tf.digamma(x, name=None) Computes Psi, the derivative of Lgamma (the log of the absolute value of Gamma(x)), element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.nn.rnn_cell.LSTMStateTuple.__getnewargs__() Return self as a plain tuple. Used by copy and pickle.

tf.div(x, y, name=None) Returns x / y element-wise. NOTE: Div 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.contrib.learn.monitors.ValidationMonitor.every_n_post_step(step, session) Callback after a step is finished or end() is called. Args: step: int, the current value of the global step. session: Session object.

tf.contrib.learn.monitors.LoggingTrainable.__init__(scope=None, every_n=100, first_n=1) Initializes LoggingTrainable monitor. Args: scope: An optional string to match variable names using re.match. every_n: Print every N steps. first_n: Print first N steps.

class tf.contrib.learn.monitors.SummarySaver Saves summaries every N steps.

tf.round(x, name=None) Rounds the values of a tensor to the nearest integer, element-wise. For example: # 'a' is [0.9, 2.5, 2.3, -4.4] tf.round(a) ==> [ 1.0, 3.0, 2.0, -4.0 ] Args: x: A Tensor of type float32 or float64. name: A name for the operation (optional). Returns: A Tensor of same shape and type as x.

tf.contrib.distributions.DirichletMultinomial.parameters Dictionary of parameters used by this Distribution.

tf.contrib.learn.monitors.LoggingTrainable.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.

tf.contrib.distributions.MultivariateNormalFull.mode(name='mode') Mode.