tf.contrib.learn.monitors.GraphDump.__init__(ignore_ops=None) Initializes GraphDump monitor. Args: ignore_ops: list of string. Names of ops to ignore. If None, GraphDump.IGNORE_OPS is used.

tf.nn.rnn_cell.RNNCell.state_size size(s) of state(s) used by this cell. It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes.

tf.contrib.learn.monitors.SummarySaver.set_estimator(estimator)

tf.contrib.learn.monitors.RunHookAdapterForMonitors.begin()

Status tensorflow::Env::RenameFile(const string &src, const string &target) Renames file src to target. If target already exists, it will be replaced.

tf.contrib.metrics.set_union(a, b, validate_indices=True) Compute set union of elements in last dimension of a and b. All but the last dimension of a and b must match. Args: a: Tensor or SparseTensor of the same type as b. If sparse, indices must be sorted in row-major order. b: Tensor or SparseTensor of the same type as a. Must be SparseTensor if a is SparseTensor. If sparse, indices must be sorted in row-major order. validate_indices: Whether to validate the order and range of sparse indi

tf.contrib.distributions.Laplace.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.Laplace.loc Distribution parameter for the location.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.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: val

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.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`.