
dense_shape=array_ops.constant(shape2, dtypes.int64))
sp_tensor1_expected = sparse_tensor.SparseTensor(
indices=sp_tensor1.indices,
values=sp_tensor1.values,
dense_shape=[8])
sp_tensor2_expected = sparse_tensor.SparseTensor(
indices=sp_tensor2.indices,
values=sp_tensor2.values,
dense_shape=[8])
sequences = {
"key_1": sp_tensor1,
"key_2": sp_tensor2,
}
_, padded_seq = sqss._padding(sequences, 4)
expected_padded_seq = {
"key_1": sp_tensor1_expected,
"key_2": sp_tensor2_expected,
}
for key, val in expected_padded_seq.items():
self.assertAllEqual(
sparse_ops.sparse_tensor_to_dense(val).eval(),
sparse_ops.sparse_tensor_to_dense(padded_seq[key]).eval())
def testPaddingOnlySparse(self):
ind1 = np.array([[0], [2]])
val1 = np.array([3, 4])
shape1 = np.array([4])
ind2 = np.array([[1], [2]])
val2 = np.array([9, 12])
shape2 = np.array([5])
with ops.Graph().as_default() as g, self.test_session(graph=g):
sp_tensor1 = sparse_tensor.SparseTensor(
indices=array_ops.constant(ind1, dtypes.int64),
values=array_ops.constant(val1, dtypes.int64),
dense_shape=array_ops.constant(shape1, dtypes.int64))
sp_tensor2 = sparse_tensor.SparseTensor(
indices=array_ops.constant(ind2, dtypes.int64),
values=array_ops.constant(val2, dtypes.int64),
dense_shape=array_ops.constant(shape2, dtypes.int64))
sp_tensor1_expected = sparse_tensor.SparseTensor(
indices=sp_tensor1.indices,
values=sp_tensor1.values,
dense_shape=[8])
sp_tensor2_expected = sparse_tensor.SparseTensor(
indices=sp_tensor2.indices,
values=sp_tensor2.values,
dense_shape=[8])
sequences = {
"key_1": sp_tensor1,
"key_2": sp_tensor2,
}
_, padded_seq = sqss._padding(sequences, 4)
expected_padded_seq = {
"key_1": sp_tensor1_expected,
"key_2": sp_tensor2_expected,
}
for key, val in expected_padded_seq.items():
self.assertAllEqual(
sparse_ops.sparse_tensor_to_dense(val).eval(),
sparse_ops.sparse_tensor_to_dense(padded_seq[key]).eval())
