Python tensorflow.python.ops.math_ops.log1p() Examples

def _sample_n(self, n, seed=None):
    shape = array_ops.concat([[n], self.batch_shape_tensor()], 0)
    # Uniform variates must be sampled from the open-interval `(-1, 1)` rather
    # than `[-1, 1)`. In the case of `(0, 1)` we'd use
    # `np.finfo(self.dtype.as_numpy_dtype).tiny` because it is the smallest,
    # positive, "normal" number. However, the concept of subnormality exists
    # only at zero; here we need the smallest usable number larger than -1,
    # i.e., `-1 + eps/2`.
    uniform_samples = random_ops.random_uniform(
        shape=shape,
        minval=np.nextafter(self.dtype.as_numpy_dtype(-1.),
                            self.dtype.as_numpy_dtype(0.)),
        maxval=1.,
        dtype=self.dtype,
        seed=seed)
    return (self.loc - self.scale * math_ops.sign(uniform_samples) *
            math_ops.log1p(-math_ops.abs(uniform_samples))) 

def _sample_n(self, n, seed=None):
    # Uniform variates must be sampled from the open-interval `(0, 1)` rather
    # than `[0, 1)`. To do so, we use `np.finfo(self.dtype.as_numpy_dtype).tiny`
    # because it is the smallest, positive, "normal" number. A "normal" number
    # is such that the mantissa has an implicit leading 1. Normal, positive
    # numbers x, y have the reasonable property that, `x + y >= max(x, y)`. In
    # this case, a subnormal number (i.e., np.nextafter) can cause us to sample
    # 0.
    uniform = random_ops.random_uniform(
        shape=array_ops.concat([[n], self.batch_shape_tensor()], 0),
        minval=np.finfo(self.dtype.as_numpy_dtype).tiny,
        maxval=1.,
        dtype=self.dtype,
        seed=seed)
    sampled = math_ops.log(uniform) - math_ops.log1p(-1. * uniform)
    return sampled * self.scale + self.loc 

def _sample_n(self, n, seed=None):
    # Uniform variates must be sampled from the open-interval `(0, 1)` rather
    # than `[0, 1)`. To do so, we use `np.finfo(self.dtype.as_numpy_dtype).tiny`
    # because it is the smallest, positive, "normal" number. A "normal" number
    # is such that the mantissa has an implicit leading 1. Normal, positive
    # numbers x, y have the reasonable property that, `x + y >= max(x, y)`. In
    # this case, a subnormal number (i.e., np.nextafter) can cause us to sample
    # 0.
    sampled = random_ops.random_uniform(
        array_ops.concat([[n], array_ops.shape(self._probs)], 0),
        minval=np.finfo(self.dtype.as_numpy_dtype).tiny,
        maxval=1.,
        seed=seed,
        dtype=self.dtype)

    return math_ops.floor(
        math_ops.log(sampled) / math_ops.log1p(-self.probs)) 

def _log_unnormalized_prob(self, x):
    y = (x - self.mu) / self.sigma  # Abs(sigma) superfluous.
    return -0.5 * (self.df + 1.) * math_ops.log1p(y**2. / self.df) 

def _forward_log_det_jacobian(self, x):
    x = self._maybe_assert_valid_x(x)
    if self.shaper is None:
      raise ValueError("Jacobian cannot be computed with unknown event_ndims")
    _, _, event_dims = self.shaper.get_dims(x)
    if self.power == 0.:
      return math_ops.reduce_sum(x, reduction_indices=event_dims)
    return (1. / self.power - 1.) * math_ops.reduce_sum(
        math_ops.log1p(x * self.power),
        reduction_indices=event_dims) 

def _forward(self, x):
    x = self._maybe_assert_valid_x(x)
    if self.power == 0.:
      return math_ops.exp(x)
    # TODO(jvdillon): If large x accuracy is an issue, consider using
    # (1. + x * self.power)**(1. / self.power) when x >> 1.
    return math_ops.exp(math_ops.log1p(x * self.power) / self.power) 

def _log_unnormalized_prob(self, x):
    y = (x - self.mu) / self.sigma  # Abs(sigma) superfluous.
    return -0.5 * (self.df + 1.) * math_ops.log1p(y**2. / self.df) 

def _log_unnormalized_prob(self, x):
    y = (x - self.loc) / self.scale  # Abs(scale) superfluous.
    return -0.5 * (self.df + 1.) * math_ops.log1p(y**2. / self.df) 

def _call_log_survival_function(self, value, name, **kwargs):
    with self._name_scope(name, values=[value]):
      value = ops.convert_to_tensor(value, name="value")
      try:
        return self._log_survival_function(value, **kwargs)
      except NotImplementedError as original_exception:
        try:
          return math_ops.log1p(-self.cdf(value, **kwargs))
        except NotImplementedError:
          raise original_exception 

def _log_unnormalized_prob(self, x):
    x = self._maybe_assert_valid_sample(x)
    return ((self.concentration1 - 1.) * math_ops.log(x)
            + (self.concentration0 - 1.) * math_ops.log1p(-x)) 

def discounted_cumulative_gain(labels,
                               predictions,
                               weights=None,
                               topn=None,
                               name=None):
    """Computes discounted cumulative gain (DCG).

    Args:
      labels: A `Tensor` of the same shape as `predictions`.
      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 discounted cumulative gain of the batch.
    """
    with ops.name_scope(name, 'discounted_cumulative_gain',
                        (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)
        dcg = _discounted_cumulative_gain(sorted_labels,
                                          sorted_weights) * math_ops.log1p(1.0)
        per_list_weights = _per_example_weights_to_per_list_weights(
            weights=weights,
            relevance=math_ops.pow(2.0, math_ops.to_float(labels)) - 1.0)
        return math_ops.reduce_mean(
            _safe_div(dcg, per_list_weights) * per_list_weights) 

def focal_loss_alpha(labels=[], logits=[], pos_weights=[], gamma=2., clips=[], name='focal_loss'):
    """
    Add focal loss weigths to the wigthted sigmoid cross entropy
    :return:
    """
    batchsize = labels.get_shape().as_list()[0]
    n_classes = labels.get_shape().as_list()[1]

    with tf.variable_scope(name) as vs:
        # first get a sigmoid to determine the focal loss weigths:
        sigmoid_logits = tf.nn.sigmoid(logits)
        # determine the focal loss weigths:
        labels = math_ops.to_float(labels)
        sigmoid_logits.get_shape().assert_is_compatible_with(labels.get_shape())
        preds = array_ops.where(math_ops.equal(labels, 1.), sigmoid_logits, 1. - sigmoid_logits)
        focal_weights = (math_ops.subtract(1., preds)) ** gamma
        print(focal_weights)

        # clip the weights at E-3 and E3
        up_clip = math_ops.multiply(tf.ones([batchsize, n_classes]), clips[1])
        low_clip = math_ops.multiply(tf.ones([batchsize, n_classes]), clips[0])
        focal_weights = array_ops.where(math_ops.greater(focal_weights, clips[1]), up_clip, focal_weights)
        focal_weights = array_ops.where(math_ops.less(focal_weights, clips[0]), low_clip, focal_weights)
        log_weight = 1. + (pos_weights - 1.) * labels

        # now put them into a weighted softmax ce:
        loss = math_ops.multiply(math_ops.add((1. - labels) * logits,
                         log_weight * (math_ops.log1p(math_ops.exp(-math_ops.abs(logits))) + nn_ops.relu(-logits))),
                                 focal_weights, name='sc_entropy')
        return loss 

def focal_loss(labels=[], logits=[], pos_weights=[], gamma=2., clips=[], name='focal_loss'):
    """
    Add focal loss weigths to the wigthted sigmoid cross entropy
    :return:
    """
    batchsize = labels.get_shape().as_list()[0]
    n_classes = labels.get_shape().as_list()[1]

    with tf.variable_scope(name) as vs:
        # first get a sigmoid to determine the focal loss weigths:
        sigmoid_logits = tf.nn.sigmoid(logits)
        # determine the focal loss weigths:
        labels = math_ops.to_float(labels)
        sigmoid_logits.get_shape().assert_is_compatible_with(labels.get_shape())
        preds = array_ops.where(math_ops.equal(labels, 1.), sigmoid_logits, 1. - sigmoid_logits)
        focal_weights = (math_ops.subtract(1., preds)) ** gamma
        print(focal_weights)

        # clip the weights at E-3 and E3
        up_clip = math_ops.multiply(tf.ones([batchsize, n_classes]), clips[1])
        low_clip = math_ops.multiply(tf.ones([batchsize, n_classes]), clips[0])
        focal_weights = array_ops.where(math_ops.greater(focal_weights, clips[1]), up_clip, focal_weights)
        focal_weights = array_ops.where(math_ops.less(focal_weights, clips[0]), low_clip, focal_weights)
        log_weight = 1. + (pos_weights - 1.) * labels

        # now put them into a weighted softmax ce:
        loss = math_ops.multiply(math_ops.add((1. - labels) * logits,
                        log_weight * (math_ops.log1p(math_ops.exp(-math_ops.abs(logits))) + nn_ops.relu(-logits))),
               focal_weights, name='sc_entropy')
        return loss 

def _forward_log_det_jacobian(self, x):
    x = self._maybe_assert_valid_x(x)
    if self.shaper is None:
      raise ValueError("Jacobian cannot be computed with unknown event_ndims")
    _, _, event_dims = self.shaper.get_dims(x)
    if self.power == 0.:
      return math_ops.reduce_sum(x, reduction_indices=event_dims)
    return (1. / self.power - 1.) * math_ops.reduce_sum(
        math_ops.log1p(x * self.power),
        reduction_indices=event_dims) 

def _forward(self, x):
    x = self._maybe_assert_valid_x(x)
    if self.power == 0.:
      return math_ops.exp(x)
    # TODO(jvdillon): If large x accuracy is an issue, consider using
    # (1. + x * self.power)**(1. / self.power) when x >> 1.
    return math_ops.exp(math_ops.log1p(x * self.power) / self.power) 

def _log_unnormalized_prob(self, positive_counts):
    if self.validate_args:
      positive_counts = distribution_util.embed_check_nonnegative_discrete(
          positive_counts, check_integer=True)
    return self.total_count * math_ops.log1p(
        -self.probs) + positive_counts * math_ops.log(self.probs) 

def _log_unnormalized_prob(self, counts):
    counts = self._maybe_assert_valid_sample(counts)
    return (counts * math_ops.log(self.probs) +
            (self.total_count - counts) * math_ops.log1p(-self.probs)) 

def _inverse_log_det_jacobian(self, y):
    return -math_ops.log(y) - math_ops.log1p(-y) 

def _inverse(self, y):
    return math_ops.log(y) - math_ops.log1p(-y) 

def _forward(self, x):
    x = self._maybe_assert_valid_x(x)
    if self.power == 0.:
      return math_ops.exp(x)
    # If large x accuracy is an issue, consider using:
    # (1. + x * self.power)**(1. / self.power) when x >> 1.
    return math_ops.exp(math_ops.log1p(x * self.power) / self.power) 

def _log_prob(self, counts):
    if self.validate_args:
      counts = distribution_util.embed_check_nonnegative_discrete(
          counts, check_integer=True)
    counts *= array_ops.ones_like(self.probs)
    probs = self.probs * array_ops.ones_like(counts)

    safe_domain = array_ops.where(
        math_ops.equal(counts, 0.),
        array_ops.zeros_like(probs),
        probs)
    return counts * math_ops.log1p(-safe_domain) + math_ops.log(probs) 

def _cdf(self, counts):
    if self.validate_args:
      # We set `check_integer=False` since the CDF is defined on whole real
      # line.
      counts = math_ops.floor(
          distribution_util.embed_check_nonnegative_discrete(
              counts, check_integer=False))
    counts *= array_ops.ones_like(self.probs)
    return array_ops.where(
        counts < 0.,
        array_ops.zeros_like(counts),
        -math_ops.expm1(
            (counts + 1) * math_ops.log1p(-self.probs))) 

def _log_unnormalized_prob(self, x):
    y = (x - self.loc) / self.scale  # Abs(scale) superfluous.
    return -0.5 * (self.df + 1.) * math_ops.log1p(y**2. / self.df) 

def _call_log_survival_function(self, value, name, **kwargs):
    with self._name_scope(name, values=[value]):
      value = ops.convert_to_tensor(value, name="value")
      try:
        return self._log_survival_function(value, **kwargs)
      except NotImplementedError as original_exception:
        try:
          return math_ops.log1p(-self.cdf(value, **kwargs))
        except NotImplementedError:
          raise original_exception 

def _log_unnormalized_prob(self, x):
    x = self._maybe_assert_valid_sample(x)
    return ((self.concentration1 - 1.) * math_ops.log(x)
            + (self.concentration0 - 1.) * math_ops.log1p(-x)) 

def get_logits_and_probs(logits=None,
                         probs=None,
                         multidimensional=False,
                         validate_args=False,
                         name="get_logits_and_probs"):
  """Converts logit to probabilities (or vice-versa), and returns both.

  Args:
    logits: Floating-point `Tensor` representing log-odds.
    probs: Floating-point `Tensor` representing probabilities.
    multidimensional: Python `bool`, default `False`.
      If `True`, represents whether the last dimension of `logits` or `probs`,
      a `[N1, N2, ...  k]` dimensional tensor, representing the
      logit or probability of `shape[-1]` classes.
    validate_args: Python `bool`, default `False`. When `True`, either assert
      `0 <= probs <= 1` (if not `multidimensional`) or that the last dimension
      of `probs` sums to one.
    name: A name for this operation (optional).

  Returns:
    logits, probs: Tuple of `Tensor`s. If `probs` has an entry that is `0` or
      `1`, then the corresponding entry in the returned logit will be `-Inf` and
      `Inf` respectively.

  Raises:
    ValueError: if neither `probs` nor `logits` were passed in, or both were.
  """
  with ops.name_scope(name, values=[probs, logits]):
    if (probs is None) == (logits is None):
      raise ValueError("Must pass probs or logits, but not both.")

    if probs is None:
      logits = ops.convert_to_tensor(logits, name="logits")
      if multidimensional:
        return logits, nn.softmax(logits, name="probs")
      return logits, math_ops.sigmoid(logits, name="probs")

    probs = ops.convert_to_tensor(probs, name="probs")
    if validate_args:
      with ops.name_scope("validate_probs"):
        one = constant_op.constant(1., probs.dtype)
        dependencies = [check_ops.assert_non_negative(probs)]
        if multidimensional:
          dependencies += [assert_close(math_ops.reduce_sum(probs, -1), one,
                                        message="probs does not sum to 1.")]
        else:
          dependencies += [check_ops.assert_less_equal(
              probs, one, message="probs has components greater than 1.")]
        probs = control_flow_ops.with_dependencies(dependencies, probs)

    with ops.name_scope("logits"):
      if multidimensional:
        # Here we don't compute the multidimensional case, in a manner
        # consistent with respect to the unidimensional case. We do so
        # following the TF convention. Typically, you might expect to see
        # logits = log(probs) - log(probs[pivot]). A side-effect of
        # being consistent with the TF approach is that the unidimensional case
        # implicitly handles the second dimension but the multidimensional case
        # explicitly keeps the pivot dimension.
        return math_ops.log(probs), probs
      return math_ops.log(probs) - math_ops.log1p(-1. * probs), probs 

def log_cdf_laplace(x, name="log_cdf_laplace"):
  """Log Laplace distribution function.

  This function calculates `Log[L(x)]`, where `L(x)` is the cumulative
  distribution function of the Laplace distribution, i.e.

  ```L(x) := 0.5 * int_{-infty}^x e^{-|t|} dt```

  For numerical accuracy, `L(x)` is computed in different ways depending on `x`,

  ```
  x <= 0:
    Log[L(x)] = Log[0.5] + x, which is exact

  0 < x:
    Log[L(x)] = Log[1 - 0.5 * e^{-x}], which is exact
  ```

  Args:
    x: `Tensor` of type `float32`, `float64`.
    name: Python string. A name for the operation (default="log_ndtr").

  Returns:
    `Tensor` with `dtype=x.dtype`.

  Raises:
    TypeError: if `x.dtype` is not handled.
  """

  with ops.name_scope(name, values=[x]):
    x = ops.convert_to_tensor(x, name="x")

    # For x < 0, L(x) = 0.5 * exp{x} exactly, so Log[L(x)] = log(0.5) + x.
    lower_solution = -np.log(2.) + x

    # safe_exp_neg_x = exp{-x} for x > 0, but is
    # bounded above by 1, which avoids
    #   log[1 - 1] = -inf for x = log(1/2), AND
    #   exp{-x} --> inf, for x << -1
    safe_exp_neg_x = math_ops.exp(-math_ops.abs(x))

    # log1p(z) = log(1 + z) approx z for |z| << 1. This approxmation is used
    # internally by log1p, rather than being done explicitly here.
    upper_solution = math_ops.log1p(-0.5 * safe_exp_neg_x)

    return array_ops.where(x < 0., lower_solution, upper_solution) 

def log_cdf_laplace(x, name="log_cdf_laplace"):
  """Log Laplace distribution function.

  This function calculates `Log[L(x)]`, where `L(x)` is the cumulative
  distribution function of the Laplace distribution, i.e.

  ```L(x) := 0.5 * int_{-infty}^x e^{-|t|} dt```

  For numerical accuracy, `L(x)` is computed in different ways depending on `x`,

  ```
  x <= 0:
    Log[L(x)] = Log[0.5] + x, which is exact

  0 < x:
    Log[L(x)] = Log[1 - 0.5 * e^{-x}], which is exact
  ```

  Args:
    x: `Tensor` of type `float32`, `float64`.
    name: Python string. A name for the operation (default="log_ndtr").

  Returns:
    `Tensor` with `dtype=x.dtype`.

  Raises:
    TypeError: if `x.dtype` is not handled.
  """

  with ops.name_scope(name, values=[x]):
    x = ops.convert_to_tensor(x, name="x")

    # For x < 0, L(x) = 0.5 * exp{x} exactly, so Log[L(x)] = log(0.5) + x.
    lower_solution = -np.log(2.) + x

    # safe_exp_neg_x = exp{-x} for x > 0, but is
    # bounded above by 1, which avoids
    #   log[1 - 1] = -inf for x = log(1/2), AND
    #   exp{-x} --> inf, for x << -1
    safe_exp_neg_x = math_ops.exp(-math_ops.abs(x))

    # log1p(z) = log(1 + z) approx z for |z| << 1. This approxmation is used
    # internally by log1p, rather than being done explicitly here.
    upper_solution = math_ops.log1p(-0.5 * safe_exp_neg_x)

    return array_ops.where(x < 0., lower_solution, upper_solution)