class_docstr: Sequence Generator Model.
  
  A deep neural network model for generating sequences.
  
  Arguments:
      network: `Tensor`. Neural network to be used.
      dictionary: `dict`. A dictionary associating each sample with a key (
          usually integers). For example: {'a': 0, 'b': 1, 'c': 2, ...}.
      seq_maxlen: `int`. The maximum length of a sequence.
      tensorboard_verbose: `int`. Summary verbose level, it accepts
          different levels of tensorboard logs:
          ```python
          0 - Loss, Accuracy (Best Speed).
          1 - Loss, Accuracy, Gradients.
          2 - Loss, Accuracy, Gradients, Weights.
          3 - Loss, Accuracy, Gradients, Weights, Activations, Sparsity.
              (Best visualization)
          ```
      tensorboard_dir: `str`. Directory to store tensorboard logs.
          Default: "/tmp/tflearn_logs/"
      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.
      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.
  
  Attributes:
      trainer: `Trainer`. Handle model training.
      predictor: `Predictor`. Handle model prediction.
      session: `Session`. The current model session.
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docstr: Fit.
  
  Train model, feeding X_inputs and Y_targets to the network.
  
  NOTE: When not feeding dicts, data assignations is made by
      input/estimator layers creation order (For example, the second
      input layer created will be feeded by the second value of
      X_inputs list).
  
  Examples:
      ```python
      model.fit(X, Y) # Single input and output
      model.fit({'input1': X}, {'output1': Y}) # Single input and output
      model.fit([X1, X2], Y) # Mutliple inputs, Single output
  
      # validate with X_val and [Y1_val, Y2_val]
      model.fit(X, [Y1, Y2], validation_set=(X_val, [Y1_val, Y2_val]))
      # 10% of training data used for validation
      model.fit(X, Y, validation_set=0.1)
      ```
  
  Arguments:
      X_inputs: array, `list` of array (if multiple inputs) or `dict`
          (with inputs layer name as keys). Data to feed to train
          model.
      Y_targets: array, `list` of array (if multiple inputs) or `dict`
          (with estimators layer name as keys). Targets (Labels) to
          feed to train model. Usually set as the next element of a
          sequence, i.e. for x[0] => y[0] = x[1].
      n_epoch: `int`. Number of epoch to run. Default: None.
      validation_set: `tuple`. Represents data used for validation.
          `tuple` holds data and targets (provided as same type as
          X_inputs and Y_targets). Additionally, it also accepts
          `float` (<1) to performs a data split over training data.
      show_metric: `bool`. Display or not accuracy at every step.
      batch_size: `int` or None. If `int`, overrides all network
          estimators 'batch_size' by this value.
      shuffle: `bool` or None. If `bool`, overrides all network
          estimators 'shuffle' by this value.
      snapshot_epoch: `bool`. If True, it will snapshot model at the end
          of every epoch. (Snapshot a model will evaluate this model
          on validation set, as well as create a checkpoint if
          'checkpoint_path' specified).
      snapshot_step: `int` or None. If `int`, it will snapshot model
          every 'snapshot_step' steps.
      excl_trainops: `list` of `TrainOp`. A list of train ops to
          exclude from training process (TrainOps can be retrieve
          through `tf.get_collection_ref(tf.GraphKeys.TRAIN_OPS)`).
      run_id: `str`. Give a name for this run. (Useful for Tensorboard).
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code: def fit(
      self,
      X_inputs,
      Y_targets,
      n_epoch=10,
      validation_set=None,
      show_metric=False,
      batch_size=None,
      shuffle=None,
      snapshot_epoch=True,
      snapshot_step=None,
      excl_trainops=None,
      run_id=None,
  ):
      """Fit.
  
      Train model, feeding X_inputs and Y_targets to the network.
  
      NOTE: When not feeding dicts, data assignations is made by
          input/estimator layers creation order (For example, the second
          input layer created will be feeded by the second value of
          X_inputs list).
  
      Examples:
          ```python
          model.fit(X, Y) # Single input and output
          model.fit({'input1': X}, {'output1': Y}) # Single input and output
          model.fit([X1, X2], Y) # Mutliple inputs, Single output
  
          # validate with X_val and [Y1_val, Y2_val]
          model.fit(X, [Y1, Y2], validation_set=(X_val, [Y1_val, Y2_val]))
          # 10% of training data used for validation
          model.fit(X, Y, validation_set=0.1)
          ```
  
      Arguments:
          X_inputs: array, `list` of array (if multiple inputs) or `dict`
              (with inputs layer name as keys). Data to feed to train
              model.
          Y_targets: array, `list` of array (if multiple inputs) or `dict`
              (with estimators layer name as keys). Targets (Labels) to
              feed to train model. Usually set as the next element of a
              sequence, i.e. for x[0] => y[0] = x[1].
          n_epoch: `int`. Number of epoch to run. Default: None.
          validation_set: `tuple`. Represents data used for validation.
              `tuple` holds data and targets (provided as same type as
              X_inputs and Y_targets). Additionally, it also accepts
              `float` (<1) to performs a data split over training data.
          show_metric: `bool`. Display or not accuracy at every step.
          batch_size: `int` or None. If `int`, overrides all network
              estimators 'batch_size' by this value.
          shuffle: `bool` or None. If `bool`, overrides all network
              estimators 'shuffle' by this value.
          snapshot_epoch: `bool`. If True, it will snapshot model at the end
              of every epoch. (Snapshot a model will evaluate this model
              on validation set, as well as create a checkpoint if
              'checkpoint_path' specified).
          snapshot_step: `int` or None. If `int`, it will snapshot model
              every 'snapshot_step' steps.
          excl_trainops: `list` of `TrainOp`. A list of train ops to
              exclude from training process (TrainOps can be retrieve
              through `tf.get_collection_ref(tf.GraphKeys.TRAIN_OPS)`).
          run_id: `str`. Give a name for this run. (Useful for Tensorboard).
  
      """
      if batch_size:
          for train_op in self.train_ops:
              train_op.batch_size = batch_size
  
      valX, valY = None, None
      if validation_set:
          if isinstance(validation_set, float):
              valX = validation_set
              valY = validation_set
          else:
              valX = validation_set[0]
              valY = validation_set[1]
  
      # For simplicity we build sync dict synchronously but
      # Trainer support asynchronous feed dict allocation
      feed_dict = feed_dict_builder(
          X_inputs, Y_targets, self.inputs, self.targets
      )
      feed_dicts = [feed_dict for i in self.train_ops]
  
      val_feed_dicts = None
      if not (is_none(valX) or is_none(valY)):
          if isinstance(valX, float):
              val_feed_dicts = valX
          else:
              val_feed_dict = feed_dict_builder(
                  valX, valY, self.inputs, self.targets
              )
              val_feed_dicts = [val_feed_dict for i in self.train_ops]
  
      # Retrieve data preprocesing and augmentation
      dprep_dict, daug_dict = {}, {}
      dprep_collection = tf.get_collection(tf.GraphKeys.DATA_PREP)
      daug_collection = tf.get_collection(tf.GraphKeys.DATA_AUG)
      for i in range(len(self.inputs)):
          if dprep_collection[i] is not None:
              dprep_dict[self.inputs[i]] = dprep_collection[i]
          if daug_collection[i] is not None:
              daug_dict[self.inputs[i]] = daug_collection[i]
  
      self.trainer.fit(
          feed_dicts,
          val_feed_dicts=val_feed_dicts,
          n_epoch=n_epoch,
          show_metric=show_metric,
          snapshot_step=snapshot_step,
          snapshot_epoch=snapshot_epoch,
          shuffle_all=shuffle,
          dprep_dict=dprep_dict,
          daug_dict=daug_dict,
          excl_trainops=excl_trainops,
          run_id=run_id,
      )
      self.predictor = Evaluator(
          [self.net], session=self.trainer.session
      )
  
file_path: tflearn/tflearn/models/generator.py
imports: from __future__ import division, print_function, absolute_import
  import sys
  import numpy
  import tensorflow.compat.v1
  from ..helpers.trainer import Trainer, evaluate
  from ..helpers.evaluator import Evaluator
  from ..utils import feed_dict_builder, is_none
label_desc: Use image node_id 2 for calling the SequenceGenerator obj's underlying member method code with example usage: obj.fit(X_inputs, Y_targets, n_epoch, validation_set, show_metric, batch_size, shuffle, snapshot_epoch, snapshot_step, excl_trainops, run_id) without return types