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

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.graph

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

tf.SparseTensor.values The non-zero values in the represented dense tensor. Returns: A 1-D Tensor of any data type.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.value_type

tf.contrib.distributions.BernoulliWithSigmoidP.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.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 Student'

Status tensorflow::EnvWrapper::GetSymbolFromLibrary(void *handle, const char *symbol_name, void **symbol) override

tf.fft3d(input, name=None) Compute the 3-dimensional discrete Fourier Transform over the inner-most 3 dimensions of input. Args: input: A Tensor of type complex64. A complex64 tensor. name: A name for the operation (optional). Returns: A Tensor of type complex64. A complex64 tensor of the same shape as input. The inner-most 3 dimensions of input are replaced with their 3D Fourier Transform.