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

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.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.bayesflow.stochastic_tensor.BinomialTensor.value_type

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.distributions.ExponentialWithSoftplusLam.beta Inverse scale parameter.

Contents Neural Network RNN CellsBase interface for all RNN Cellsclass tf.nn.rnn_cell.RNNCell RNN Cells for use with TensorFlow's core RNN methodsclass tf.nn.rnn_cell.BasicRNNCell class tf.nn.rnn_cell.BasicLSTMCell class tf.nn.rnn_cell.GRUCell class tf.nn.rnn_cell.LSTMCell Classes storing split RNNCell stateclass tf.nn.rnn_cell.LSTMStateTuple RNN Cell wrappers (RNNCells that wrap other RNNCells)class tf.nn.rnn_cell.MultiRNNCell class tf.nn.rnn_cell.DropoutWrapper class tf.nn.rnn_cell.Embedd

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for S

tf.FixedLenFeature.dtype Alias for field number 1