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N/A __future__ division print_function absolute_import N/A numpy np N/A tensorflow tf tensorflow.python.training optimizer tf_optimizer N/A tflearn N/A callbacks config init_training_mode utils to_list id_generator check_dir_name standarize_dict get_dict_first_element make_batches slice_array check_scope_path summarizer summaries summarize summarize_gradients summarize_variables summarize_activations Trainer N/A object N/A N/A  Trainer.

    Generic class to handle any TensorFlow graph training. It requires
    the use of `TrainOp` to specify all optimization parameters.

    Arguments:
        train_ops: list of `TrainOp`. A list of a network training
            operations for performing optimizations.
        graph: `tf.Graph`. The TensorFlow graph to use. Default: default tf
            graph.
        clip_gradients: `float`. Clip gradient. Default: 5.0.
        tensorboard_dir: `str`. Tensorboard log directory.
            Default: "/tmp/tflearn_logs/".
        tensorboard_verbose: `int`. Verbose level. It supports:
            ```python
            0 - Loss, Accuracy. (Best Speed)
            1 - Loss, Accuracy, Gradients.
            2 - Loss, Accuracy, Gradients, Weights.
            3 - Loss, Accuracy, Gradients, Weights, Activations, Sparsity.
                (Best Visualization)
            ```
        checkpoint_path: `str`. Path to store model checkpoints. If None,
            no model checkpoint will be saved. Default: None.
        max_checkpoints: `int` or None. Maximum amount of checkpoints. If
            None, no limit. Default: None.
        keep_checkpoint_every_n_hours: `float`. Number of hours between each
            model checkpoints.
        random_seed: `int`. Random seed, for test reproductivity.
            Default: None.
        session: `Session`. A session for running ops. If None, a new one will
            be created. Note: When providing a session, variables must have been
            initialized already, otherwise an error will be raised.

     __init__ N/A N/A self train_ops graph clip_gradients tensorboard_dir tensorboard_verbose checkpoint_path max_checkpoints keep_checkpoint_every_n_hours random_seed session N/A None 5.0 /tmp/tflearn_logs/ 0 None None 10000.0 None None N/A N/A N/A self graph N/A N/A tf get_default_graph N/A graph N/A N/A N/A self graph graph N/A N/A N/A N/A self graph as_default N/A N/A N/A init_training_mode N/A train_ops N/A to_list train_ops N/A N/A duplicate_identical_ops train_ops N/A random_seed N/A N/A N/A N/A tf set_random_seed random_seed N/A N/A self restored False N/A N/A self tensorboard_dir N/A check_dir_name tensorboard_dir N/A N/A self training_step 0 N/A N/A self train_ops N/A to_list train_ops N/A N/A N/A self validate_trainop_names N/A N/A self global_loss None N/A N/A self global_step N/A N/A tf Variable 0.0 name Global_Step trainable False N/A N/A self incr_global_step N/A N/A tf assign N/A self global_step N/A N/A tf add N/A self global_step 1 N/A config None N/A tflearn_conf N/A N/A tf get_collection N/A N/A tf GraphKeys GRAPH_CONFIG N/A tflearn_conf N/A N/A config N/A tflearn_conf N/A 0 N/A N/A session N/A N/A N/A self session N/A N/A tf Session config config N/A N/A N/A self session session N/A N/A self restored True N/A N/A i train_op N/A enumerate N/A self train_ops N/A N/A N/A N/A len train_ops 1 N/A N/A N/A train_op scope_name N/A N/A N/A train_op initialize_training_ops i N/A self session tensorboard_verbose clip_gradients N/A N/A self saver N/A N/A N/A tf train Saver max_to_keep max_checkpoints keep_checkpoint_every_n_hours keep_checkpoint_every_n_hours N/A all_vars N/A N/A tf get_collection N/A N/A tf GraphKeys VARIABLES N/A excl_vars N/A N/A tf get_collection N/A N/A tf GraphKeys EXCL_RESTORE_VARS N/A to_restore N/A item N/A item all_vars N/A item excl_vars N/A N/A self restorer N/A N/A N/A tf train Saver var_list to_restore max_to_keep max_checkpoints keep_checkpoint_every_n_hours keep_checkpoint_every_n_hours N/A N/A self checkpoint_path checkpoint_path N/A N/A N/A self restored N/A N/A init N/A N/A tf initialize_all_variables N/A N/A N/A N/A self session run init N/A fit N/A N/A self feed_dicts n_epoch val_feed_dicts show_metric snapshot_step snapshot_epoch shuffle_all run_id N/A 10 None False None True None None N/A N/A  fit.

        Train network with feeded data dicts.

        Examples:
            ```python
            # 1 Optimizer
            trainer.fit(feed_dicts={input1: X, output1: Y},
                        val_feed_dicts={input1: X, output1: Y})
            trainer.fit(feed_dicts={input1: X1, input2: X2, output1: Y},
                        val_feed_dicts=0.1) # 10% of data used for validation

            # 2 Optimizers
            trainer.fit(feed_dicts=[{in1: X1, out1:Y}, {in2: X2, out2:Y2}],
                        val_feed_dicts=[{in1: X1, out1:Y}, {in2: X2, out2:Y2}])
            ```

        Arguments:
            feed_dicts: `dict` or list of `dict`. The dictionary to feed
                data to the network. It follows Tensorflow feed dict
                specifications: '{placeholder: data}'. In case of multiple
                optimizers, a list of dict is expected, that will
                respectively feed optimizers.
            n_epoch: `int`. Number of epoch to runs.
            val_feed_dicts: `dict`, list of `dict`, `float` or list of
                `float`. The data used for validation. Feed dict are
                following the same specification as `feed_dicts` above. It
                is also possible to provide a `float` for splitting training
                data for validation.
            show_metric: `bool`. If True, accuracy will be calculated and
                displayed at every step. Might give slower training.
            snapshot_step: `int`. If not None, the network will be snapshot
                every provided step (calculate validation loss/accuracy and
                save model, if a `checkpoint_path` is specified in `Trainer`).
            snapshot_epoch: `bool`. If True, snapshot the network at the end
                of every epoch.
            shuffle_all: `bool`. If True, shuffle all data batches (overrides
                `TrainOp` shuffle parameter behavior).
            run_id: `str`. A name for the current run. Used for Tensorboard
                display. If no name provided, a random one will be generated.

         N/A N/A run_id N/A N/A run_id N/A id_generator 6 N/A N/A print --------------------------------- N/A N/A print N/A Run id:  run_id N/A N/A print N/A Log directory:  N/A self tensorboard_dir N/A N/A isinstance shuffle_all bool N/A N/A t N/A self train_ops N/A N/A N/A t shuffle shuffle_all N/A N/A N/A N/A self graph as_default N/A N/A N/A self summ_writer N/A N/A N/A tf train SummaryWriter N/A N/A self tensorboard_dir run_id N/A N/A self session graph_def N/A feed_dicts N/A to_list feed_dicts N/A d feed_dicts N/A N/A N/A standarize_dict d N/A val_feed_dicts N/A to_list val_feed_dicts N/A val_feed_dicts N/A N/A N/A N/A standarize_dict d N/A d val_feed_dicts N/A N/A validation_split val_feed_dicts feed_dicts N/A termlogger N/A N/A callbacks TermLogger N/A self training_step N/A modelsaver N/A N/A callbacks ModelSaver N/A self save N/A self training_step N/A self checkpoint_path snapshot_epoch N/A N/A i train_op N/A enumerate N/A self train_ops N/A N/A vd N/A val_feed_dicts N/A val_feed_dicts N/A i None N/A N/A N/A train_op initialize_fit N/A feed_dicts N/A i vd show_metric N/A self summ_writer N/A metric_term_name None N/A N/A N/A train_op metric None N/A N/A N/A hasattr N/A train_op metric m_name N/A N/A metric_term_name N/A N/A train_op metric m_name N/A N/A metric_term_name N/A N/A N/A N/A N/A train_op metric name split : N/A 0 N/A N/A N/A termlogger add N/A train_op n_train_samples val_size N/A train_op n_val_samples metric_name metric_term_name name N/A train_op name N/A max_batches_len N/A N/A np max N/A N/A t n_batches N/A t N/A self train_ops N/A N/A N/A termlogger on_train_begin N/A N/A N/A modelsaver on_epoch_begin N/A N/A N/A epoch N/A range n_epoch N/A N/A N/A N/A termlogger on_epoch_begin N/A N/A N/A modelsaver on_epoch_begin N/A batch_step N/A range max_batches_len N/A N/A N/A self training_step 1 N/A N/A N/A termlogger on_batch_begin N/A N/A N/A modelsaver on_batch_begin N/A N/A global_loss global_acc N/A 0.0 0.0 N/A N/A i train_op N/A enumerate N/A self train_ops N/A N/A N/A N/A termlogger on_sub_epoch_begin N/A N/A N/A modelsaver on_sub_batch_begin N/A snapshot N/A N/A train_op _train N/A self training_step snapshot_epoch snapshot_step show_metric N/A global_loss N/A train_op loss_value N/A N/A N/A train_op acc_value global_acc N/A N/A global_acc N/A N/A train_op acc_value N/A len N/A self train_ops N/A N/A global_acc None N/A N/A N/A termlogger on_sub_batch_end i N/A train_op epoch N/A train_op step N/A train_op loss_value N/A train_op acc_value N/A train_op val_loss N/A train_op val_acc N/A N/A N/A modelsaver on_sub_batch_end N/A N/A N/A N/A self session run N/A self incr_global_step N/A N/A N/A termlogger on_batch_end global_loss global_acc snapshot N/A N/A N/A modelsaver on_batch_end snapshot N/A N/A N/A termlogger on_epoch_end N/A N/A N/A modelsaver on_epoch_end N/A N/A N/A N/A termlogger on_train_end N/A N/A N/A modelsaver on_train_end N/A save N/A N/A self model_file global_step N/A None N/A N/A  save.

        Save a Tensorflow model

        Arguments:
            model_file: `str`. Saving path of tensorflow model
            global_step: `float`. The training step to append to the
                model file name (optional).

         N/A N/A N/A l N/A N/A tf get_collection_ref summary_tags N/A N/A N/A Exception N/A N/A l N/A N/A tf get_collection summary_tags N/A l_stags N/A list l N/A N/A l N/A N/A N/A N/A model_file N/A 0 N/A / ~ N/A N/A model_file N/A ./ model_file N/A N/A N/A N/A self saver save N/A self session model_file global_step global_step N/A t l_stags N/A N/A N/A N/A tf add_to_collection summary_tags t N/A restore N/A N/A self model_file N/A N/A N/A  restore.

        Restore a Tensorflow model

        Arguments:
            model_file: path of tensorflow model to restore

         N/A N/A N/A self close_session N/A N/A self session N/A N/A tf Session N/A N/A N/A N/A self session run N/A N/A tf initialize_all_variables N/A N/A N/A N/A self restorer restore N/A self session model_file N/A o N/A self train_ops N/A N/A N/A o session N/A self session N/A N/A self restored True N/A N/A self training_step N/A int N/A N/A N/A self global_step eval N/A self session N/A close_session N/A N/A self N/A N/A N/A  Close session  N/A N/A N/A N/A self session close N/A validate_trainop_names N/A N/A self N/A N/A N/A  Give names to all TrainOp, handle no names and duplicated names  N/A t_len N/A len N/A self train_ops N/A i N/A range t_len N/A N/A N/A N/A N/A N/A self train_ops N/A i name N/A N/A N/A N/A N/A self train_ops N/A i name Optimizer N/A N/A N/A N/A self train_ops N/A i scope_name Optimizer N/A i N/A range t_len N/A N/A dupl 0 N/A j N/A range N/A i 1 t_len N/A N/A N/A N/A N/A N/A self train_ops N/A i name N/A N/A N/A N/A N/A N/A N/A self train_ops N/A i name N/A N/A N/A self train_ops N/A j name N/A N/A N/A dupl 0 N/A N/A N/A N/A N/A self train_ops N/A i name N/A _ N/A str dupl N/A N/A N/A N/A self train_ops N/A i scope_name N/A N/A N/A self train_ops N/A i name N/A dupl 1 N/A N/A N/A N/A self train_ops N/A j name N/A _ N/A str dupl N/A N/A N/A N/A self train_ops N/A j scope_name N/A N/A N/A self train_ops N/A j name N/A N/A TrainOp N/A object N/A N/A  TrainOp.

    TrainOp represents a set of operation used for optimizing a network.

    A TrainOp is meant to hold all training parameters of an optimizer.
    `Trainer` class will then instantiate them all specifically considering all
    optimizers of the network (set names, scopes... set optimization ops...).

    Arguments:
        loss: `Tensor`. Loss operation to evaluate network cost.
            Optimizer will use this cost function to train network.
        optimizer: `Optimizer`. Tensorflow Optimizer. The optimizer to
            use to train network.
        metric:  `Tensor`. The metric tensor to be used for evaluation.
        batch_size: `int`. Batch size for data feeded to this optimizer.
            Default: 64.
        ema: `float`. Exponential moving averages.
        trainable_vars: list of `tf.Variable`. List of trainable variables to
            use for training. Default: all trainable variables.
        shuffle: `bool`. Shuffle data.
        step_tensor: `tf.Tensor`. A variable holding training step. If not
            provided, it will be created. Early defining the step tensor
            might be useful for network creation, such as for learning rate
            decay.
        name: `str`. A name for this class (optional).
        graph: `tf.Graph`. Tensorflow Graph to use for training. Default:
            default tf graph.

     __init__ N/A N/A self loss optimizer metric batch_size ema trainable_vars shuffle step_tensor name graph N/A None 64 0.0 None True None None None N/A N/A N/A self graph N/A N/A tf get_default_graph N/A graph N/A N/A N/A self graph graph N/A N/A self name name N/A N/A self scope_name name N/A N/A self loss loss N/A N/A self optimizer optimizer N/A N/A self metric metric N/A N/A self metric_summ_name N/A N/A metric None N/A N/A N/A self metric_summ_name N/A N/A N/A N/A metric name split / N/A 0 N/A N/A self grad None N/A N/A self apply_grad None N/A N/A self summ_op None N/A N/A self val_summary_op None N/A N/A self train_vars trainable_vars N/A N/A self shuffle shuffle N/A N/A self epoch 0 N/A N/A self step 0 N/A N/A self batches None N/A N/A self batch_index 0 N/A N/A self batch_start 0 N/A N/A self batch_end 0 N/A N/A self batch_size batch_size N/A N/A self data_size 0 N/A N/A self n_batches 0 N/A N/A self ema ema N/A N/A self feed_dict None N/A N/A self val_feed_dict None N/A N/A self loss_value None N/A N/A self val_loss None N/A N/A self acc_value None N/A N/A self val_acc None N/A N/A step_tensor None N/A N/A N/A N/A N/A self graph as_default N/A N/A N/A self training_steps N/A N/A tf Variable 0.0 name Training_step trainable False N/A N/A N/A self training_steps step_tensor N/A N/A N/A isinstance N/A self loss N/A tf Tensor N/A N/A N/A ValueError Unknown Loss type N/A N/A N/A isinstance N/A self optimizer N/A tf_optimizer Optimizer N/A N/A N/A ValueError Unknown Optimizer N/A N/A N/A self train_vars None N/A N/A N/A self train_vars N/A N/A tf trainable_variables N/A N/A N/A self train_var N/A to_list N/A self train_vars N/A N/A self train None N/A initialize_training_ops N/A N/A self i session tensorboard_verbose clip_gradients N/A N/A N/A  initialize_training_ops.

        Initialize all ops used for training. Because a network can have
        multiple optimizers, an id 'i' is allocated to differentiate them.
        This is meant to be used by `Trainer` when initializing all train ops.

        Arguments:
            i: `int`. This optimizer training process ID.
            session: `tf.Session`. The session used to train the network.
            tensorboard_verbose: `int`. Logs verbose. Supports:
                ```
                0 - Loss, Accuracy.
                1 - Loss, Accuracy, Gradients.
                2 - Loss, Accuracy, Gradients, Weights.
                3 - Loss, Accuracy, Gradients, Weights, Activations, Sparsity..
                ```
            clip_gradients: `float`. Option for clipping gradients.
         N/A N/A self session session N/A N/A self val_loss_T N/A N/A tf Variable 0.0 name val_loss trainable False N/A N/A self val_acc_T N/A N/A tf Variable 0.0 name val_acc trainable False N/A N/A N/A self metric None N/A N/A N/A self acc_averages N/A N/A N/A tf train ExponentialMovingAverage 0.9 N/A self training_steps name moving_avg N/A acc_avg_op N/A N/A N/A self acc_averages apply N/A N/A self metric N/A N/A acc_avg_op N/A N/A tf no_op N/A N/A N/A tf name_scope N/A self name N/A N/A lss N/A N/A N/A self loss N/A N/A tf get_collection N/A N/A tf GraphKeys REGULARIZATION_LOSSES N/A total_loss N/A N/A tf add_n lss name Total_Loss N/A loss_avg_op N/A N/A summaries add_loss_summaries total_loss N/A self loss regul_losses_collection_key N/A N/A tf GraphKeys REGULARIZATION_LOSSES name_prefix N/A self scope_name summaries_collection_key N/A N/A self name _training_summaries exp_moving_avg 0.9 ema_num_updates N/A self training_steps N/A N/A N/A tf control_dependencies N/A loss_avg_op acc_avg_op N/A N/A N/A self grad N/A N/A tf gradients total_loss N/A self train_vars N/A N/A clip_gradients 0.0 N/A N/A N/A N/A self grad N/A self grad_norm N/A N/A tf clip_by_global_norm N/A self grad clip_gradients N/A N/A self grad N/A list N/A zip N/A self grad N/A self train_vars N/A N/A self apply_grad N/A N/A N/A self optimizer apply_gradients grads_and_vars N/A self grad global_step N/A self training_steps name N/A apply_grad_op_ N/A str i N/A N/A N/A self create_summaries tensorboard_verbose N/A N/A N/A self ema 0.0 N/A N/A var_averages N/A N/A N/A tf train ExponentialMovingAverage N/A self ema N/A self training_steps N/A var_averages_op N/A N/A var_averages apply N/A self train_vars N/A N/A N/A tf control_dependencies N/A var_averages_op N/A N/A N/A N/A tf control_dependencies N/A N/A self apply_grad N/A N/A N/A self train N/A N/A tf no_op name N/A train_op_ N/A str i N/A N/A N/A N/A tf control_dependencies N/A N/A self apply_grad N/A N/A N/A self train N/A N/A tf no_op name N/A train_op_ N/A str i N/A initialize_fit N/A N/A self feed_dict val_feed_dict show_metric summ_writer N/A N/A N/A  initialize_fit.

        Initialize data for feeding the training process. It is meant to
        be used by `Trainer` before starting to fit data.

        Arguments:
            feed_dict: `dict`. The data dictionary to feed.
            val_feed_dict: `dict`. The validation data dictionary to feed.
            show_metric: `bool`. If True, display accuracy at every step.
            summ_writer: `SummaryWriter`. The summary writer to use for
                Tensorboard logging.

         N/A N/A self summary_writer summ_writer N/A N/A self feed_dict feed_dict N/A N/A self val_feed_dict val_feed_dict N/A N/A self n_train_samples N/A len N/A get_dict_first_element feed_dict N/A N/A self n_val_samples 0 N/A val_feed_dict N/A N/A N/A self n_val_samples N/A len N/A get_dict_first_element val_feed_dict N/A N/A self index_array N/A N/A np arange N/A self n_train_samples N/A N/A N/A self create_testing_summaries show_metric N/A self metric_summ_name val_feed_dict N/A N/A self shuffle N/A N/A N/A N/A N/A np random shuffle N/A self index_array N/A N/A N/A self set_batches N/A make_batches N/A self n_train_samples N/A self batch_size N/A set_batches N/A N/A self batches N/A N/A N/A N/A self batches batches N/A N/A self n_batches N/A len batches N/A N/A self batch_size N/A int N/A N/A N/A batches N/A 0 N/A 1 N/A N/A batches N/A 0 N/A 0 N/A N/A self data_size N/A N/A N/A self batch_size N/A N/A self n_batches 1 N/A int N/A N/A N/A batches N/A -1 N/A 1 N/A N/A batches N/A -1 N/A 0 N/A N/A N/A self batch_start N/A self batch_end N/A N/A self batches N/A N/A self batch_index N/A next_batch N/A N/A self N/A N/A N/A  Return True if a next batch is available  N/A N/A self batch_index 1 N/A N/A self step N/A min N/A N/A self batch_index N/A self batch_size N/A self data_size N/A N/A N/A self batch_index N/A self n_batches N/A N/A N/A self batch_index 0 N/A N/A self epoch 1 N/A N/A self step 0 N/A False N/A N/A N/A self batch_start N/A self batch_end N/A N/A self batches N/A N/A self batch_index N/A True N/A _train N/A N/A self training_step snapshot_epoch snapshot_step show_metric N/A N/A N/A  Training process for this optimizer.

        Arguments:
            training_step: `int`. The global step.
            snapshot_epoch: `bool`. If True, snapshot network at each epoch.
            snapshot_step: `int`. If not None, snapshot network given 'step'.
            show_metric: `bool`. If True, display accuracy at every step.

         N/A N/A N/A tflearn is_training True N/A self session N/A N/A N/A self loss_value N/A self acc_value N/A None None N/A N/A N/A self val_loss N/A self val_acc N/A None None N/A N/A train_summ_str test_summ_str N/A None None N/A snapshot False N/A batch_ids N/A N/A self index_array N/A N/A self batch_start N/A self batch_end N/A feed_batch N/A N/A key N/A self feed_dict N/A N/A N/A N/A N/A np ndim N/A N/A self feed_dict N/A key 0 N/A N/A N/A feed_batch N/A key N/A slice_array N/A N/A self feed_dict N/A key batch_ids N/A N/A N/A feed_batch N/A key N/A N/A self feed_dict N/A key N/A N/A N/A tflearn is_training True N/A self session N/A N/A N/A N/A self session run N/A N/A self train feed_batch N/A N/A N/A tflearn is_training False N/A self session N/A N/A N/A self summ_op None N/A N/A train_summ_str N/A N/A N/A self session run N/A self summ_op feed_batch N/A sname N/A - Loss/ N/A self scope_name N/A N/A self loss_value N/A N/A summaries get_value_from_summary_string sname train_summ_str N/A N/A show_metric N/A N/A self metric None N/A N/A sname N/A N/A N/A -  N/A self metric_summ_name / N/A self scope_name N/A N/A self acc_value N/A N/A summaries get_value_from_summary_string sname train_summ_str N/A N/A N/A N/A self next_batch N/A N/A N/A self shuffle N/A N/A N/A N/A N/A np random shuffle N/A self index_array N/A batches N/A make_batches N/A self n_train_samples N/A self batch_size N/A N/A N/A self set_batches batches N/A snapshot_epoch N/A N/A snapshot True N/A snapshot_step N/A N/A N/A N/A training_step snapshot_step 0 N/A N/A snapshot True N/A N/A snapshot N/A self val_feed_dict N/A N/A N/A self val_loss N/A evaluate N/A self session N/A self loss N/A self val_feed_dict N/A self batch_size N/A N/A show_metric N/A N/A self metric None N/A N/A N/A self val_acc N/A evaluate N/A self session N/A self metric N/A self val_feed_dict N/A self batch_size N/A show_metric N/A N/A update_val_op N/A N/A N/A tf assign N/A self val_loss_T N/A self val_loss N/A N/A tf assign N/A self val_acc_T N/A self val_acc N/A N/A update_val_op N/A N/A tf assign N/A self val_loss_T N/A self val_loss N/A N/A N/A N/A self session run update_val_op N/A test_summ_str N/A N/A N/A self session run N/A self val_summary_op N/A self val_feed_dict N/A n_step N/A N/A N/A self training_steps eval session N/A self session N/A N/A n_step 1 N/A N/A train_summ_str N/A N/A N/A N/A N/A self summary_writer add_summary train_summ_str n_step N/A test_summ_str N/A N/A N/A N/A N/A self summary_writer add_summary test_summ_str n_step N/A snapshot N/A duplicate N/A N/A self N/A N/A N/A  Returns a duplicated `TrainOp`  N/A N/A TrainOp N/A self loss optimizer N/A self optimizer batch_size N/A self batch_size ema N/A self ema metric N/A self metric trainable_vars N/A self train_vars shuffle N/A self shuffle N/A create_summaries N/A N/A self verbose N/A 2 N/A N/A  Create summaries with `verbose` level  N/A summ_collection N/A N/A self name _training_summaries N/A N/A verbose N/A 3 N/A N/A activations N/A N/A tf get_collection N/A N/A tf GraphKeys ACTIVATIONS N/A N/A summarize_activations activations summ_collection N/A N/A verbose N/A 2 3 N/A N/A N/A summarize_variables N/A self train_vars summ_collection N/A N/A verbose N/A 1 2 3 N/A N/A N/A summarize_gradients N/A self grad summ_collection N/A N/A self summ_op N/A N/A tf merge_summary N/A N/A tf get_collection summ_collection N/A create_testing_summaries N/A N/A self show_metric metric_name validation_set N/A False Accuracy None N/A N/A  Create accuracy and validation summaries  N/A tr_summ_collection N/A N/A self name _training_summaries N/A te_summ_collection N/A N/A self name _testing_summaries N/A mn N/A N/A metric_name replace /Mean:0/ N/A N/A show_metric N/A N/A self metric None N/A N/A sname N/A N/A N/A N/A -  mn / N/A self scope_name  (raw) N/A N/A summarize N/A self metric scalar sname tr_summ_collection N/A sname N/A N/A N/A -  mn / N/A self scope_name N/A N/A self summ_op N/A summarize N/A N/A N/A self acc_averages average N/A self metric scalar sname tr_summ_collection N/A N/A validation_set None N/A N/A loss_val_name N/A N/A - Loss/ N/A self scope_name /Validation N/A loss_val_name N/A check_scope_path loss_val_name N/A N/A self val_summary_op N/A summarize N/A self val_loss_T scalar loss_val_name te_summ_collection N/A N/A show_metric N/A N/A self metric None N/A N/A acc_val_name N/A N/A N/A N/A -  mn / N/A self scope_name /Validation N/A acc_val_name N/A check_scope_path acc_val_name N/A N/A self val_summary_op N/A summarize N/A self val_acc_T scalar acc_val_name te_summ_collection N/A N/A duplicate_identical_ops N/A N/A ops N/A N/A N/A  Duplicate identical `TrainOp`  N/A i N/A range N/A len ops N/A N/A j N/A range N/A i 1 N/A len ops N/A N/A N/A N/A ops N/A i N/A ops N/A j N/A N/A N/A ops N/A j N/A N/A N/A ops N/A i duplicate N/A validation_split N/A N/A val_feed_dicts feed_dicts N/A N/A N/A  validation_split.

    Handles validation split; build validation data based on a
    percentage of training data. It checks all val_feed_dicts keys
    values for a float, if found, it retrieves the exact same key in feed_dict
    and split its data according to `float` value and move it to val_feed_dict.

    Args:
        val_feed_dicts: `dict` of arrays or float. validation dictionary.
        feed_dicts: `dict` of arrays. training data dictionary.

     N/A val_feed_dicts N/A N/A N/A i val_dict N/A enumerate val_feed_dicts N/A N/A N/A key val N/A N/A val_dict items N/A N/A N/A isinstance val float N/A N/A split val N/A N/A N/A type N/A N/A feed_dicts N/A i N/A key N/A list N/A np ndarray N/A N/A split_at N/A int N/A N/A len N/A N/A feed_dicts N/A i N/A key N/A 1 split N/A N/A N/A N/A feed_dicts N/A i N/A key N/A N/A val_feed_dicts N/A i N/A key N/A N/A slice_array N/A N/A feed_dicts N/A i N/A key 0 split_at N/A slice_array N/A N/A feed_dicts N/A i N/A key split_at N/A N/A N/A N/A val_feed_dicts N/A i N/A key N/A N/A feed_dicts N/A i N/A key N/A evaluate N/A N/A session op_to_evaluate feed_dict batch_size N/A N/A N/A  evaluate.

        Evaluate an operation with provided data dict using a batch size
        to save GPU memory.

        Args:
            session: `tf.Session`. Session for running operations.
            op_to_evaluate: `tf.Op`. Operation to be evaluated.
            feed_dict: `dict`. Data dictionary to feed op_to_evaluate.
            batch_size: `int`. Batch size to be used for evaluation.

        Ret:
            `float`. op_to_evaluate mean over all batches.

         N/A N/A N/A tflearn is_training False session N/A n_test_samples N/A len N/A get_dict_first_element feed_dict N/A batches N/A make_batches n_test_samples batch_size N/A index_array N/A N/A np arange n_test_samples N/A avg 0.0 N/A N/A i N/A batch_start batch_end N/A enumerate batches N/A N/A batch_ids N/A index_array N/A batch_start batch_end N/A feed_batch N/A N/A key feed_dict N/A N/A N/A N/A N/A np ndim N/A feed_dict N/A key 0 N/A N/A N/A feed_batch N/A key N/A slice_array N/A feed_dict N/A key batch_ids N/A N/A N/A feed_batch N/A key N/A feed_dict N/A key N/A avg N/A N/A N/A session run op_to_evaluate feed_batch N/A len batches N/A avg N/A