tf.contrib.distributions.InverseGamma.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from _MultivariateNormalOperatorPD: x is a batch vector with compatible shape if x is a Tensor whose shape can be broadcast up to either: self.batch_shape + self.event_shape or [M1,...,Mm] + self.batch_shape + self.event_shape Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor o

tf.image.rgb_to_grayscale(images, name=None) Converts one or more images from RGB to Grayscale. Outputs a tensor of the same DType and rank as images. The size of the last dimension of the output is 1, containing the Grayscale value of the pixels. Args: images: The RGB tensor to convert. Last dimension must have size 3 and should contain RGB values. name: A name for the operation (optional). Returns: The converted grayscale image(s).

tf.contrib.distributions.Uniform.prob(value, name='prob') Probability density/mass function (depending on is_continuous). 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.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.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:

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.distribution

class tf.contrib.bayesflow.stochastic_tensor.UniformTensor UniformTensor is a StochasticTensor backed by the distribution Uniform.

tf.image.random_flip_up_down(image, seed=None) Randomly flips an image vertically (upside down). With a 1 in 2 chance, outputs the contents of image flipped along the first dimension, which is height. Otherwise output the image as-is. Args: image: A 3-D tensor of shape [height, width, channels]. seed: A Python integer. Used to create a random seed. See set_random_seed for behavior. Returns: A 3-D tensor of the same type and shape as image. Raises: ValueError: if the shape of image not su

tf.contrib.learn.LinearClassifier.get_variable_names()

tf.nn.rnn_cell.BasicLSTMCell.__init__(num_units, forget_bias=1.0, input_size=None, state_is_tuple=True, activation=tanh) Initialize the basic LSTM cell. Args: num_units: int, The number of units in the LSTM cell. forget_bias: float, The bias added to forget gates (see above). input_size: Deprecated and unused. state_is_tuple: If True, accepted and returned states are 2-tuples of the c_state and m_state. If False, they are concatenated along the column axis. The latter behavior will soon be