tf.nn.rnn_cell.BasicLSTMCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shap

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.mul(x, y, name=None) Returns x * y element-wise. NOTE: Mul supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.multinomial(logits, num_samples, seed=None, name=None) Draws samples from a multinomial distribution. Example: # samples has shape [1, 5], where each value is either 0 or 1 with equal # probability. samples = tf.multinomial(tf.log([[10., 10.]]), 5) Args: logits: 2-D Tensor with shape [batch_size, num_classes]. Each slice [i, :] represents the unnormalized log probabilities for all classes. num_samples: 0-D. Number of independent samples to draw for each row slice. seed: A Python integer

tf.mod(x, y, name=None) Returns element-wise remainder of division. NOTE: Mod supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: int32, int64, float32, float64. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.minimum(x, y, name=None) Returns the min of x and y (i.e. x < y ? x : y) element-wise. NOTE: Minimum supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.matrix_triangular_solve(matrix, rhs, lower=None, adjoint=None, name=None) Solves systems of linear equations with upper or lower triangular matrices by backsubstitution. matrix is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. If lower is True then the strictly upper triangular part of each inner-most matrix is assumed to be zero and not accessed. If lower is False then the strictly lower triangular part of each inner-most matrix is assumed to be zero and

tf.maximum(x, y, name=None) Returns the max of x and y (i.e. x > y ? x : y) element-wise. NOTE: Maximum supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.matrix_transpose(a, name='matrix_transpose') Transposes last two dimensions of tensor a. For example: # Matrix with no batch dimension. # 'x' is [[1 2 3] # [4 5 6]] tf.matrix_transpose(x) ==> [[1 4] [2 5] [3 6]] # Matrix with two batch dimensions. # x.shape is [1, 2, 3, 4] # tf.matrix_transpose(x) is shape [1, 2, 4, 3] Args: a: A Tensor with rank >= 2. name: A name for the operation (optional). Returns: A

tf.matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None) Solves one or more linear least-squares problems. matrix is a tensor of shape [..., M, N] whose inner-most 2 dimensions form M-by-N matrices. Rhs is a tensor of shape [..., M, K] whose inner-most 2 dimensions form M-by-K matrices. The computed output is a Tensor of shape [..., N, K] whose inner-most 2 dimensions form M-by-K matrices that solve the equations matrix[..., :, :] * output[..., :, :] = rhs[..., :, :] in the le