tf.contrib.distributions.LaplaceWithSoftplusScale.get_batch_shape()

tf.contrib.distributions.LaplaceWithSoftplusScale.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.DirichletMultinomial.alpha_sum

tf.contrib.distributions.DirichletMultinomial.alpha_sum Summation of alpha parameter.

tf.contrib.distributions.WishartCholesky.entropy()

tf.contrib.distributions.WishartCholesky.entropy(name='entropy') Shanon entropy in nats.

tf.contrib.distributions.Normal.parameters

tf.contrib.distributions.Normal.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.LaplaceWithSoftplusScale.survival_function()

tf.contrib.distributions.LaplaceWithSoftplusScale.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.ExponentialTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.value_type

tf.contrib.distributions.Uniform.log_survival_function()

tf.contrib.distributions.Uniform.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 Ten

tensorflow::Tensor::bit_casted_tensor()

TTypes< T, NDIMS >::Tensor tensorflow::Tensor::bit_casted_tensor() Return the tensor data to an Eigen::Tensor with the same size but a bitwise cast to the specified dtype T. Using a bitcast is useful for move and copy operations. NOTE: this is the same as tensor() except a bitcast is allowed.

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.mean()

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

tensorflow::Env::NewWritableFile()

Status tensorflow::Env::NewWritableFile(const string &fname, std::unique_ptr< WritableFile > *result) Creates an object that writes to a new file with the specified name. Deletes any existing file with the same name and creates a new file. On success, stores a pointer to the new file in *result and returns OK. On failure stores NULL in *result and returns non-OK. The returned file will only be accessed by one thread at a time. The ownership of the returned WritableFile is passed to t