tf.contrib.learn.monitors.PrintTensor.begin(max_steps=None) Called at the beginning of training. When called, the default graph is the one we are executing. Args: max_steps: int, the maximum global step this training will run until. Raises: ValueError: if we've already begun a run.

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.value_type

tf.contrib.distributions.WishartCholesky.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.Chi2WithAbsDf.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

TTypes<T>::ConstMatrix tensorflow::Tensor::matrix() const

Status tensorflow::Env::IsDirectory(const string &fname) Returns whether the given path is a directory or not. Typical return codes (not guaranteed exhaustive): OK - The path exists and is a directory. FAILED_PRECONDITION - The path exists and is not a directory. NOT_FOUND - The path entry does not exist. PERMISSION_DENIED - Insufficient permissions. UNIMPLEMENTED - The file factory doesn't support directories.

tf.contrib.distributions.LaplaceWithSoftplusScale.mean(name='mean') Mean.

tf.contrib.learn.monitors.EveryN.every_n_step_begin(step) Callback before every n'th step begins. Args: step: int, the current value of the global step. Returns: A list of tensors that will be evaluated at this step.

tf.IdentityReader.__init__(name=None) Create a IdentityReader. Args: name: A name for the operation (optional).

tf.IdentityReader.read(queue, name=None) Returns the next record (key, value pair) produced by a reader. Will dequeue a work unit from queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file). Args: queue: A Queue or a mutable string Tensor representing a handle to a Queue, with string work items. name: A name for the operation (optional). Returns: A tuple of Tensors (key, value). key: A string scalar Tensor. value: A s