void tensorflow::TensorShape::operator=(const TensorShape &b)

tf.contrib.distributions.Uniform.pdf(value, name='pdf') Probability density function. 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. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.Uniform.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 sample_shape(x) + self.

tf.contrib.learn.TensorFlowRNNClassifier.get_variable_names() Returns list of all variable names in this model. Returns: List of names.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.variance(name='variance') Variance. Additional documentation from StudentT: The variance for Student's T equals df / (df - 2), when df > 2 infinity, when 1 < df <= 2 NaN, when df <= 1

tf.contrib.distributions.Dirichlet.is_reparameterized

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

tf.einsum(axes, *inputs) A generalized contraction between tensors of arbitrary dimension. Like numpy.einsum.

tf.contrib.distributions.Exponential.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.bayesflow.stochastic_tensor.LaplaceTensor.dtype