parameters: (fingerprint_input, model_settings, is_training)
docstring: Builds a model with a single hidden fully-connected layer.
  
    This is a very simple model with just one matmul and bias layer. As you'd
    expect, it doesn't produce very accurate results, but it is very fast and
    simple, so it's useful for sanity testing.
  
    Here's the layout of the graph:
  
    (fingerprint_input)
            v
        [MatMul]<-(weights)
            v
        [BiasAdd]<-(bias)
            v
  
    Args:
      fingerprint_input: TensorFlow node that will output audio feature vectors.
      model_settings: Dictionary of information about the model.
      is_training: Whether the model is going to be used for training.
  
    Returns:
      TensorFlow node outputting logits results, and optionally a dropout
      placeholder.
docstring_summary: Builds a model with a single hidden fully-connected layer.
docstring_tokens: Builds a model with a single hidden fully - connected layer .
function: def create_single_fc_model(fingerprint_input, model_settings, is_training):
    """Builds a model with a single hidden fully-connected layer.
  
    This is a very simple model with just one matmul and bias layer. As you'd
    expect, it doesn't produce very accurate results, but it is very fast and
    simple, so it's useful for sanity testing.
  
    Here's the layout of the graph:
  
    (fingerprint_input)
            v
        [MatMul]<-(weights)
            v
        [BiasAdd]<-(bias)
            v
  
    Args:
      fingerprint_input: TensorFlow node that will output audio feature vectors.
      model_settings: Dictionary of information about the model.
      is_training: Whether the model is going to be used for training.
  
    Returns:
      TensorFlow node outputting logits results, and optionally a dropout
      placeholder.
    """
    if is_training:
      dropout_prob = tf.placeholder(tf.float32, name='dropout_prob')
    fingerprint_size = model_settings['fingerprint_size']
    label_count = model_settings['label_count']
    weights = tf.Variable(
        tf.truncated_normal([fingerprint_size, label_count], stddev=0.001))
    bias = tf.Variable(tf.zeros([label_count]))
    logits = tf.matmul(fingerprint_input, weights) + bias
    if is_training:
      return logits, dropout_prob
    else:
      return logits
function_tokens: def create_single_fc_model ( fingerprint_input , model_settings , is_training ) : if is_training : dropout_prob = tf . placeholder ( tf . float32 , name = 'dropout_prob' ) fingerprint_size = model_settings [ 'fingerprint_size' ] label_count = model_settings [ 'label_count' ] weights = tf . Variable ( tf . truncated_normal ( [ fingerprint_size , label_count ] , stddev = 0.001 ) ) bias = tf . Variable ( tf . zeros ( [ label_count ] ) ) logits = tf . matmul ( fingerprint_input , weights ) + bias if is_training : return logits , dropout_prob else : return logits