tf.contrib.learn.train( graph, output_dir, train_op, loss_op, global_step_tensor=None, init_op=None, init_feed_dict=None, init_fn=None, log_every_steps=10, supervisor_is_chief=True, supervisor_master='', supervisor_save_model_secs=600, keep_checkpoint_max=5, supervisor_save_summaries_steps=100, feed_fn=None, steps=None, fail_on_nan_loss=True, monitors=None, max_steps=None )
Train a model. (deprecated)
graph, a directory to write outputs to (
output_dir), and some ops,
run a training loop. The given
train_op performs one step of training on the
loss_op represents the objective function of the training. It is
expected to increment the
global_step_tensor, a scalar integer tensor
counting training steps. This function uses
Supervisor to initialize the
graph (from a checkpoint if one is available in
output_dir), write summaries
defined in the graph, and write regular checkpoints as defined by
Training continues until
global_step_tensor evaluates to
max_steps, or, if
loss_op evaluates to
NaN. In that case the
program is terminated with exit code 1.
graph: A graph to train. It is expected that this graph is not in use elsewhere.
output_dir: A directory to write outputs to.
train_op: An op that performs one training step when run.
loss_op: A scalar loss tensor.
global_step_tensor: A tensor representing the global step. If none is given, one is extracted from the graph using the same logic as in
init_op: An op that initializes the graph. If
init_feed_dict: A dictionary that maps
Tensorobjects to feed values. This feed dictionary will be used when
init_fn: Optional callable passed to Supervisor to initialize the model.
log_every_steps: Output logs regularly. The logs contain timing data and the current loss.
supervisor_is_chief: Whether the current process is the chief supervisor in charge of restoring the model and running standard services.
supervisor_master: The master string to use when preparing the session.
supervisor_save_model_secs: Save a checkpoint every
supervisor_save_model_secsseconds when training.
keep_checkpoint_max: The maximum number of recent checkpoint files to keep. As new files are created, older files are deleted. If None or 0, all checkpoint files are kept. This is simply passed as the max_to_keep arg to tf.train.Saver constructor.
supervisor_save_summaries_steps: Save summaries every
supervisor_save_summaries_stepsseconds when training.
feed_fn: A function that is called every iteration to produce a
steps: Trains for this many steps (e.g. current global step +
fail_on_nan_loss: If true, raise
NaN. If false, continue training as if nothing happened.
monitors: List of
BaseMonitorsubclass instances. Used for callbacks inside the training loop.
max_steps: Number of total steps for which to train model. If
None, train forever. Two calls fit(steps=100) means 200 training iterations. On the other hand two calls of fit(max_steps=100) means, second call will not do any iteration since first call did all 100 steps.
The final loss value.
global_step_tensoris not provided. See
tf.contrib.framework.get_global_stepfor how we look up the latter if not provided explicitly.
True, and loss ever evaluates to
ValueError: If both