tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.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`.

tf.polygamma(a, x, name=None) Compute the polygamma function \(\psi^{(n)}(x)\). The polygamma function is defined as: \psi^{(n)}(x) = \frac{d^n}{dx^n} \psi(x) where \(\psi(x)\) is the digamma function. Args: a: A Tensor. Must be one of the following types: float32, float64. x: A Tensor. Must have the same type as a. name: A name for the operation (optional). Returns: A Tensor. Has the same type as a.

tf.contrib.losses.add_loss(*args, **kwargs) Adds a externally defined loss to the collection of losses. Args: loss: A loss Tensor. loss_collection: Optional collection to add the loss to.

tf.nn.rnn_cell.RNNCell.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 shapes [ba

tf.nn.rnn_cell.OutputProjectionWrapper.__call__(inputs, state, scope=None) Run the cell and output projection on inputs, starting from state.

tf.nn.rnn_cell.LSTMStateTuple.__getstate__() Exclude the OrderedDict from pickling

tf.nn.rnn_cell.InputProjectionWrapper.output_size

tf.nn.rnn_cell.BasicRNNCell.output_size

tf.listdiff(x, y, out_idx=None, name=None) Computes the difference between two lists of numbers or strings. Given a list x and a list y, this operation returns a list out that represents all values that are in x but not in y. The returned list out is sorted in the same order that the numbers appear in x (duplicates are preserved). This operation also returns a list idx that represents the position of each out element in x. In other words: out[i] = x[idx[i]] for i in [0, 1, ..., len(out) - 1] F

class tf.nn.rnn_cell.GRUCell Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078).