def test():
fea = tf.constant(feature)
la = tf.constant(label)
po = tf.constant(pos)
features = {
params.feature_name: dense_to_sparse_tensor(fea),
params.pos_name: dense_to_sparse_tensor(po),
params.distance_name: dense_to_sparse_tensor(dis)
}
if tf.executing_eagerly():
print("feature is \r\n {}".format(features[params.feature_name]))
print("pos is \r\n {}".format(features[params.pos_name]))
model_fn(features, la, tf.estimator.ModeKeys.TRAIN, config, params)
def testBowEncoderSparseTensor(self):
with self.cached_session() as sess:
docs = [[0, 1], [2, 3]]
sparse_docs = sparse_ops.dense_to_sparse_tensor(docs)
enc = encoders.bow_encoder(sparse_docs, 4, 3)
sess.run(variables.global_variables_initializer())
self.assertAllEqual([2, 3], enc.eval().shape)
def testBowEncoderSparseTensor(self):
with self.test_session() as sess:
docs = [[0, 1], [2, 3]]
sparse_docs = sparse_ops.dense_to_sparse_tensor(docs)
enc = encoders.bow_encoder(sparse_docs, 4, 3)
sess.run(variables.global_variables_initializer())
self.assertAllEqual([2, 3], enc.eval().shape)
def bow_encoder(ids,
vocab_size,
embed_dim,
sparse_lookup=True,
initializer=None,
regularizer=None,
trainable=True,
scope=None,
reuse=None):
"""Maps a sequence of symbols to a vector per example by averaging embeddings.
Args:
ids: `[batch_size, doc_length]` `Tensor` or `SparseTensor` of type
`int32` or `int64` with symbol ids.
vocab_size: Integer number of symbols in vocabulary.
embed_dim: Integer number of dimensions for embedding matrix.
sparse_lookup: `bool`, if `True`, converts ids to a `SparseTensor`
and performs a sparse embedding lookup. This is usually faster,
but not desirable if padding tokens should have an embedding. Empty rows
are assigned a special embedding.
initializer: An initializer for the embeddings, if `None` default for
current scope is used.
regularizer: Optional regularizer for the embeddings.
trainable: If `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
scope: Optional string specifying the variable scope for the op, required
if `reuse=True`.
reuse: If `True`, variables inside the op will be reused.
Returns:
Encoding `Tensor` `[batch_size, embed_dim]` produced by
averaging embeddings.
Raises:
ValueError: If `embed_dim` or `vocab_size` are not specified.
"""
if not vocab_size or not embed_dim:
raise ValueError('Must specify vocab size and embedding dimension')
with variable_scope.variable_scope(scope,
'bow_encoder', [ids],
reuse=reuse):
embeddings = variables.model_variable('embeddings',
shape=[vocab_size, embed_dim],
initializer=initializer,
regularizer=regularizer,
trainable=trainable)
if sparse_lookup:
if isinstance(ids, ops.SparseTensor):
sparse_ids = ids
else:
sparse_ids = sparse_ops.dense_to_sparse_tensor(ids)
return contrib_embedding_ops.safe_embedding_lookup_sparse(
[embeddings], sparse_ids, combiner='mean', default_id=0)
else:
if isinstance(ids, ops.SparseTensor):
raise TypeError('ids are expected to be dense Tensor, got: %s',
ids)
return math_ops.reduce_mean(embedding_ops.embedding_lookup(
embeddings, ids),
reduction_indices=1)
def test_dense_to_sparse_tensor_1d_no_shape(self):
with self.test_session() as sess:
tensor = array_ops.placeholder(shape=[None], dtype=dtypes.int32)
st = sparse_ops.dense_to_sparse_tensor(tensor)
result = sess.run(st, feed_dict={tensor: [0, 100, 0, 3]})
self.assertAllEqual([[1], [3]], result.indices)
self.assertAllEqual([100, 3], result.values)
self.assertAllEqual([4], result.dense_shape)
def test_dense_to_sparse_tensor_2d(self):
with self.test_session() as sess:
st = sparse_ops.dense_to_sparse_tensor([[1, 2, 0, 0], [3, 4, 5, 0]])
result = sess.run(st)
self.assertAllEqual([[0, 0], [0, 1], [1, 0], [1, 1], [1, 2]],
result.indices)
self.assertAllEqual([1, 2, 3, 4, 5], result.values)
self.assertAllEqual([2, 4], result.dense_shape)
def test_dense_to_sparse_tensor_1d_no_shape(self):
with self.test_session() as sess:
tensor = array_ops.placeholder(shape=[None], dtype=dtypes.int32)
st = sparse_ops.dense_to_sparse_tensor(tensor)
result = sess.run(st, feed_dict={tensor: [0, 100, 0, 3]})
self.assertAllEqual([[1], [3]], result.indices)
self.assertAllEqual([100, 3], result.values)
self.assertAllEqual([4], result.dense_shape)
def bow_encoder(ids,
vocab_size,
embed_dim,
sparse_lookup=True,
initializer=None,
regularizer=None,
trainable=True,
scope=None,
reuse=None):
"""Maps a sequence of symbols to a vector per example by averaging embeddings.
Args:
ids: `[batch_size, doc_length]` `Tensor` or `SparseTensor` of type
`int32` or `int64` with symbol ids.
vocab_size: Integer number of symbols in vocabulary.
embed_dim: Integer number of dimensions for embedding matrix.
sparse_lookup: `bool`, if `True`, converts ids to a `SparseTensor`
and performs a sparse embedding lookup. This is usually faster,
but not desirable if padding tokens should have an embedding. Empty rows
are assigned a special embedding.
initializer: An initializer for the embeddings, if `None` default for
current scope is used.
regularizer: Optional regularizer for the embeddings.
trainable: If `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
scope: Optional string specifying the variable scope for the op, required
if `reuse=True`.
reuse: If `True`, variables inside the op will be reused.
Returns:
Encoding `Tensor` `[batch_size, embed_dim]` produced by
averaging embeddings.
Raises:
ValueError: If `embed_dim` or `vocab_size` are not specified.
"""
if not vocab_size or not embed_dim:
raise ValueError('Must specify vocab size and embedding dimension')
with variable_scope.variable_scope(
scope, 'bow_encoder', [ids], reuse=reuse):
embeddings = variables.model_variable(
'embeddings', shape=[vocab_size, embed_dim],
initializer=initializer, regularizer=regularizer,
trainable=trainable)
if sparse_lookup:
if isinstance(ids, sparse_tensor.SparseTensor):
sparse_ids = ids
else:
sparse_ids = sparse_ops.dense_to_sparse_tensor(ids)
return contrib_embedding_ops.safe_embedding_lookup_sparse(
[embeddings], sparse_ids, combiner='mean', default_id=0)
else:
if isinstance(ids, sparse_tensor.SparseTensor):
raise TypeError('ids are expected to be dense Tensor, got: %s', ids)
return math_ops.reduce_mean(
embedding_ops.embedding_lookup(embeddings, ids),
reduction_indices=1)
def test_dense_to_sparse_unknown_rank(self):
ph = array_ops.placeholder(dtype=dtypes.int32)
with self.cached_session() as sess:
st = sparse_ops.dense_to_sparse_tensor(ph)
result = sess.run(st, feed_dict={ph: [[1, 2, 0, 0], [3, 4, 5, 0]]})
self.assertAllEqual([[0, 0], [0, 1], [1, 0], [1, 1], [1, 2]],
result.indices)
self.assertAllEqual([1, 2, 3, 4, 5], result.values)
self.assertAllEqual([2, 4], result.dense_shape)
def test_dense_to_sparse_unknown_rank(self):
ph = array_ops.placeholder(dtype=dtypes.int32)
with self.cached_session() as sess:
st = sparse_ops.dense_to_sparse_tensor(ph)
result = sess.run(st, feed_dict={ph: [[1, 2, 0, 0], [3, 4, 5, 0]]})
self.assertAllEqual([[0, 0], [0, 1], [1, 0], [1, 1], [1, 2]],
result.indices)
self.assertAllEqual([1, 2, 3, 4, 5], result.values)
self.assertAllEqual([2, 4], result.dense_shape)
def test_dense_to_sparse_tensor_1d_str(self):
with self.test_session() as sess:
st = sparse_ops.dense_to_sparse_tensor([b'qwe', b'', b'ewq', b''])
result = sess.run(st)
self.assertEqual(result.indices.dtype, np.int64)
self.assertEqual(result.values.dtype, np.object)
self.assertEqual(result.dense_shape.dtype, np.int64)
self.assertAllEqual([[0], [2]], result.indices)
self.assertAllEqual([b'qwe', b'ewq'], result.values)
self.assertAllEqual([4], result.dense_shape)
def test_dense_to_sparse_tensor_1d_bool(self):
with self.test_session() as sess:
st = sparse_ops.dense_to_sparse_tensor([True, False, True, False])
result = sess.run(st)
self.assertEqual(result.indices.dtype, np.int64)
self.assertEqual(result.values.dtype, np.bool)
self.assertEqual(result.dense_shape.dtype, np.int64)
self.assertAllEqual([[0], [2]], result.indices)
self.assertAllEqual([True, True], result.values)
self.assertAllEqual([4], result.dense_shape)
def test_dense_to_sparse_tensor_1d(self):
with self.test_session() as sess:
st = sparse_ops.dense_to_sparse_tensor([1, 0, 2, 0])
result = sess.run(st)
self.assertEqual(result.indices.dtype, np.int64)
self.assertEqual(result.values.dtype, np.int32)
self.assertEqual(result.dense_shape.dtype, np.int64)
self.assertAllEqual([[0], [2]], result.indices)
self.assertAllEqual([1, 2], result.values)
self.assertAllEqual([4], result.dense_shape)
def test_dense_to_sparse_tensor_1d_str(self):
with self.test_session() as sess:
st = sparse_ops.dense_to_sparse_tensor([b'qwe', b'', b'ewq', b''])
result = sess.run(st)
self.assertEqual(result.indices.dtype, np.int64)
self.assertEqual(result.values.dtype, np.object)
self.assertEqual(result.dense_shape.dtype, np.int64)
self.assertAllEqual([[0], [2]], result.indices)
self.assertAllEqual([b'qwe', b'ewq'], result.values)
self.assertAllEqual([4], result.dense_shape)
def test_dense_to_sparse_tensor_1d_float(self):
with self.test_session() as sess:
st = sparse_ops.dense_to_sparse_tensor([1.5, 0.0, 2.3, 0.0])
result = sess.run(st)
self.assertEqual(result.indices.dtype, np.int64)
self.assertEqual(result.values.dtype, np.float32)
self.assertEqual(result.dense_shape.dtype, np.int64)
self.assertAllEqual([[0], [2]], result.indices)
self.assertAllClose([1.5, 2.3], result.values)
self.assertAllEqual([4], result.dense_shape)
def test_dense_to_sparse_tensor_1d_bool(self):
with self.test_session() as sess:
st = sparse_ops.dense_to_sparse_tensor([True, False, True, False])
result = sess.run(st)
self.assertEqual(result.indices.dtype, np.int64)
self.assertEqual(result.values.dtype, np.bool)
self.assertEqual(result.dense_shape.dtype, np.int64)
self.assertAllEqual([[0], [2]], result.indices)
self.assertAllEqual([True, True], result.values)
self.assertAllEqual([4], result.dense_shape)
def test_dense_to_sparse_tensor_1d(self):
with self.test_session() as sess:
st = sparse_ops.dense_to_sparse_tensor([1, 0, 2, 0])
result = sess.run(st)
self.assertEqual(result.indices.dtype, np.int64)
self.assertEqual(result.values.dtype, np.int32)
self.assertEqual(result.dense_shape.dtype, np.int64)
self.assertAllEqual([[0], [2]], result.indices)
self.assertAllEqual([1, 2], result.values)
self.assertAllEqual([4], result.dense_shape)
def test_dense_to_sparse_tensor_1d_float(self):
with self.test_session() as sess:
st = sparse_ops.dense_to_sparse_tensor([1.5, 0.0, 2.3, 0.0])
result = sess.run(st)
self.assertEqual(result.indices.dtype, np.int64)
self.assertEqual(result.values.dtype, np.float32)
self.assertEqual(result.dense_shape.dtype, np.int64)
self.assertAllEqual([[0], [2]], result.indices)
self.assertAllClose([1.5, 2.3], result.values)
self.assertAllEqual([4], result.dense_shape)
def test_dense_to_sparse_tensor_1d_str_special_ignore(self):
with self.test_session() as sess:
st = sparse_ops.dense_to_sparse_tensor(
[b'qwe', b'', b'ewq', b''], ignore_value=b'qwe')
result = sess.run(st)
self.assertEqual(result.indices.dtype, np.int64)
self.assertEqual(result.values.dtype, np.object)
self.assertEqual(result.shape.dtype, np.int64)
self.assertAllEqual([[1], [2], [3]], result.indices)
self.assertAllEqual([b'', b'ewq', b''], result.values)
self.assertAllEqual([4], result.shape)
def test_dense_to_sparse_tensor_1d_str_special_ignore(self):
with self.cached_session() as sess:
st = sparse_ops.dense_to_sparse_tensor([b'qwe', b'', b'ewq', b''],
ignore_value=b'qwe')
result = sess.run(st)
self.assertEqual(result.indices.dtype, np.int64)
self.assertEqual(result.values.dtype, np.object)
self.assertEqual(result.dense_shape.dtype, np.int64)
self.assertAllEqual([[1], [2], [3]], result.indices)
self.assertAllEqual([b'', b'ewq', b''], result.values)
self.assertAllEqual([4], result.dense_shape)
def test_dense_to_sparse_tensor_3d_no_shape(self):
with self.test_session() as sess:
tensor = tf.placeholder(shape=[None, None, None], dtype=tf.int32)
st = sparse_ops.dense_to_sparse_tensor(tensor)
result = sess.run(st,
feed_dict={
tensor: [[[1, 2, 0, 0], [3, 4, 5, 0]],
[[7, 8, 0, 0], [9, 0, 0, 0]]]
})
self.assertAllEqual([[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [0, 1, 2],
[1, 0, 0], [1, 0, 1], [1, 1, 0]], result.indices)
self.assertAllEqual([1, 2, 3, 4, 5, 7, 8, 9], result.values)
self.assertAllEqual([2, 2, 4], result.shape)
def test_dense_to_sparse_tensor_3d_no_shape(self):
with self.test_session() as sess:
tensor = tf.placeholder(shape=[None, None, None], dtype=tf.int32)
st = sparse_ops.dense_to_sparse_tensor(tensor)
result = sess.run(st,
feed_dict={
tensor: [[[1, 2, 0, 0], [3, 4, 5, 0]],
[[7, 8, 0, 0], [9, 0, 0, 0]]]
})
self.assertAllEqual([[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [0, 1, 2],
[1, 0, 0], [1, 0, 1], [1, 1, 0]], result.indices)
self.assertAllEqual([1, 2, 3, 4, 5, 7, 8, 9], result.values)
self.assertAllEqual([2, 2, 4], result.shape)
def _prepare_inputs_for_fit_sparse(self,
model_matrix,
response,
model_coefficients_start=None,
convert_to_sparse_tensor=False):
if model_coefficients_start is None:
model_coefficients_start = np.zeros(model_matrix.shape[:-2] +
model_matrix.shape[-1:])
if convert_to_sparse_tensor:
model_matrix = sparse_ops.dense_to_sparse_tensor(model_matrix)
model_matrix = self._adjust_dtype_and_shape_hints(model_matrix)
response = self._adjust_dtype_and_shape_hints(response)
model_coefficients_start = self._adjust_dtype_and_shape_hints(
model_coefficients_start)
return model_matrix, response, model_coefficients_start
def _prepare_inputs_for_fit_sparse(self,
model_matrix,
response,
model_coefficients_start=None,
convert_to_sparse_tensor=False):
if model_coefficients_start is None:
model_coefficients_start = np.zeros(model_matrix.shape[:-2] +
model_matrix.shape[-1:])
if convert_to_sparse_tensor:
model_matrix = sparse_ops.dense_to_sparse_tensor(model_matrix)
model_matrix = self._adjust_dtype_and_shape_hints(model_matrix)
response = self._adjust_dtype_and_shape_hints(response)
model_coefficients_start = self._adjust_dtype_and_shape_hints(
model_coefficients_start)
return model_matrix, response, model_coefficients_start
def verify_sparse_dense_matmul(self, x_, y_):
if self.use_sparse_tensor:
x = self._make_sparse_placeholder(
sparse_ops.dense_to_sparse_tensor(x_))
else:
x = self._make_placeholder(x_)
y = self._make_placeholder(y_)
z = tfp.math.sparse_or_dense_matmul(x, y)
z_ = self.evaluate(z)
if self.use_static_shape:
batch_shape = x_.shape[:-2]
self.assertAllEqual(z_.shape,
batch_shape + (x_.shape[-2], y_.shape[-1]))
self.assertAllClose(z_, np.matmul(x_, y_), atol=0., rtol=1e-3)
def to_sparse_tensor(self, input_tensor):
"""Creates a SparseTensor from the bucketized Tensor."""
dimension = self.source_column.dimension
batch_size = array_ops.shape(input_tensor, name="shape")[0]
if len(input_tensor.get_shape()) > 2:
return sparse_ops.dense_to_sparse_tensor(input_tensor,
ignore_value=-2**31)
if dimension > 1:
i1 = array_ops.reshape(array_ops.tile(
array_ops.expand_dims(math_ops.range(0, batch_size),
1,
name="expand_dims"), [1, dimension],
name="tile"), [-1],
name="reshape")
i2 = array_ops.tile(math_ops.range(0, dimension), [batch_size],
name="tile")
# Flatten the bucket indices and unique them across dimensions
# E.g. 2nd dimension indices will range from k to 2*k-1 with k buckets
bucket_indices = array_ops.reshape(
input_tensor, [-1], name="reshape") + self.length * i2
else:
# Simpler indices when dimension=1
i1 = math_ops.range(0, batch_size)
i2 = array_ops.zeros([batch_size],
dtype=dtypes.int32,
name="zeros")
bucket_indices = array_ops.reshape(input_tensor, [-1],
name="reshape")
indices = math_ops.to_int64(
array_ops.transpose(array_ops.stack((i1, i2))))
shape = math_ops.to_int64(array_ops.stack([batch_size, dimension]))
sparse_id_values = sparse_tensor_py.SparseTensor(
indices, bucket_indices, shape)
return sparse_id_values
def testBowEncoderSparseTensorDenseLookup(self):
with self.cached_session():
docs = [[0, 1]]
sparse_docs = sparse_ops.dense_to_sparse_tensor(docs)
with self.assertRaises(TypeError):
encoders.bow_encoder(sparse_docs, 4, 3, sparse_lookup=False)
def test_convert_to_sparse_undef_shape(self):
with self.test_session():
with self.assertRaises(ValueError):
tensor = array_ops.placeholder(dtype=dtypes.int32)
sparse_ops.dense_to_sparse_tensor(tensor)
def test_convert_to_sparse_undef_shape(self):
with self.test_session():
with self.assertRaises(ValueError):
tensor = array_ops.placeholder(dtype=dtypes.int32)
sparse_ops.dense_to_sparse_tensor(tensor)
def testBowEncoderSparseTensorDenseLookup(self):
with self.test_session():
docs = [[0, 1]]
sparse_docs = sparse_ops.dense_to_sparse_tensor(docs)
with self.assertRaises(TypeError):
encoders.bow_encoder(sparse_docs, 4, 3, sparse_lookup=False)
