tf.contrib.learn.monitors.CheckpointSaver.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.bayesflow.stochastic_tensor.StudentTTensor.name

tf.contrib.graph_editor.copy_op_handler(info, op, copy_shape=True) Copy a tf.Operation. Args: info: Transform._Info instance. op: the tf.Operation to be copied. copy_shape: also copy the shape of the tensor Returns: A copy of op.

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

tf.Session.graph The graph that was launched in this session.

class tf.FixedLengthRecordReader A Reader that outputs fixed-length records from a file. See ReaderBase for supported methods.

TTypes<T>::Vec tensorflow::Tensor::vec() Return the tensor data as an Eigen::Tensor with the type and sizes of this Tensor. Use these methods when you know the data type and the number of dimensions of the Tensor and you want an Eigen::Tensor automatically sized to the Tensor sizes. The implementation check fails if either type or sizes mismatch. Example: Tensor my_mat(...built with Shape{rows: 3, cols: 5}...); auto mat = my_mat.matrix<T>(); // 2D Eigen::Tensor, 3 x 5. auto mat

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.entropy(name='entropy') Shanon entropy in nats. Additional documentation from Gamma: This is defined to be entropy = alpha - log(beta) + log(Gamma(alpha)) + (1-alpha)digamma(alpha) where digamma(alpha) is the digamma function.

tensorflow::Session::Session()

static constexpr int tensorflow::TensorShape::MaxDimensions()