python类add()的实例源码

def _select_class_id(ids, selected_id):
  """Filter all but `selected_id` out of `ids`.

  Args:
    ids: `int64` `Tensor` or `SparseTensor` of IDs.
    selected_id: Int id to select.

  Returns:
    `SparseTensor` of same dimensions as `ids`. This contains only the entries
    equal to `selected_id`.
  """
  if isinstance(
      ids, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
    return sparse_ops.sparse_retain(
        ids, math_ops.equal(ids.values, selected_id))

  # TODO(ptucker): Make this more efficient, maybe add a sparse version of
  # tf.equal and tf.reduce_any?

  # Shape of filled IDs is the same as `ids` with the last dim collapsed to 1.
  ids_shape = array_ops.shape(ids, out_type=dtypes.int64)
  ids_last_dim = array_ops.size(ids_shape) - 1
  filled_selected_id_shape = math_ops.reduced_shape(
      ids_shape, array_ops.reshape(ids_last_dim, [1]))

  # Intersect `ids` with the selected ID.
  filled_selected_id = array_ops.fill(
      filled_selected_id_shape, math_ops.to_int64(selected_id))
  result = set_ops.set_intersection(filled_selected_id, ids)
  return sparse_tensor.SparseTensor(
      indices=result.indices, values=result.values, dense_shape=ids_shape)

def setUp(self):
    super(CoreBinaryOpsTest, self).setUp()

    self.x_probs_broadcast_tensor = array_ops.reshape(
        self.x_probs_lt.tensor, [self.x_size, 1, self.probs_size])

    self.channel_probs_broadcast_tensor = array_ops.reshape(
        self.channel_probs_lt.tensor, [1, self.channel_size, self.probs_size])

    # == and != are not element-wise for tf.Tensor, so they shouldn't be
    # elementwise for LabeledTensor, either.
    self.ops = [
        ('add', operator.add, math_ops.add, core.add),
        ('sub', operator.sub, math_ops.subtract, core.sub),
        ('mul', operator.mul, math_ops.multiply, core.mul),
        ('div', operator.truediv, math_ops.div, core.div),
        ('mod', operator.mod, math_ops.mod, core.mod),
        ('pow', operator.pow, math_ops.pow, core.pow_function),
        ('equal', None, math_ops.equal, core.equal),
        ('less', operator.lt, math_ops.less, core.less),
        ('less_equal', operator.le, math_ops.less_equal, core.less_equal),
        ('not_equal', None, math_ops.not_equal, core.not_equal),
        ('greater', operator.gt, math_ops.greater, core.greater),
        ('greater_equal', operator.ge, math_ops.greater_equal,
         core.greater_equal),
    ]
    self.test_lt_1 = self.x_probs_lt
    self.test_lt_2 = self.channel_probs_lt
    self.test_lt_1_broadcast = self.x_probs_broadcast_tensor
    self.test_lt_2_broadcast = self.channel_probs_broadcast_tensor
    self.broadcast_axes = [self.a0, self.a1, self.a3]

def __add__(self, other):
    return add(self, other)

def __radd__(self, other):
    return add(other, self)

def test_unconstrained(self):

    def objective(x):
      """Rosenbrock function. (Carl Edward Rasmussen, 2001-07-21).

      f(x) = sum_{i=1:D-1} 100*(x(i+1) - x(i)^2)^2 + (1-x(i))^2

      Args:
        x: a Variable
      Returns:
        f: a tensor (objective value)
      """

      d = array_ops.size(x)
      s = math_ops.add(
          100 * math_ops.square(
              math_ops.subtract(
                  array_ops.strided_slice(x, [1], [d]),
                  math_ops.square(array_ops.strided_slice(x, [0], [d - 1])))),
          math_ops.square(
              math_ops.subtract(1.0, array_ops.strided_slice(x, [0], [d - 1]))))
      return math_ops.reduce_sum(s)

    dimension = 5
    x = variables.Variable(array_ops.zeros(dimension))
    optimizer = external_optimizer.ScipyOptimizerInterface(objective(x))

    with self.test_session() as sess:
      sess.run(variables.global_variables_initializer())
      optimizer.minimize(sess)

      self.assertAllClose(np.ones(dimension), sess.run(x))

def testAdd(self):
    val1 = np.array([4, 3, 2, 1], dtype=np.float32)
    val2 = np.array([5, 6, 7, 8], dtype=np.float32)
    expected = val1 + val2
    with self.test_session():
      with self.test_scope():
        input1 = constant_op.constant(val1, name="const1")
        input2 = constant_op.constant(val2, name="const2")
        output = math_ops.add(input1, input2)
      result = output.eval()
    self.assertAllClose(result, expected, rtol=1e-3)

def testCond(self):
    """Tests that tf.cond works on XLA devices."""

    with session_lib.Session() as session:
      x = array_ops.placeholder(dtypes.float32)
      y = array_ops.placeholder(dtypes.float32)
      c = array_ops.placeholder(dtypes.bool)
      with ops.device("device:XLA_CPU:0"):
        z = x + 1.0
        w = control_flow_ops.cond(c, lambda: z, lambda: y)
        t = math_ops.add(z, w)

      result = session.run(t, {x: np.float32(2), y: np.float32(4), c: True})
      self.assertAllClose(result, np.float32(6), rtol=1e-3)

def testAliasing(self):
    """Regression test for compiled functions that return an aliased buffer.

       XLA returns aliased buffers if outputs are identical. Tests that
       we handle that case.
    """

    def AddOnceReturnTwice(x):
      y = math_ops.add(x, x)
      return y, y

    # Exercises compling a function (say, Foo) which calls another
    # function (say, Bar) which is not inlined. When the compiler compiles
    # Foo, it needs to symbolic execute Bar correctly regardless whether
    # Bar is inlined or not.
    #
    # Tests compiled=True and noinline=True.
    self._compare(
        AddOnceReturnTwice, [np.array(
            [[[0.5, -1.0]]], dtype=np.float32)],
        noinline=True)
    # Tests compiled=True and noinline=False.
    self._compare(
        AddOnceReturnTwice, [np.array(
            [[[0.5, -1.0]]], dtype=np.float32)],
        noinline=False)

def testInt32Input(self):
    """Test an int32-typed input.

       On a GPU, int32 tensors will be placed in host memory.
    """

    def AddToSelf(x):
      return math_ops.add(x, x)

    self._compare(AddToSelf, [np.array([7, 1, 3], dtype=np.int32)])

def testExplicitMarking(self):
    """Test explicit marking of operators to compile."""
    batch_size = 16
    image_size = 28 * 28
    num_classes = 10

    with ops.Graph().as_default():
      x = array_ops.placeholder(dtypes.float32)
      w = array_ops.placeholder(dtypes.float32)
      b = array_ops.placeholder(dtypes.float32)
      with jit_scope():
        y1 = math_ops.matmul(x, w)
      y2 = math_ops.add(y1, b)
      with jit_scope():
        y = math_ops.square(y2)

      dw = np.random.random_sample((image_size, num_classes)).astype(np.float32)
      db = np.random.random_sample((num_classes)).astype(np.float32)
      dx = np.random.random_sample((batch_size, image_size)).astype(np.float32)
      with session_lib.Session() as sess:
        run_metadata = config_pb2.RunMetadata()
        output = sess.run(y, {x: dx,
                              w: dw,
                              b: db},
                          run_metadata=run_metadata,
                          options=config_pb2.RunOptions(
                              trace_level=config_pb2.RunOptions.FULL_TRACE))

        # TODO(phawkins): really we would like to test that there were exactly
        # two kernel launches. However, we have no reliable way to determine
        # that.
        self.assert_(MetadataHasXlaLaunch(run_metadata))

        expected = np.square(np.dot(dx, dw) + db)
        self.assertAllClose(expected, output, rtol=1e-1)

def testBroadcasting(self):
    """Tests broadcasting behavior of an operator."""

    for dtype in self.numeric_types:
      self._testBinary(
          math_ops.add,
          np.array(3, dtype=dtype),
          np.array([10, 20], dtype=dtype),
          expected=np.array([13, 23], dtype=dtype))
      self._testBinary(
          math_ops.add,
          np.array([10, 20], dtype=dtype),
          np.array(4, dtype=dtype),
          expected=np.array([14, 24], dtype=dtype))

      # [1,3] x [4,1] => [4,3]
      self._testBinary(
          math_ops.add,
          np.array([[10, 20, 30]], dtype=dtype),
          np.array([[1], [2], [3], [4]], dtype=dtype),
          expected=np.array(
              [[11, 21, 31], [12, 22, 32], [13, 23, 33], [14, 24, 34]],
              dtype=dtype))

      # [3] * [4,1] => [4,3]
      self._testBinary(
          math_ops.add,
          np.array([10, 20, 30], dtype=dtype),
          np.array([[1], [2], [3], [4]], dtype=dtype),
          expected=np.array(
              [[11, 21, 31], [12, 22, 32], [13, 23, 33], [14, 24, 34]],
              dtype=dtype))

def tfadd(_):
  x = constant_op.constant([1], name='x_const')
  y = constant_op.constant([2], name='y_const')
  math_ops.add(x, y, name='x_y_sum')

def tfadd_with_ckpt(out_dir):
  x = array_ops.placeholder(dtypes.int32, name='x_hold')
  y = variables.Variable(constant_op.constant([0]), name='y_saved')
  math_ops.add(x, y, name='x_y_sum')

  init_op = variables.initialize_all_variables()
  saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1)
  with session.Session() as sess:
    sess.run(init_op)
    sess.run(y.assign(y + 42))
    # Without the checkpoint, the variable won't be set to 42.
    ckpt = '%s/test_graph_tfadd_with_ckpt.ckpt' % out_dir
    saver.save(sess, ckpt)

def tfmatmulandadd(_):
  # This tests multiple outputs.
  x = array_ops.placeholder(dtypes.float32, name='x_hold')
  y = array_ops.placeholder(dtypes.float32, name='y_hold')
  math_ops.matmul(x, y, name='x_y_prod')
  math_ops.add(x, y, name='x_y_sum')

def random_uniform(shape,
                   minval=0,
                   maxval=None,
                   dtype=dtypes.float32,
                   seed=None,
                   name=None):
  """Outputs random values from a uniform distribution.

  The generated values follow a uniform distribution in the range
  `[minval, maxval)`. The lower bound `minval` is included in the range, while
  the upper bound `maxval` is excluded.

  For floats, the default range is `[0, 1)`.  For ints, at least `maxval` must
  be specified explicitly.

  In the integer case, the random integers are slightly biased unless
  `maxval - minval` is an exact power of two.  The bias is small for values of
  `maxval - minval` significantly smaller than the range of the output (either
  `2**32` or `2**64`).

  Args:
    shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
    minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
      range of random values to generate.  Defaults to 0.
    maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on
      the range of random values to generate.  Defaults to 1 if `dtype` is
      floating point.
    dtype: The type of the output: `float32`, `float64`, `int32`, or `int64`.
    seed: A Python integer. Used to create a random seed for the distribution.
      See @{tf.set_random_seed}
      for behavior.
    name: A name for the operation (optional).

  Returns:
    A tensor of the specified shape filled with random uniform values.

  Raises:
    ValueError: If `dtype` is integral and `maxval` is not specified.
  """
  dtype = dtypes.as_dtype(dtype)
  if maxval is None:
    if dtype.is_integer:
      raise ValueError("Must specify maxval for integer dtype %r" % dtype)
    maxval = 1
  with ops.name_scope(name, "random_uniform", [shape, minval, maxval]) as name:
    shape = _ShapeTensor(shape)
    minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
    maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
    seed1, seed2 = random_seed.get_seed(seed)
    if dtype.is_integer:
      return gen_random_ops._random_uniform_int(
          shape, minval, maxval, seed=seed1, seed2=seed2, name=name)
    else:
      rnd = gen_random_ops._random_uniform(
          shape, dtype, seed=seed1, seed2=seed2)
      return math_ops.add(rnd * (maxval - minval), minval, name=name)

def bias_add(inputs,
             activation_fn=None,
             initializer=init_ops.zeros_initializer,
             regularizer=None,
             reuse=None,
             variables_collections=None,
             outputs_collections=None,
             trainable=True,
             scope=None):
  """Adds a bias to the inputs.

  Can be used as a normalizer function for conv2d and fully_connected.

  Args:
    inputs: a tensor of with at least rank 2 and value for the last dimension,
      e.g. `[batch_size, depth]`, `[None, None, None, depth]`.
    activation_fn: activation function, default set to None to skip it and
      maintain a linear activation.
    initializer: An initializer for the bias, defaults to 0.
    regularizer: A regularizer like the result of
      `l1_regularizer` or `l2_regularizer`.
    reuse: whether or not the layer and its variables should be reused. To be
      able to reuse the layer scope must be given.
    variables_collections: optional collections for the variables.
    outputs_collections: collections to add the outputs.
    trainable: If `True` also add variables to the graph collection
      `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
    scope: Optional scope for variable_scope.

  Returns:
    a tensor representing the result of adding biases to the inputs.
  """
  with variable_scope.variable_scope(scope, 'BiasAdd', [inputs],
                                     reuse=reuse) as sc:
    inputs = ops.convert_to_tensor(inputs)
    dtype = inputs.dtype.base_dtype
    num_features = utils.last_dimension(inputs.get_shape(), min_rank=2)
    biases_collections = utils.get_variable_collections(variables_collections,
                                                        'biases')
    biases = variables.model_variable('biases',
                                      shape=[num_features,],
                                      dtype=dtype,
                                      initializer=initializer,
                                      regularizer=regularizer,
                                      collections=biases_collections,
                                      trainable=trainable)
    outputs = nn.bias_add(inputs, biases)
    if activation_fn is not None:
      outputs = activation_fn(outputs)
    return utils.collect_named_outputs(outputs_collections,
                                       sc.original_name_scope, outputs)

def _auc_convert_hist_to_auc(hist_true_acc, hist_false_acc, nbins):
  """Convert histograms to auc.

  Args:
    hist_true_acc:  `Tensor` holding accumulated histogram of scores for records
      that were `True`.
    hist_false_acc:  `Tensor` holding accumulated histogram of scores for
      records that were `False`.
    nbins:  Integer number of bins in the histograms.

  Returns:
    Scalar `Tensor` estimating AUC.
  """
  # Note that this follows the "Approximating AUC" section in:
  # Efficient AUC learning curve calculation, R. R. Bouckaert,
  # AI'06 Proceedings of the 19th Australian joint conference on Artificial
  # Intelligence: advances in Artificial Intelligence
  # Pages 181-191.
  # Note that the above paper has an error, and we need to re-order our bins to
  # go from high to low score.

  # Normalize histogram so we get fraction in each bin.
  normed_hist_true = math_ops.truediv(hist_true_acc,
                                      math_ops.reduce_sum(hist_true_acc))
  normed_hist_false = math_ops.truediv(hist_false_acc,
                                       math_ops.reduce_sum(hist_false_acc))

  # These become delta x, delta y from the paper.
  delta_y_t = array_ops.reverse(normed_hist_true, [True], name='delta_y_t')
  delta_x_t = array_ops.reverse(normed_hist_false, [True], name='delta_x_t')

  # strict_1d_cumsum requires float32 args.
  delta_y_t = math_ops.cast(delta_y_t, dtypes.float32)
  delta_x_t = math_ops.cast(delta_x_t, dtypes.float32)

  # Trapezoidal integration, \int_0^1 0.5 * (y_t + y_{t-1}) dx_t
  y_t = _strict_1d_cumsum(delta_y_t, nbins)
  first_trap = delta_x_t[0] * y_t[0] / 2.0
  other_traps = delta_x_t[1:] * (y_t[1:] + y_t[:nbins - 1]) / 2.0
  return math_ops.add(first_trap, math_ops.reduce_sum(other_traps), name='auc')


# TODO(langmore) Remove once a faster cumsum (accumulate_sum) Op is available.
# Also see if cast to float32 above can be removed with new cumsum.
# See:  https://github.com/tensorflow/tensorflow/issues/813

def dense_to_sparse_tensor(dense_tensor, ignore_value=None):
  """Converts a dense Tensor to a SparseTensor, dropping ignore_value cells.

  Args:
    dense_tensor: A `Tensor`.
    ignore_value: Entries in `dense_tensor` equal to this value will be
      absent from the return `SparseTensor`. If `None`, default value of
      dense_tensor's dtype will be used (e.g. '' for `str`, 0 for `int`).

  Returns:
    A `SparseTensor` with the same shape as `dense_tensor`.

  Raises:
    ValueError: when `dense_tensor`'s rank is `None`.
  """
  with ops.name_scope("DenseToSparseTensor"):
    dense_t = ops.convert_to_tensor(dense_tensor)
    if dense_t.get_shape().ndims is None:
      # TODO(b/32318825): Implement dense_to_sparse_tensor for undefined rank.
      raise ValueError("dense_tensor.get_shape() should be defined, got None.")
    if ignore_value is None:
      if dense_t.dtype == dtypes.string:
        # Exception due to TF strings are converted to numpy objects by default.
        ignore_value = ""
      else:
        ignore_value = dense_t.dtype.as_numpy_dtype()
    dense_shape = math_ops.cast(array_ops.shape(dense_t), dtypes.int64)
    indices = array_ops.where(
        math_ops.not_equal(dense_t, math_ops.cast(ignore_value, dense_t.dtype)))
    index_dims = len(dense_t.get_shape())
    # Flattens the tensor and indices for use with gather.
    flat_tensor = array_ops.reshape(dense_t, [-1])
    flat_indices = indices[:, index_dims - 1]
    # Computes the correct flattened indices for 2d (or higher) tensors.
    if index_dims > 1:
      higher_dims = indices[:, :index_dims - 1]
      shape_multipliers = array_ops.pack(
          _multiplier_helper(array_ops.unpack(dense_shape)[1:]))
      offsets = math_ops.reduce_sum(
          math_ops.mul(higher_dims, shape_multipliers), reduction_indices=[1])
      flat_indices = math_ops.add(flat_indices, offsets)
    values = array_ops.gather(flat_tensor, flat_indices)
    return sparse_tensor.SparseTensor(indices, values, dense_shape)

def _remove_squeezable_dimensions(predictions, labels, weights):
  """Squeeze last dim if needed.

  Squeezes `predictions` and `labels` if their rank differs by 1.
  Squeezes `weights` if its rank is 1 more than the new rank of `predictions`

  This will use static shape if available. Otherwise, it will add graph
  operations, which could result in a performance hit.

  Args:
    predictions: Predicted values, a `Tensor` of arbitrary dimensions.
    labels: Label values, a `Tensor` whose dimensions match `predictions`.
    weights: optional `weights` tensor. It will be squeezed if its rank is 1
      more than the new rank of `predictions`

  Returns:
    Tuple of `predictions`, `labels` and `weights`, possibly with the last
    dimension squeezed.
  """
  predictions, labels = tensor_util.remove_squeezable_dimensions(
      predictions, labels)
  predictions.get_shape().assert_is_compatible_with(labels.get_shape())

  if weights is not None:
    predictions_shape = predictions.get_shape()
    predictions_rank = predictions_shape.ndims
    weights_shape = weights.get_shape()
    weights_rank = weights_shape.ndims

    if (predictions_rank is not None) and (weights_rank is not None):
      # Use static rank.
      if weights_rank - predictions_rank == 1:
        weights = array_ops.squeeze(weights, [-1])
    elif (weights_rank is None) or (
        weights_shape.dims[-1].is_compatible_with(1)):
      # Use dynamic rank
      weights = control_flow_ops.cond(
          math_ops.equal(array_ops.rank(weights),
                         math_ops.add(array_ops.rank(predictions), 1)),
          lambda: array_ops.squeeze(weights, [-1]),
          lambda: weights)
  return predictions, labels, weights

def _auc_convert_hist_to_auc(hist_true_acc, hist_false_acc, nbins):
  """Convert histograms to auc.

  Args:
    hist_true_acc:  `Tensor` holding accumulated histogram of scores for records
      that were `True`.
    hist_false_acc:  `Tensor` holding accumulated histogram of scores for
      records that were `False`.
    nbins:  Integer number of bins in the histograms.

  Returns:
    Scalar `Tensor` estimating AUC.
  """
  # Note that this follows the "Approximating AUC" section in:
  # Efficient AUC learning curve calculation, R. R. Bouckaert,
  # AI'06 Proceedings of the 19th Australian joint conference on Artificial
  # Intelligence: advances in Artificial Intelligence
  # Pages 181-191.
  # Note that the above paper has an error, and we need to re-order our bins to
  # go from high to low score.

  # Normalize histogram so we get fraction in each bin.
  normed_hist_true = math_ops.truediv(hist_true_acc,
                                      math_ops.reduce_sum(hist_true_acc))
  normed_hist_false = math_ops.truediv(hist_false_acc,
                                       math_ops.reduce_sum(hist_false_acc))

  # These become delta x, delta y from the paper.
  delta_y_t = array_ops.reverse(normed_hist_true, [True], name='delta_y_t')
  delta_x_t = array_ops.reverse(normed_hist_false, [True], name='delta_x_t')

  # strict_1d_cumsum requires float32 args.
  delta_y_t = math_ops.cast(delta_y_t, dtypes.float32)
  delta_x_t = math_ops.cast(delta_x_t, dtypes.float32)

  # Trapezoidal integration, \int_0^1 0.5 * (y_t + y_{t-1}) dx_t
  y_t = _strict_1d_cumsum(delta_y_t, nbins)
  first_trap = delta_x_t[0] * y_t[0] / 2.0
  other_traps = delta_x_t[1:] * (y_t[1:] + y_t[:nbins - 1]) / 2.0
  return math_ops.add(first_trap, math_ops.reduce_sum(other_traps), name='auc')


# TODO(langmore) Remove once a faster cumsum (accumulate_sum) Op is available.
# Also see if cast to float32 above can be removed with new cumsum.
# See:  https://github.com/tensorflow/tensorflow/issues/813