parameters: (self,
                 rnn_size,
                 n_classes,
                 cell_type='gru',
                 num_layers=1,
                 input_op_fn=null_input_op_fn,
                 initial_state=None,
                 bidirectional=False,
                 sequence_length=None,
                 attn_length=None,
                 attn_size=None,
                 attn_vec_size=None,
                 batch_size=32,
                 steps=50,
                 optimizer='Adagrad',
                 learning_rate=0.1,
                 class_weight=None,
                 clip_gradients=5.0,
                 continue_training=False,
                 config=None,
                 verbose=1)
docstring: Initializes a TensorFlowRNNClassifier instance.
  
      Args:
        rnn_size: The size for rnn cell, e.g. size of your word embeddings.
        cell_type: The type of rnn cell, including rnn, gru, and lstm.
        num_layers: The number of layers of the rnn model.
        input_op_fn: Function that will transform the input tensor, such as
          creating word embeddings, byte list, etc. This takes
          an argument x for input and returns transformed x.
        bidirectional: boolean, Whether this is a bidirectional rnn.
        sequence_length: If sequence_length is provided, dynamic calculation
          is performed. This saves computational time when unrolling past max
          sequence length.
        initial_state: An initial state for the RNN. This must be a tensor of
          appropriate type and shape [batch_size x cell.state_size].
        attn_length: integer, the size of attention vector attached to rnn cells.
        attn_size: integer, the size of an attention window attached to rnn cells.
        attn_vec_size: integer, the number of convolutional features calculated on
          attention state and the size of the hidden layer built from base cell state.
        n_classes: Number of classes in the target.
        batch_size: Mini batch size.
        steps: Number of steps to run over data.
        optimizer: Optimizer name (or class), for example "SGD", "Adam",
          "Adagrad".
        learning_rate: If this is constant float value, no decay function is
          used. Instead, a customized decay function can be passed that accepts
          global_step as parameter and returns a Tensor.
          e.g. exponential decay function:
  
          ````python
          def exp_decay(global_step):
              return tf.train.exponential_decay(
                  learning_rate=0.1, global_step,
                  decay_steps=2, decay_rate=0.001)
          ````
  
        class_weight: None or list of n_classes floats. Weight associated with
          classes for loss computation. If not given, all classes are
          supposed to have weight one.
        continue_training: when continue_training is True, once initialized
          model will be continuely trained on every call of fit.
        config: RunConfig object that controls the configurations of the session,
          e.g. num_cores, gpu_memory_fraction, etc.
docstring_summary: Initializes a TensorFlowRNNClassifier instance.
docstring_tokens: Initializes a TensorFlowRNNClassifier instance .
function: def __init__(self,
                 rnn_size,
                 n_classes,
                 cell_type='gru',
                 num_layers=1,
                 input_op_fn=null_input_op_fn,
                 initial_state=None,
                 bidirectional=False,
                 sequence_length=None,
                 attn_length=None,
                 attn_size=None,
                 attn_vec_size=None,
                 batch_size=32,
                 steps=50,
                 optimizer='Adagrad',
                 learning_rate=0.1,
                 class_weight=None,
                 clip_gradients=5.0,
                 continue_training=False,
                 config=None,
                 verbose=1):
      """Initializes a TensorFlowRNNClassifier instance.
  
      Args:
        rnn_size: The size for rnn cell, e.g. size of your word embeddings.
        cell_type: The type of rnn cell, including rnn, gru, and lstm.
        num_layers: The number of layers of the rnn model.
        input_op_fn: Function that will transform the input tensor, such as
          creating word embeddings, byte list, etc. This takes
          an argument x for input and returns transformed x.
        bidirectional: boolean, Whether this is a bidirectional rnn.
        sequence_length: If sequence_length is provided, dynamic calculation
          is performed. This saves computational time when unrolling past max
          sequence length.
        initial_state: An initial state for the RNN. This must be a tensor of
          appropriate type and shape [batch_size x cell.state_size].
        attn_length: integer, the size of attention vector attached to rnn cells.
        attn_size: integer, the size of an attention window attached to rnn cells.
        attn_vec_size: integer, the number of convolutional features calculated on
          attention state and the size of the hidden layer built from base cell state.
        n_classes: Number of classes in the target.
        batch_size: Mini batch size.
        steps: Number of steps to run over data.
        optimizer: Optimizer name (or class), for example "SGD", "Adam",
          "Adagrad".
        learning_rate: If this is constant float value, no decay function is
          used. Instead, a customized decay function can be passed that accepts
          global_step as parameter and returns a Tensor.
          e.g. exponential decay function:
  
          ````python
          def exp_decay(global_step):
              return tf.train.exponential_decay(
                  learning_rate=0.1, global_step,
                  decay_steps=2, decay_rate=0.001)
          ````
  
        class_weight: None or list of n_classes floats. Weight associated with
          classes for loss computation. If not given, all classes are
          supposed to have weight one.
        continue_training: when continue_training is True, once initialized
          model will be continuely trained on every call of fit.
        config: RunConfig object that controls the configurations of the session,
          e.g. num_cores, gpu_memory_fraction, etc.
      """
  
      self.rnn_size = rnn_size
      self.cell_type = cell_type
      self.input_op_fn = input_op_fn
      self.bidirectional = bidirectional
      self.num_layers = num_layers
      self.sequence_length = sequence_length
      self.initial_state = initial_state
      self.attn_length = attn_length
      self.attn_size = attn_size
      self.attn_vec_size = attn_vec_size
      super(TensorFlowRNNClassifier, self).__init__(
          model_fn=self._model_fn,
          n_classes=n_classes,
          batch_size=batch_size,
          steps=steps,
          optimizer=optimizer,
          learning_rate=learning_rate,
          class_weight=class_weight,
          clip_gradients=clip_gradients,
          continue_training=continue_training,
          config=config,
          verbose=verbose)
function_tokens: def __init__ ( self , rnn_size , n_classes , cell_type = 'gru' , num_layers = 1 , input_op_fn = null_input_op_fn , initial_state = None , bidirectional = False , sequence_length = None , attn_length = None , attn_size = None , attn_vec_size = None , batch_size = 32 , steps = 50 , optimizer = 'Adagrad' , learning_rate = 0.1 , class_weight = None , clip_gradients = 5.0 , continue_training = False , config = None , verbose = 1 ) : self . rnn_size = rnn_size self . cell_type = cell_type self . input_op_fn = input_op_fn self . bidirectional = bidirectional self . num_layers = num_layers self . sequence_length = sequence_length self . initial_state = initial_state self . attn_length = attn_length self . attn_size = attn_size self . attn_vec_size = attn_vec_size super ( TensorFlowRNNClassifier , self ) . __init__ ( model_fn = self . _model_fn , n_classes = n_classes , batch_size = batch_size , steps = steps , optimizer = optimizer , learning_rate = learning_rate , class_weight = class_weight , clip_gradients = clip_gradients , continue_training = continue_training , config = config , verbose = verbose )