tf.contrib.distributions.Normal.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.framework.get_unique_variable(var_op_name) Gets the variable uniquely identified by that var_op_name. Args: var_op_name: the full name of the variable op, including the scope. Returns: a tensorflow variable. Raises: ValueError: if no variable uniquely identified by the name exists.

tf.contrib.distributions.Beta.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from Beta: Note that the argument x must be a non-negative floating point tensor whose shape can be broadcast with self.a and self.b. For fixed leading dimensions, the last dimension represents counts for the corresponding Beta distribution in self.a and self.b. x is only legal if 0 < x < 1. Args: value: float or double Tensor. name: The name t

tf.contrib.graph_editor.graph_replace(target_ts, replacement_ts, dst_scope='', src_scope='', reuse_dst_scope=False) Create a new graph which compute the targets from the replaced Tensors. Args: target_ts: a single tf.Tensor or an iterabble of tf.Tensor. replacement_ts: dictionary mapping from original tensors to replaced tensors dst_scope: the destination scope. src_scope: the source scope. reuse_dst_scope: if True the dst_scope is re-used if it already exists. Otherwise, the scope is giv

tf.contrib.distributions.DirichletMultinomial.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.ExponentialWithSoftplusLam.is_continuous

tf.contrib.distributions.Uniform.dtype The DType of Tensors handled by this Distribution.

tf.image.random_saturation(image, lower, upper, seed=None) Adjust the saturation of an RGB image by a random factor. Equivalent to adjust_saturation() but uses a saturation_factor randomly picked in the interval [lower, upper]. Args: image: RGB image or images. Size of the last dimension must be 3. lower: float. Lower bound for the random saturation factor. upper: float. Upper bound for the random saturation factor. seed: An operation-specific seed. It will be used in conjunction with the

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: