tf.contrib.graph_editor.make_placeholder_from_tensor(t, scope=None) Create a tf.placeholder for the Graph Editor. Note that the correct graph scope must be set by the calling function. Args: t: a tf.Tensor whose name will be used to create the placeholder (see function placeholder_name). scope: absolute scope within which to create the placeholder. None means that the scope of t is preserved. "" means the root scope. Returns: A newly created tf.placeholder. Raises: TypeError: if t is not

tf.contrib.distributions.Categorical.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: value: float or double

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n') Generate n samples. Additional documentation from Gamma: See the documentation for tf.random_gamma for more details. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.distributions.InverseGamma.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.distributions.WishartFull.entropy(name='entropy') Shanon entropy in nats.

tf.image.resize_images(images, size, method=0, align_corners=False) Resize images to size using the specified method. Resized images will be distorted if their original aspect ratio is not the same as size. To avoid distortions see resize_image_with_crop_or_pad. method can be one of: ResizeMethod.BILINEAR: Bilinear interpolation. ResizeMethod.NEAREST_NEIGHBOR: Nearest neighbor interpolation. ResizeMethod.BICUBIC: Bicubic interpolation. ResizeMethod.AREA: Area interpolation. Args: ima

tf.contrib.distributions.WishartFull.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.Poisson.pdf(value, name='pdf') Probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.Exponential.mode(name='mode') Mode. Additional documentation from Gamma: The mode of a gamma distribution is (alpha - 1) / beta when alpha > 1, and NaN otherwise. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN.

tf.contrib.distributions.LaplaceWithSoftplusScale.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: value: fl