tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for S

tf.FixedLenFeature.dtype Alias for field number 1

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

static Status tensorflow::PartialTensorShape::MakePartialShape(const int32 *dims, int n, PartialTensorShape *out) Returns a PartialTensorShape whose dimensions are dims[0], dims[1], ..., dims[n-1]. Values of -1 are considered "unknown".

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

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

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

tf.contrib.distributions.NormalWithSoftplusSigma.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.nn.rnn_cell.BasicRNNCell.__call__(inputs, state, scope=None) Most basic RNN: output = new_state = activation(W * input + U * state + B).

static bool tensorflow::TensorShapeUtils::IsScalar(const TensorShape &shape)