string tensorflow::Status::ToString() const Return a string representation of this status suitable for printing. Returns the string "OK" for success.

tf.contrib.distributions.Normal.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.WishartFull.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.SparseTensorValue.__getnewargs__() Return self as a plain tuple. Used by copy and pickle.

An interface used by the tensorflow implementation to access operating system functionality like the filesystem etc. Callers may wish to provide a custom Env object to get fine grain control. All Env implementations are safe for concurrent access from multiple threads without any external synchronization. Member Details tensorflow::Env::Env() virtual tensorflow::Env::~Env()=default Status tensorflow::Env::GetFileSystemForFile(const string &fname, FileSystem **result) Returns the FileSystem

tf.contrib.distributions.ExponentialWithSoftplusLam.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.distributions.Binomial.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.learn.monitors.ExportMonitor.every_n_step_begin(step) Callback before every n'th step begins. Args: step: int, the current value of the global step. Returns: A list of tensors that will be evaluated at this step.

tf.contrib.distributions.InverseGamma.beta Scale parameter.

TTypes<T>::UnalignedFlat tensorflow::Tensor::unaligned_flat()