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: Evaluate.
  
  Evaluate model on given samples.
  
  Arguments:
      X: array, `list` of array (if multiple inputs) or `dict`
          (with inputs layer name as keys). Data to feed to train
          model.
      Y: 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].
      batch_size: `int`. The batch size. Default: 128.
  
  Returns:
      The metric score.
docstr_tok: ["Evaluate",".","Evaluate","model","on","given","samples",".","Arguments",":","X",":","array",",","`","list","`","of","array","(","if","multiple","inputs",")","or","`","dict","`","(","with","inputs","layer","name","as","keys",")",".","Data","to","feed","to","train","model",".","Y",":","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","]",".","batch_size",":","`","int","`",".","The","batch","size",".","Default",":","128",".","Returns",":","The","metric","score","."]
code: def evaluate(self, X, Y, batch_size=128):
      """Evaluate.
  
      Evaluate model on given samples.
  
      Arguments:
          X: array, `list` of array (if multiple inputs) or `dict`
              (with inputs layer name as keys). Data to feed to train
              model.
          Y: 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].
          batch_size: `int`. The batch size. Default: 128.
  
      Returns:
          The metric score.
  
      """
      feed_dict = feed_dict_builder(X, Y, self.inputs, self.targets)
      return eval(self.trainer.session, self.net, feed_dict, batch_size)
  
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 9 for calling the SequenceGenerator obj's underlying member method code with example usage: obj.evaluate(X, Y, batch_size) and returns: eval