Python tensorflow.python.ops.math_ops.greater_equal() Examples

def greater_equal(x, y):
  """Element-wise truth value of (x >= y).

  Arguments:
      x: Tensor or variable.
      y: Tensor or variable.

  Returns:
      A bool tensor.
  """
  return math_ops.greater_equal(x, y) 

def _MaximumGrad(op, grad):
  """Returns grad*(x > y, x <= y) with type of grad."""
  return _MaximumMinimumGrad(op, grad, math_ops.greater_equal) 

def assert_greater_equal(x, y, data=None, summarize=None, message=None,
                         name=None):
  """Assert the condition `x >= y` holds element-wise.

  Example of adding a dependency to an operation:

  ```python
  with tf.control_dependencies([tf.assert_greater_equal(x, y)]):
    output = tf.reduce_sum(x)
  ```

  This condition holds if for every pair of (possibly broadcast) elements
  `x[i]`, `y[i]`, we have `x[i] >= y[i]`.
  If both `x` and `y` are empty, this is trivially satisfied.

  Args:
    x:  Numeric `Tensor`.
    y:  Numeric `Tensor`, same dtype as and broadcastable to `x`.
    data:  The tensors to print out if the condition is False.  Defaults to
      error message and first few entries of `x`, `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).  Defaults to
      "assert_greater_equal"

  Returns:
    Op that raises `InvalidArgumentError` if `x >= y` is False.
  """
  message = message or ''
  with ops.name_scope(name, 'assert_greater_equal', [x, y, data]):
    x = ops.convert_to_tensor(x, name='x')
    y = ops.convert_to_tensor(y, name='y')
    if data is None:
      data = [
          message,
          'Condition x >= y did not hold element-wise:'
          'x (%s) = ' % x.name, x, 'y (%s) = ' % y.name, y
      ]
    condition = math_ops.reduce_all(math_ops.greater_equal(x, y))
    return control_flow_ops.Assert(condition, data, summarize=summarize) 

def _prune_invalid_ids(sparse_ids, sparse_weights):
  """Prune invalid IDs (< 0) from the input ids and weights."""
  is_id_valid = math_ops.greater_equal(sparse_ids.values, 0)
  if sparse_weights is not None:
    is_id_valid = math_ops.logical_and(
        is_id_valid, math_ops.greater(sparse_weights.values, 0))
  sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_id_valid)
  if sparse_weights is not None:
    sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_id_valid)
  return sparse_ids, sparse_weights 

def _accuracy_at_threshold(threshold):

  def _accuracy_metric(predictions, labels, weights=None):
    threshold_predictions = math_ops.to_float(
        math_ops.greater_equal(predictions, threshold))
    return metrics_lib.streaming_accuracy(predictions=threshold_predictions,
                                          labels=labels,
                                          weights=weights)

  return _accuracy_metric 

def mean_reciprocal_rank(labels, predictions, weights=None, name=None):
    """Computes mean reciprocal rank (MRR).

    Args:
      labels: A `Tensor` of the same shape as `predictions`. A value >= 1 means a
        relevant example.
      predictions: A `Tensor` with shape [batch_size, list_size]. Each value is
        the ranking score of the corresponding example.
      weights: A `Tensor` of the same shape of predictions or [batch_size, 1]. The
        former case is per-example and the latter case is per-list.
      name: A string used as the name for this metric.

    Returns:
      A metric for the weighted mean reciprocal rank of the batch.
    """
    with ops.name_scope(name, 'mean_reciprocal_rank',
                        (labels, predictions, weights)):
        _, list_size = array_ops.unstack(array_ops.shape(predictions))
        labels, predictions, weights, topn = _prepare_and_validate_params(
            labels, predictions, weights, list_size)
        sorted_labels, = utils.sort_by_scores(predictions, [labels], topn=topn)
        # Relevance = 1.0 when labels >= 1.0 to accommodate graded relevance.
        relevance = math_ops.to_float(
            math_ops.greater_equal(
                sorted_labels, 1.0))
        reciprocal_rank = 1.0 / math_ops.to_float(math_ops.range(1, topn + 1))
        # MRR has a shape of [batch_size, 1]
        mrr = math_ops.reduce_max(
            relevance * reciprocal_rank, axis=1, keepdims=True)
        return math_ops.reduce_mean(
            mrr * array_ops.ones_like(weights) * weights) 

def expected_reciprocal_rank(
        labels, predictions, weights=None, topn=None, name=None):
    """Computes expected reciprocal rank (ERR).

    Args:
      labels: A `Tensor` of the same shape as `predictions`. A value >= 1 means a
        relevant example.
      predictions: A `Tensor` with shape [batch_size, list_size]. Each value is
        the ranking score of the corresponding example.
      weights: A `Tensor` of the same shape of predictions or [batch_size, 1]. The
        former case is per-example and the latter case is per-list.
      topn: A cutoff for how many examples to consider for this metric.
      name: A string used as the name for this metric.

    Returns:
      A metric for the weighted expected reciprocal rank of the batch.
    """
    with ops.name_scope(name, 'expected_reciprocal_rank',
                        (labels, predictions, weights)):
        labels, predictions, weights, topn = _prepare_and_validate_params(
            labels, predictions, weights, topn)
        sorted_labels, sorted_weights = utils.sort_by_scores(
            predictions, [labels, weights], topn=topn)
        _, list_size = array_ops.unstack(array_ops.shape(sorted_labels))

        relevance = (math_ops.pow(2.0, sorted_labels) - 1) / \
            math_ops.pow(2.0, RankingMetricKey.MAX_LABEL)
        non_rel = tf.math.cumprod(1.0 - relevance, axis=1) / (1.0 - relevance)
        reciprocal_rank = 1.0 / \
            math_ops.to_float(math_ops.range(1, list_size + 1))
        mask = math_ops.to_float(math_ops.greater_equal(
            reciprocal_rank, 1.0 / (topn + 1)))
        reciprocal_rank = reciprocal_rank * mask
        # ERR has a shape of [batch_size, 1]
        err = math_ops.reduce_sum(
            relevance * non_rel * reciprocal_rank * sorted_weights, axis=1, keepdims=True)
        return math_ops.reduce_mean(err) 

def precision(labels, predictions, weights=None, topn=None, name=None):
    """Computes precision as weighted average of relevant examples.

    Args:
      labels: A `Tensor` of the same shape as `predictions`. A value >= 1 means a
        relevant example.
      predictions: A `Tensor` with shape [batch_size, list_size]. Each value is
        the ranking score of the corresponding example.
      weights: A `Tensor` of the same shape of predictions or [batch_size, 1]. The
        former case is per-example and the latter case is per-list.
      topn: A cutoff for how many examples to consider for this metric.
      name: A string used as the name for this metric.

    Returns:
      A metric for the weighted precision of the batch.
    """
    with ops.name_scope(name, 'precision', (labels, predictions, weights)):
        labels, predictions, weights, topn = _prepare_and_validate_params(
            labels, predictions, weights, topn)
        sorted_labels, sorted_weights = utils.sort_by_scores(
            predictions, [labels, weights], topn=topn)
        # Relevance = 1.0 when labels >= 1.0.
        relevance = math_ops.to_float(
            math_ops.greater_equal(
                sorted_labels, 1.0))
        per_list_precision = _safe_div(
            math_ops.reduce_sum(relevance * sorted_weights, 1, keepdims=True),
            math_ops.reduce_sum(
                array_ops.ones_like(relevance) * sorted_weights, 1, keepdims=True))
        # per_list_weights are computed from the whole list to avoid the problem of
        # 0 when there is no relevant example in topn.
        per_list_weights = _per_example_weights_to_per_list_weights(
            weights, math_ops.to_float(math_ops.greater_equal(labels, 1.0)))
        return math_ops.reduce_mean(per_list_precision * per_list_weights) 

def is_mask_update_iter(self, global_step, last_update_step):
    """Function for checking if the current step is a mask update step.

    It also creates the drop_fraction op and assigns it to the self object.

    Args:
      global_step: tf.Variable(int), current training step.
      last_update_step: tf.Variable(int), holding the last iteration the mask
        is updated. Used to determine whether current iteration is a mask
        update step.


    Returns:
      bool, whether the current iteration is a mask_update step.
    """
    gs_dtype = global_step.dtype
    self._begin_step = math_ops.cast(self._begin_step, gs_dtype)
    self._end_step = math_ops.cast(self._end_step, gs_dtype)
    self._frequency = math_ops.cast(self._frequency, gs_dtype)
    is_step_within_update_range = math_ops.logical_and(
        math_ops.greater_equal(global_step, self._begin_step),
        math_ops.logical_or(
            math_ops.less_equal(global_step, self._end_step),
            # If _end_step is negative, we never stop updating the mask.
            # In other words we update the mask with given frequency until the
            # training ends.
            math_ops.less(self._end_step, 0)))
    is_update_step = math_ops.less_equal(
        math_ops.add(last_update_step, self._frequency), global_step)
    is_mask_update_iter_op = math_ops.logical_and(
        is_step_within_update_range, is_update_step)
    self.drop_fraction = self.get_drop_fraction(global_step,
                                                is_mask_update_iter_op)
    return is_mask_update_iter_op 

def _test_greater_equal(data):
    """ One iteration of greater_equal """
    return _test_elemwise(math_ops.greater_equal, data)
#######################################################################
# Less
# ---- 

def test_forward_rel_ops():
    t1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    t2 = np.array([[9, 8, 7], [6, 5, 4], [3, 2, 1]])
    _test_forward_rel_op([t1, t2], math_ops.less)
    _test_forward_rel_op([t1, t2], math_ops.greater)
    _test_forward_rel_op([t1, t2], math_ops.less_equal)
    _test_forward_rel_op([t1, t2], math_ops.greater_equal)
    _test_forward_rel_op([t1, t2], math_ops.equal)
    _test_forward_rel_op([t1, t2], math_ops.not_equal)

#######################################################################
# ExpandDims
# ---------- 

def _accuracy_at_threshold(threshold):

  def _accuracy_metric(predictions, labels, weights=None):
    threshold_predictions = math_ops.to_float(
        math_ops.greater_equal(predictions, threshold))
    return metrics_lib.streaming_accuracy(predictions=threshold_predictions,
                                          labels=labels,
                                          weights=weights)

  return _accuracy_metric 

def _accuracy_at_threshold(labels, predictions, weights, threshold, name=None):
  with ops.name_scope(
      name, 'accuracy_at_%s' % threshold,
      (predictions, labels, weights, threshold)) as scope:
    threshold_predictions = math_ops.to_float(
        math_ops.greater_equal(predictions, threshold))
    return metrics_lib.accuracy(
        labels=labels, predictions=threshold_predictions, weights=weights,
        name=scope) 

def assert_greater_equal(x, y, data=None, summarize=None, message=None,
                         name=None):
  """Assert the condition `x >= y` holds element-wise.

  Example of adding a dependency to an operation:

  ```python
  with tf.control_dependencies([tf.assert_greater_equal(x, y)]):
    output = tf.reduce_sum(x)
  ```

  This condition holds if for every pair of (possibly broadcast) elements
  `x[i]`, `y[i]`, we have `x[i] >= y[i]`.
  If both `x` and `y` are empty, this is trivially satisfied.

  Args:
    x:  Numeric `Tensor`.
    y:  Numeric `Tensor`, same dtype as and broadcastable to `x`.
    data:  The tensors to print out if the condition is False.  Defaults to
      error message and first few entries of `x`, `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).  Defaults to
      "assert_greater_equal"

  Returns:
    Op that raises `InvalidArgumentError` if `x >= y` is False.
  """
  message = message or ''
  with ops.name_scope(name, 'assert_greater_equal', [x, y, data]):
    x = ops.convert_to_tensor(x, name='x')
    y = ops.convert_to_tensor(y, name='y')
    if data is None:
      data = [
          message,
          'Condition x >= y did not hold element-wise:'
          'x (%s) = ' % x.name, x, 'y (%s) = ' % y.name, y
      ]
    condition = math_ops.reduce_all(math_ops.greater_equal(x, y))
    return control_flow_ops.Assert(condition, data, summarize=summarize) 

def _prune_invalid_ids(sparse_ids, sparse_weights):
  """Prune invalid IDs (< 0) from the input ids and weights."""
  is_id_valid = math_ops.greater_equal(sparse_ids.values, 0)
  if sparse_weights is not None:
    is_id_valid = math_ops.logical_and(
        is_id_valid, math_ops.greater(sparse_weights.values, 0))
  sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_id_valid)
  if sparse_weights is not None:
    sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_id_valid)
  return sparse_ids, sparse_weights 

def assert_greater_equal(x, y, data=None, summarize=None, message=None,
                         name=None):
  """Assert the condition `x >= y` holds element-wise.

  Example of adding a dependency to an operation:

  ```python
  with tf.control_dependencies([tf.assert_greater_equal(x, y)]):
    output = tf.reduce_sum(x)
  ```

  This condition holds if for every pair of (possibly broadcast) elements
  `x[i]`, `y[i]`, we have `x[i] >= y[i]`.
  If both `x` and `y` are empty, this is trivially satisfied.

  Args:
    x:  Numeric `Tensor`.
    y:  Numeric `Tensor`, same dtype as and broadcastable to `x`.
    data:  The tensors to print out if the condition is False.  Defaults to
      error message and first few entries of `x`, `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).  Defaults to
      "assert_greater_equal"

  Returns:
    Op that raises `InvalidArgumentError` if `x >= y` is False.
  """
  message = message or ''
  with ops.name_scope(name, 'assert_greater_equal', [x, y, data]):
    x = ops.convert_to_tensor(x, name='x')
    y = ops.convert_to_tensor(y, name='y')
    if data is None:
      data = [
          message,
          'Condition x >= y did not hold element-wise: x = ', x.name, x, 'y = ',
          y.name, y
      ]
    condition = math_ops.reduce_all(math_ops.greater_equal(x, y))
    return control_flow_ops.Assert(condition, data, summarize=summarize) 

def _MaximumGrad(op, grad):
  """Returns grad*(x > y, x <= y) with type of grad."""
  return _MaximumMinimumGrad(op, grad, math_ops.greater_equal) 

def _accuracy_at_threshold(threshold):

  def _accuracy_metric(predictions, labels, weights=None):
    threshold_predictions = math_ops.to_float(
        math_ops.greater_equal(predictions, threshold))
    return metrics_lib.streaming_accuracy(
        predictions=threshold_predictions, labels=labels, weights=weights)

  return _accuracy_metric 

def _prune_invalid_ids(sparse_ids, sparse_weights):
  """Prune invalid IDs (< 0) from the input ids and weights."""
  is_id_valid = math_ops.greater_equal(sparse_ids.values, 0)
  if sparse_weights is not None:
    is_id_valid = math_ops.logical_and(
        is_id_valid, math_ops.greater(sparse_weights.values, 0))
  sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_id_valid)
  if sparse_weights is not None:
    sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_id_valid)
  return sparse_ids, sparse_weights 

def _accuracy_at_threshold(threshold):

  def _accuracy_metric(predictions, labels, weights=None):
    threshold_predictions = math_ops.to_float(
        math_ops.greater_equal(predictions, threshold))
    return metric_ops.streaming_accuracy(
        predictions=threshold_predictions, labels=labels, weights=weights)

  return _accuracy_metric 

def __ge__(self, other):
    return greater_equal(self, other) 

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 _accuracy_at_threshold(threshold):

  def _accuracy_metric(predictions, labels, weights=None):
    threshold_predictions = math_ops.to_float(
        math_ops.greater_equal(predictions, threshold))
    return metrics_lib.streaming_accuracy(
        predictions=threshold_predictions, labels=labels, weights=weights)

  return _accuracy_metric 

def _prune_invalid_ids(sparse_ids, sparse_weights):
  """Prune invalid IDs (< 0) from the input ids and weights."""
  is_id_valid = math_ops.greater_equal(sparse_ids.values, 0)
  if sparse_weights is not None:
    is_id_valid = math_ops.logical_and(
        is_id_valid, math_ops.greater(sparse_weights.values, 0))
  sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_id_valid)
  if sparse_weights is not None:
    sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_id_valid)
  return sparse_ids, sparse_weights 

def _accuracy_at_threshold(threshold):

  def _accuracy_metric(predictions, labels, weights=None):
    threshold_predictions = math_ops.to_float(
        math_ops.greater_equal(predictions, threshold))
    return metric_ops.streaming_accuracy(
        predictions=threshold_predictions, labels=labels, weights=weights)

  return _accuracy_metric 

def __ge__(self, other):
    return greater_equal(self, other) 

def assert_greater_equal(x, y, data=None, summarize=None, message=None,
                         name=None):
  """Assert the condition `x >= y` holds element-wise.

  Example of adding a dependency to an operation:

  ```python
  with tf.control_dependencies([tf.assert_greater_equal(x, y)]):
    output = tf.reduce_sum(x)
  ```

  This condition holds if for every pair of (possibly broadcast) elements
  `x[i]`, `y[i]`, we have `x[i] >= y[i]`.
  If both `x` and `y` are empty, this is trivially satisfied.

  Args:
    x:  Numeric `Tensor`.
    y:  Numeric `Tensor`, same dtype as and broadcastable to `x`.
    data:  The tensors to print out if the condition is False.  Defaults to
      error message and first few entries of `x`, `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).  Defaults to
      "assert_greater_equal"

  Returns:
    Op that raises `InvalidArgumentError` if `x >= y` is False.
  """
  message = message or ''
  with ops.name_scope(name, 'assert_greater_equal', [x, y, data]):
    x = ops.convert_to_tensor(x, name='x')
    y = ops.convert_to_tensor(y, name='y')
    if data is None:
      data = [
          message,
          'Condition x >= y did not hold element-wise: x = ', x.name, x, 'y = ',
          y.name, y
      ]
    condition = math_ops.reduce_all(math_ops.greater_equal(x, y))
    return control_flow_ops.Assert(condition, data, summarize=summarize) 

def _streaming_accuracy_at_threshold(predictions, labels, weights, threshold):
  threshold_predictions = math_ops.to_float(
      math_ops.greater_equal(predictions, threshold))
  return metrics_lib.streaming_accuracy(
      predictions=threshold_predictions, labels=labels, weights=weights) 

def _MaximumGrad(op, grad):
  """Returns grad*(x > y, x <= y) with type of grad."""
  return _MaximumMinimumGrad(op, grad, math_ops.greater_equal) 

def _prune_invalid_ids(sparse_ids, sparse_weights):
  """Prune invalid IDs (< 0) from the input ids and weights."""
  is_id_valid = math_ops.greater_equal(sparse_ids.values, 0)
  if sparse_weights is not None:
    is_id_valid = math_ops.logical_and(
        is_id_valid, math_ops.greater(sparse_weights.values, 0))
  sparse_ids = sparse_ops.sparse_retain(sparse_ids, is_id_valid)
  if sparse_weights is not None:
    sparse_weights = sparse_ops.sparse_retain(sparse_weights, is_id_valid)
  return sparse_ids, sparse_weights