tf.contrib.learn.LinearRegressor.config

static bool tensorflow::TensorShapeUtils::IsScalar(const TensorShape &shape)

tf.contrib.distributions.ExponentialWithSoftplusLam.beta Inverse scale parameter.

virtual Thread* tensorflow::Env::StartThread(const ThreadOptions &thread_options, const string &name, std::function< void()> fn) TF_MUST_USE_RESULT=0 Returns a new thread that is running fn() and is identified (for debugging/performance-analysis) by "name". Caller takes ownership of the result and must delete it eventually (the deletion will block until fn() stops running).

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.value_type

tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.graph_editor.sgv_scope(scope, graph) Make a subgraph from a name scope. Args: scope: the name of the scope. graph: the tf.Graph. Returns: A subgraph view representing the given scope.

tf.contrib.graph_editor.reroute_b2a_outputs(sgv0, sgv1) Re-route all the outputs of sgv1 to sgv0 (see _reroute_outputs).

tf.sparse_add(a, b, thresh=0) Adds two tensors, at least one of each is a SparseTensor. If one SparseTensor and one Tensor are passed in, returns a Tensor. If both arguments are SparseTensors, this returns a SparseTensor. The order of arguments does not matter. Use vanilla tf.add() for adding two dense Tensors. The indices of any input SparseTensor are assumed ordered in standard lexicographic order. If this is not the case, before this step run SparseReorder to restore index ordering. If both

tf.errors.InvalidArgumentError.__init__(node_def, op, message) Creates an InvalidArgumentError.