tf.contrib.model_pruning.train

Wrapper around tf-slim's train function.

tf.contrib.model_pruning.train(
    train_op, logdir, mask_update_op, train_step_fn=train_step,
    train_step_kwargs=_USE_DEFAULT, log_every_n_steps=1, graph=None, master='',
    is_chief=True, global_step=None, number_of_steps=None, init_op=_USE_DEFAULT,
    init_feed_dict=None, local_init_op=_USE_DEFAULT, init_fn=None,
    ready_op=_USE_DEFAULT, summary_op=_USE_DEFAULT, save_summaries_secs=600,
    summary_writer=_USE_DEFAULT, startup_delay_steps=0, saver=None,
    save_interval_secs=600, sync_optimizer=None, session_config=None,
    trace_every_n_steps=None
)

Runs a training loop using a TensorFlow supervisor. When the sync_optimizer is supplied, gradient updates are applied synchronously. Otherwise, gradient updates are applied asynchronous.

Args
`train_op`	A `Tensor` that, when executed, will apply the gradients and return the loss value.
`logdir`	The directory where training logs are written to. If None, model checkpoints and summaries will not be written.
`mask_update_op`	Operation that upon execution updates the weight masks and thresholds.
`train_step_fn`	The function to call in order to execute a single gradient step. The function must have take exactly four arguments: the current session, the `train_op` `Tensor`, a global step `Tensor` and a dictionary.
`train_step_kwargs`	A dictionary which is passed to the `train_step_fn`. By default, two `Boolean`, scalar ops called "should_stop" and "should_log" are provided.
`log_every_n_steps`	The frequency, in terms of global steps, that the loss and global step and logged.
`graph`	The graph to pass to the supervisor. If no graph is supplied the default graph is used.
`master`	The address of the tensorflow master.
`is_chief`	Specifies whether or not the training is being run by the primary replica during replica training.
`global_step`	The `Tensor` representing the global step. If left as `None`, then slim.variables.get_or_create_global_step() is used.
`number_of_steps`	The max number of gradient steps to take during training, as measured by 'global_step': training will stop if global_step is greater than 'number_of_steps'. If the value is left as None, training proceeds indefinitely.
`init_op`	The initialization operation. If left to its default value, then the session is initialized by calling `tf.compat.v1.global_variables_initializer()`.
`init_feed_dict`	A feed dictionary to use when executing the `init_op`.
`local_init_op`	The local initialization operation. If left to its default value, then the session is initialized by calling `tf.compat.v1.local_variables_initializer()` and `tf.compat.v1.tables_initializer()`.
`init_fn`	An optional callable to be executed after `init_op` is called. The callable must accept one argument, the session being initialized.
`ready_op`	Operation to check if the model is ready to use. If left to its default value, then the session checks for readiness by calling `tf.compat.v1.report_uninitialized_variables()`.
`summary_op`	The summary operation.
`save_summaries_secs`	How often, in seconds, to save summaries.
`summary_writer`	`SummaryWriter` to use. Can be `None` to indicate that no summaries should be written. If unset, we create a SummaryWriter.
`startup_delay_steps`	The number of steps to wait for before beginning. Note that this must be 0 if a sync_optimizer is supplied.
`saver`	Saver to save checkpoints. If None, a default one will be created and used.
`save_interval_secs`	How often, in seconds, to save the model to `logdir`.
`sync_optimizer`	an instance of tf.compat.v1.train.SyncReplicasOptimizer, or a list of them. If the argument is supplied, gradient updates will be synchronous. If left as `None`, gradient updates will be asynchronous.
`session_config`	An instance of `tf.compat.v1.ConfigProto` that will be used to configure the `Session`. If left as `None`, the default will be used.
`trace_every_n_steps`	produce and save a `Timeline` in Chrome trace format and add it to the summaries every `trace_every_n_steps`. If None, no trace information will be produced or saved.

Returns
the value of the loss function after training.

Raises
`ValueError`	if `train_op` is empty or if `startup_delay_steps` is non-zero when `sync_optimizer` is supplied, if `number_of_steps` is negative, or if `trace_every_n_steps` is not `None` and no `logdir` is provided.

tf.contrib.model_pruning.train

Args

Returns

Raises