parameters: (self, hidden_units, n_classes=0, batch_size=32,
                 steps=200, optimizer="Adagrad", learning_rate=0.1,
                 clip_gradients=5.0, activation=nn.relu, add_noise=None,
                 continue_training=False, config=None,
                 verbose=1, dropout=None)
docstring: Initializes a TensorFlowDNNAutoencoder instance.
  
      Args:
        hidden_units: List of hidden units per layer.
        batch_size: Mini batch size.
        activation: activation function used to map inner latent layer onto
                    reconstruction layer.
        add_noise: a function that adds noise to tensor_in,
               e.g. def add_noise(x):
                        return(x + np.random.normal(0, 0.1, (len(x), len(x[0]))))
        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:
            def exp_decay(global_step):
                return tf.train.exponential_decay(
                    learning_rate=0.1, global_step,
                    decay_steps=2, decay_rate=0.001)
        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.
        verbose: Controls the verbosity, possible values:
                 0: the algorithm and debug information is muted.
                 1: trainer prints the progress.
                 2: log device placement is printed.
        dropout: When not None, the probability we will drop out a given
                 coordinate.
docstring_summary: Initializes a TensorFlowDNNAutoencoder instance.
docstring_tokens: Initializes a TensorFlowDNNAutoencoder instance .
function: def __init__(self, hidden_units, n_classes=0, batch_size=32,
                 steps=200, optimizer="Adagrad", learning_rate=0.1,
                 clip_gradients=5.0, activation=nn.relu, add_noise=None,
                 continue_training=False, config=None,
                 verbose=1, dropout=None):
      """Initializes a TensorFlowDNNAutoencoder instance.
  
      Args:
        hidden_units: List of hidden units per layer.
        batch_size: Mini batch size.
        activation: activation function used to map inner latent layer onto
                    reconstruction layer.
        add_noise: a function that adds noise to tensor_in,
               e.g. def add_noise(x):
                        return(x + np.random.normal(0, 0.1, (len(x), len(x[0]))))
        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:
            def exp_decay(global_step):
                return tf.train.exponential_decay(
                    learning_rate=0.1, global_step,
                    decay_steps=2, decay_rate=0.001)
        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.
        verbose: Controls the verbosity, possible values:
                 0: the algorithm and debug information is muted.
                 1: trainer prints the progress.
                 2: log device placement is printed.
        dropout: When not None, the probability we will drop out a given
                 coordinate.
      """
      self.hidden_units = hidden_units
      self.dropout = dropout
      self.activation = activation
      self.add_noise = add_noise
      super(TensorFlowDNNAutoencoder, self).__init__(
          model_fn=self._model_fn,
          n_classes=n_classes,
          batch_size=batch_size, steps=steps, optimizer=optimizer,
          learning_rate=learning_rate, clip_gradients=clip_gradients,
          continue_training=continue_training,
          config=config, verbose=verbose)
function_tokens: def __init__ ( self , hidden_units , n_classes = 0 , batch_size = 32 , steps = 200 , optimizer = "Adagrad" , learning_rate = 0.1 , clip_gradients = 5.0 , activation = nn . relu , add_noise = None , continue_training = False , config = None , verbose = 1 , dropout = None ) : self . hidden_units = hidden_units self . dropout = dropout self . activation = activation self . add_noise = add_noise super ( TensorFlowDNNAutoencoder , self ) . __init__ ( model_fn = self . _model_fn , n_classes = n_classes , batch_size = batch_size , steps = steps , optimizer = optimizer , learning_rate = learning_rate , clip_gradients = clip_gradients , continue_training = continue_training , config = config , verbose = verbose )