tf.contrib.bayesflow.stochastic_tensor.GammaTensor.value(name='value')

tf.QueueBase.names The list of names for each component of a queue element.

class tf.contrib.rnn.LSTMBlockCell Basic LSTM recurrent network cell. The implementation is based on: http://arxiv.org/abs/1409.2329. We add forget_bias (default: 1) to the biases of the forget gate in order to reduce the scale of forgetting in the beginning of the training. Unlike BasicLSTMCell, this is a monolithic op and should be much faster. The weight and bias matrixes should be compatible as long as the variabel scope matches.

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.value_type

tf.contrib.distributions.Mixture.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.Mixture.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.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.distributions.Chi2.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 Tensor