tf.contrib.metrics.streaming_accuracy(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None) Calculates how often predictions matches labels. The streaming_accuracy function creates two local variables, total and count that are used to compute the frequency with which predictions matches labels. This frequency is ultimately returned as accuracy: an idempotent operation that simply divides total by count. For estimation of the metric over a stream of d

tf.contrib.distributions.normal_conjugates_known_sigma_posterior(prior, sigma, s, n) Posterior Normal distribution with conjugate prior on the mean. This model assumes that n observations (with sum s) come from a Normal with unknown mean mu (described by the Normal prior) and known variance sigma^2. The "known sigma posterior" is the distribution of the unknown mu. Accepts a prior Normal distribution object, having parameters mu0 and sigma0, as well as known sigma values of the predictive dist

tf.contrib.distributions.MultivariateNormalCholesky.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.Beta.__init__(a, b, validate_args=False, allow_nan_stats=True, name='Beta') Initialize a batch of Beta distributions. Args: a: Positive floating point tensor with shape broadcastable to [N1,..., Nm] m >= 0. Defines this as a batch of N1 x ... x Nm different Beta distributions. This also defines the dtype of the distribution. b: Positive floating point tensor with shape broadcastable to [N1,..., Nm] m >= 0. Defines this as a batch of N1 x ... x Nm different Beta

tf.contrib.distributions.WishartCholesky.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.Chi2.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.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.clone(name=None, **dist_args)

tf.contrib.learn.LinearRegressor.__repr__()

tf.mul(x, y, name=None) Returns x * y element-wise. NOTE: Mul supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

TTypes< T, NDIMS >::ConstTensor tensorflow::Tensor::flat_inner_dims() const