tf.contrib.bayesflow.stochastic_tensor.GammaTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.value_type

tf.nn.rnn_cell.LSTMCell.output_size

tf.contrib.learn.monitors.NanLoss.epoch_end(epoch) End epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've not begun an epoch, or epoch number does not match.

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

tf.contrib.distributions.Chi2.validate_args Python boolean indicated possibly expensive checks are enabled.

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

tf.contrib.distributions.Distribution.dtype The DType of Tensors handled by this Distribution.

tf.contrib.distributions.StudentT.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 Te

tf.contrib.distributions.QuantizedDistribution.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.