tf.TFRecordReader.restore_state(state, name=None) Restore a reader to a previously saved state. Not all Readers support being restored, so this can produce an Unimplemented error. Args: state: A string Tensor. Result of a SerializeState of a Reader with matching type. name: A name for the operation (optional). Returns: The created Operation.

class tf.errors.UnauthenticatedError The request does not have valid authentication credentials. This exception is not currently used.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

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