tf.contrib.bayesflow.stochastic_tensor.GammaTensor.input_dict

tf.TensorArray.unpack(value, name=None) Pack the values of a Tensor in the TensorArray. Args: value: (N+1)-D. Tensor of type dtype. The Tensor to unpack. name: A name for the operation (optional). Returns: A new TensorArray object with flow that ensures the unpack occurs. Use this object all for subsequent operations. Raises: ValueError: if the shape inference fails.

tf.contrib.distributions.Poisson.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.

tf.contrib.learn.monitors.StepCounter.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.

tf.contrib.distributions.Beta.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.matmul(a, b, transpose_a=False, transpose_b=False, a_is_sparse=False, b_is_sparse=False, name=None) Multiplies matrix a by matrix b, producing a * b. The inputs must be two-dimensional matrices, with matching inner dimensions, possibly after transposition. Both matrices must be of the same type. The supported types are: float32, float64, int32, complex64. Either matrix can be transposed on the fly by setting the corresponding flag to True. This is False by default. If one or both of the mat

tf.reduce_any(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the "logical or" of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is r

tf.contrib.distributions.Mixture.entropy_lower_bound(name='entropy_lower_bound') A lower bound on the entropy of this mixture model. The bound below is not always very tight, and its usefulness depends on the mixture probabilities and the components in use. A lower bound is useful for ELBO when the Mixture is the variational distribution: \( \log p(x) >= ELBO = \int q(z) \log p(x, z) dz + H[q] \) where \( p \) is the prior disribution, \( q \) is the variational, and \( H[q] \) is the entro

tf.contrib.distributions.Categorical.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.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.clone(name=None, **dist_args)