tf.contrib.distributions.Poisson.mean(name='mean') Mean.

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.loss(final_loss, name='Loss')

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.nn.rnn_cell.MultiRNNCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shape

tf.contrib.metrics.streaming_auc(predictions, labels, weights=None, num_thresholds=200, metrics_collections=None, updates_collections=None, curve='ROC', name=None) Computes the approximate AUC via a Riemann sum. The streaming_auc function creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the AUC. To discretize the AUC curve, a linearly spaced set of thresholds is used to compute pairs of recall and precision values. The a

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.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.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

Manages the partially known dimensions of a Tensor and their sizes. Member Details tensorflow::PartialTensorShape::PartialTensorShape() Construct an unknown PartialTensorShape. tensorflow::PartialTensorShape::PartialTensorShape(gtl::ArraySlice< int64 > dim_sizes) Construct a PartialTensorShape from the provided sizes. REQUIRES: dim_sizes[i] >= 0 tensorflow::PartialTensorShape::PartialTensorShape(std::initializer_list< int64 > dim_sizes) tensorflow::PartialTensorShape::PartialTens

tf.atan(x, name=None) Computes atan of x element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.