void tensorflow::TensorShape::RemoveDim(int d) Removes dimension d from the TensorShape. REQUIRES: 0 <= d < dims()

tf.neg(x, name=None) Computes numerical negative value element-wise. I.e., (y = -x). Args: x: A Tensor or SparseTensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor or SparseTensor, respectively. Has the same type as x.

tf.contrib.metrics.streaming_sparse_precision_at_k(*args, **kwargs) Computes precision@k of the predictions with respect to sparse labels. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask). If class_id is specified, we calculate precision by considering only the entries in the batch for which

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.name

StringPiece tensorflow::Tensor::tensor_data() const Returns a StringPiece mapping the current tensor's buffer. The returned StringPiece may point to memory location on devices that the CPU cannot address directly. NOTE: The underlying tensor buffer is refcounted, so the lifetime of the contents mapped by the StringPiece matches the lifetime of the buffer; callers should arrange to make sure the buffer does not get destroyed while the StringPiece is still used. REQUIRES: DataTypeCanUseMemcpy(dt

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.name

bool tensorflow::Env::FileExists(const string &fname) Returns true iff the named file exists.

class tf.contrib.rnn.GRUBlockCell Block GRU cell implementation. The implementation is based on: http://arxiv.org/abs/1406.1078 Computes the LSTM cell forward propagation for 1 time step. This kernel op implements the following mathematical equations: Baises are initialized with : b_ru - constant_initializer(1.0) b_c - constant_initializer(0.0) ``` x_h_prev = [x, h_prev] [r_bar u_bar] = x_h_prev * w_ru + b_ru r = sigmoid(r_bar) u = sigmoid(u_bar) h_prevr = h_prev \circ r x_h_prevr = [x h_prevr

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.name

tf.contrib.learn.monitors.PrintTensor.post_step(step, session)