tf.contrib.learn.TensorFlowRNNClassifier.save(path) Saves checkpoints and graph to given path. Args: path: Folder to save model to.

class tf.contrib.learn.monitors.BaseMonitor Base class for Monitors. Defines basic interfaces of Monitors. Monitors can either be run on all workers or, more commonly, restricted to run exclusively on the elected chief worker.

tf.TensorArray.write(index, value, name=None) Write value into index index of the TensorArray. Args: index: 0-D. int32 scalar with the index to write to. value: N-D. Tensor of type dtype. The Tensor to write to this index. name: A name for the operation (optional). Returns: A new TensorArray object with flow that ensures the write occurs. Use this object all for subsequent operations. Raises: ValueError: if there are more writers than specified.

void tensorflow::EnvWrapper::SchedClosure(std::function< void()> closure) override

tf.contrib.learn.monitors.ValidationMonitor.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.

Member Details tensorflow::error::Code tensorflow::Status::State::code string tensorflow::Status::State::msg

Status tensorflow::Env::GetChildren(const string &dir, std::vector< string > *result) Stores in *result the names of the children of the specified directory. The names are relative to "dir". Original contents of *results are dropped.

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)