tf.contrib.distributions.Mixture.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.learn.RunConfig.__init__(master=None, task=None, num_ps_replicas=None, num_cores=0, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=600, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, job_name=None, is_chief=None, evaluation_master='') Constructor. If set to None, master, task, num_ps_replicas, cluster_spec, job_name, and is_chief are set based on the TF_CONFIG environment variable,

tf.contrib.learn.monitors.ValidationMonitor.post_step(step, session)

tf.TextLineReader.serialize_state(name=None) Produce a string tensor that encodes the state of a reader. Not all Readers support being serialized, so this can produce an Unimplemented error. Args: name: A name for the operation (optional). Returns: A string Tensor.

tf.contrib.learn.TensorFlowEstimator.config

tf.contrib.learn.monitors.EveryN.step_begin(step) Overrides BaseMonitor.step_begin. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. Returns: A list, the result of every_n_step_begin, if that was called this step, or an empty list otherwise. Raises: ValueError: if called more than once during a step.

tf.contrib.learn.monitors.CheckpointSaver.epoch_end(epoch) End epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've not begun an epoch, or epoch number does not match.

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.value(name='value')

tf.inv(x, name=None) Computes the reciprocal of x element-wise. I.e., \(y = 1 / x\). Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.learn.monitors.CaptureVariable.values Returns the values captured so far. Returns: dict mapping int step numbers to that values of the variable at the respective step.