Python sparse_tensor.SparseTensorValue方法代码示例

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin
  """Returns the shape of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the shape as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.

  """
  with ops.name_scope(name, "Shape", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops.cast(input.dense_shape, out_type)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.as_list(), out_type, name=name)
      return gen_array_ops.shape(input, name=name, out_type=out_type) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin,protected-access
  """Returns the size of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the size as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.
  """
  with ops.name_scope(name, "Size", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops._prod(
          gen_math_ops.cast(input.dense_shape, out_type), 0, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.num_elements(), out_type, name=name)
      return gen_array_ops.size(input, name=name, out_type=out_type) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def rank_internal(input, name=None, optimize=True):
  # pylint: disable=redefined-builtin
  """Returns the rank of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the rank as a constant when possible.

  Returns:
    A `Tensor` of type `int32`.
  """
  with ops.name_scope(name, "Rank", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_array_ops.size(input.dense_shape, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.ndims is not None:
        return constant(input_shape.ndims, dtypes.int32, name=name)
      return gen_array_ops.rank(input, name=name) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def _convert_to_sparse_tensor(sp_input):
  """Convert `sp_input` to `SparseTensor` and return it.

  Args:
    sp_input: `SparseTensor` or `SparseTensorValue`.

  Returns:
    `sp_input` converted to `SparseTensor`.

  Raises:
    ValueError: if `sp_input` is neither `SparseTensor` nor `SparseTensorValue`.
  """
  if isinstance(sp_input, sparse_tensor.SparseTensorValue):
    return sparse_tensor.SparseTensor.from_value(sp_input)
  if not isinstance(sp_input, sparse_tensor.SparseTensor):
    raise TypeError("Input must be a SparseTensor.")
  return sp_input 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def _predict_generator(self, mon_sess, predictions, feed_fn, iterate_batches):
    with mon_sess:
      while not mon_sess.should_stop():
        preds = mon_sess.run(predictions, feed_fn() if feed_fn else None)
        if iterate_batches:
          yield preds
        elif not isinstance(predictions, dict):
          for pred in preds:
            yield pred
        else:
          first_tensor = list(preds.values())[0]
          if isinstance(first_tensor, sparse_tensor.SparseTensorValue):
            batch_length = first_tensor.dense_shape[0]
          else:
            batch_length = first_tensor.shape[0]
          for i in range(batch_length):
            yield {key: value[i] for key, value in six.iteritems(preds)}
        if self._is_input_constant(feed_fn, mon_sess.graph):
          return 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def normalize_tensors(tensors):
  """Converts a nested structure of tensor-like objects to tensors.
  * `SparseTensor`-like inputs are converted to `SparseTensor`.
  * `TensorArray` inputs are passed through.
  * Everything else is converted to a dense `Tensor`.
  Args:
    tensors: A nested structure of tensor-like, list,
      `SparseTensor`, `SparseTensorValue`, or `TensorArray` objects.
  Returns:
    A nested structure of tensor, `SparseTensor`, or `TensorArray` objects.
  """
  flat_tensors = nest.flatten(tensors)
  prepared = []
  with ops.name_scope("normalize_tensors"):
    for i, t in enumerate(flat_tensors):
      if sparse_tensor_lib.is_sparse(t):
        prepared.append(sparse_tensor_lib.SparseTensor.from_value(t))
      elif isinstance(t, tensor_array_ops.TensorArray):
        prepared.append(t)
      else:
        prepared.append(ops.convert_to_tensor(t, name="component_%d" % i))
  return nest.pack_sequence_as(tensors, prepared) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def test_indicators_to_sparse_ids_3d(self):
    indicators = (
        ((0, 0, 1, 0, 0), (0, 0, 0, 0, 0)),
        ((1, 0, 0, 1, 0), (0, 0, 1, 0, 0)),
        ((0, 0, 0, 0, 0), (0, 0, 0, 0, 0)),
        ((1, 0, 0, 1, 1), (0, 0, 1, 0, 0)),
    )
    sparse_ids = sparse_ops.indicators_to_sparse_ids(indicators)
    with self.cached_session():
      _assert_sparse_tensor_value(self, sparse_tensor.SparseTensorValue(
          indices=(
              (0, 0, 0),
              (1, 0, 0), (1, 0, 1), (1, 1, 0),
              (3, 0, 0), (3, 0, 1), (3, 0, 2), (3, 1, 0)
          ), values=(
              2,
              0, 3, 2,
              0, 3, 4, 2
          ), dense_shape=(4, 2, 3),
      ), sparse_ids.eval()) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin
  """Returns the shape of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the shape as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.

  """
  with ops.name_scope(name, "Shape", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops.cast(input.shape, out_type)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.as_list(), out_type, name=name)
      return gen_array_ops.shape(input, name=name, out_type=out_type) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
  # pylint: disable=redefined-builtin,protected-access
  """Returns the size of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the size as a constant when possible.
    out_type: (Optional) The specified output type of the operation
      (`int32` or `int64`). Defaults to tf.int32.

  Returns:
    A `Tensor` of type `out_type`.
  """
  with ops.name_scope(name, "Size", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_math_ops._prod(
          gen_math_ops.cast(input.shape, out_type), 0, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.is_fully_defined():
        return constant(input_shape.num_elements(), out_type, name=name)
      return gen_array_ops.size(input, name=name, out_type=out_type) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def rank_internal(input, name=None, optimize=True):
  # pylint: disable=redefined-builtin
  """Returns the rank of a tensor.

  Args:
    input: A `Tensor` or `SparseTensor`.
    name: A name for the operation (optional).
    optimize: if true, encode the rank as a constant when possible.

  Returns:
    A `Tensor` of type `int32`.
  """
  with ops.name_scope(name, "Rank", [input]) as name:
    if isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      return gen_array_ops.size(input.shape, name=name)
    else:
      input_tensor = ops.convert_to_tensor(input)
      input_shape = input_tensor.get_shape()
      if optimize and input_shape.ndims is not None:
        return constant(input_shape.ndims, dtypes.int32, name=name)
      return gen_array_ops.rank(input, name=name) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def testPythonConstruction(self):
    indices = [[1, 2], [2, 0], [3, 4]]
    values = [b"a", b"b", b"c"]
    shape = [4, 5]
    sp_value = sparse_tensor.SparseTensorValue(indices, values, shape)
    for sp in [
        sparse_tensor.SparseTensor(indices, values, shape),
        sparse_tensor.SparseTensor.from_value(sp_value),
        sparse_tensor.SparseTensor.from_value(
            sparse_tensor.SparseTensor(indices, values, shape))]:
      self.assertEqual(sp.indices.dtype, dtypes.int64)
      self.assertEqual(sp.values.dtype, dtypes.string)
      self.assertEqual(sp.shape.dtype, dtypes.int64)
      self.assertEqual(sp.get_shape(), (4, 5))

      with self.test_session() as sess:
        value = sp.eval()
        self.assertAllEqual(indices, value.indices)
        self.assertAllEqual(values, value.values)
        self.assertAllEqual(shape, value.shape)
        sess_run_value = sess.run(sp)
        self.assertAllEqual(sess_run_value.indices, value.indices)
        self.assertAllEqual(sess_run_value.values, value.values)
        self.assertAllEqual(sess_run_value.shape, value.shape) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def test_linear_model(self):
    wire_column = fc.categorical_column_with_hash_bucket('wire', 4)
    self.assertEqual(4, wire_column.num_buckets)
    with ops.Graph().as_default():
      model = linear.LinearModel((wire_column,))
      predictions = model({
          wire_column.name:
              sparse_tensor.SparseTensorValue(
                  indices=((0, 0), (1, 0), (1, 1)),
                  values=('marlo', 'skywalker', 'omar'),
                  dense_shape=(2, 2))
      })
      wire_var, bias = model.variables

      self.evaluate(variables_lib.global_variables_initializer())
      self.evaluate(lookup_ops.tables_initializer())

      self.assertAllClose((0.,), self.evaluate(bias))
      self.assertAllClose(((0.,), (0.,), (0.,), (0.,)), self.evaluate(wire_var))
      self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions))
      self.evaluate(wire_var.assign(((1.,), (2.,), (3.,), (4.,))))
      # 'marlo' -> 3: wire_var[3] = 4
      # 'skywalker' -> 2, 'omar' -> 2: wire_var[2] + wire_var[2] = 3+3 = 6
      self.assertAllClose(((4.,), (6.,)), self.evaluate(predictions)) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def test_sparse_multi_rank(self):
    wire_cast = fc.categorical_column_with_hash_bucket('wire_cast', 4)
    with ops.Graph().as_default():
      wire_tensor = array_ops.sparse_placeholder(dtypes.string)
      wire_value = sparse_tensor.SparseTensorValue(
          values=['omar', 'stringer', 'marlo', 'omar'],  # hashed = [2, 0, 3, 2]
          indices=[[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 0, 1]],
          dense_shape=[2, 2, 2])
      features = {'wire_cast': wire_tensor}
      model = linear.LinearModel([wire_cast])
      predictions = model(features)
      wire_cast_var, _ = model.variables
      with _initialized_session() as sess:
        self.assertAllClose(np.zeros((4, 1)), self.evaluate(wire_cast_var))
        self.assertAllClose(
            np.zeros((2, 1)),
            predictions.eval(feed_dict={wire_tensor: wire_value}))
        sess.run(wire_cast_var.assign([[10.], [100.], [1000.], [10000.]]))
        self.assertAllClose(
            [[1010.], [11000.]],
            predictions.eval(feed_dict={wire_tensor: wire_value})) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def test_linear_model_mismatched_shape(self):
    column = fc.weighted_categorical_column(
        categorical_column=fc.categorical_column_with_identity(
            key='ids', num_buckets=3),
        weight_feature_key='values')
    with ops.Graph().as_default():
      with self.assertRaisesRegexp(ValueError,
                                   r'Dimensions.*are not compatible'):
        model = linear.LinearModel((column,))
        model({
            'ids':
                sparse_tensor.SparseTensorValue(
                    indices=((0, 0), (1, 0), (1, 1)),
                    values=(0, 2, 1),
                    dense_shape=(2, 2)),
            'values':
                sparse_tensor.SparseTensorValue(
                    indices=((0, 0), (0, 1), (1, 0), (1, 1)),
                    values=(.5, 11., 1., .1),
                    dense_shape=(2, 2))
        }) 

# 需要导入模块: from tensorflow.python.framework import sparse_tensor [as 别名]
# 或者: from tensorflow.python.framework.sparse_tensor import SparseTensorValue [as 别名]
def test_linear_model_mismatched_dense_values(self):
    column = fc.weighted_categorical_column(
        categorical_column=fc.categorical_column_with_identity(
            key='ids', num_buckets=3),
        weight_feature_key='values')
    with ops.Graph().as_default():
      model = linear.LinearModel((column,), sparse_combiner='mean')
      predictions = model({
          'ids':
              sparse_tensor.SparseTensorValue(
                  indices=((0, 0), (1, 0), (1, 1)),
                  values=(0, 2, 1),
                  dense_shape=(2, 2)),
          'values': ((.5,), (1.,))
      })
      # Disabling the constant folding optimizer here since it changes the
      # error message differently on CPU and GPU.
      config = config_pb2.ConfigProto()
      config.graph_options.rewrite_options.constant_folding = (
          rewriter_config_pb2.RewriterConfig.OFF)
      with _initialized_session(config):
        with self.assertRaisesRegexp(errors.OpError, 'Incompatible shapes'):
          self.evaluate(predictions)