def create_model(self, model_input, vocab_size, l2_penalty=1e-08, **unused_params):
    "Creates a logistic model.\n\n    Args:\n      model_input: 'batch' x 'num_features' matrix of input features.\n      vocab_size: The number of classes in the dataset.\n\n    Returns:\n      A dictionary with a tensor containing the probability predictions of the\n      model in the 'predictions' key. The dimensions of the tensor are\n      batch_size x num_classes.\n    "
    output = slim.fully_connected(model_input, vocab_size, activation_fn=tf.nn.sigmoid, weights_regularizer=slim.l2_regularizer(l2_penalty))
    return {'predictions': output}
Creates a logistic model.

Args:
  model_input: 'batch' x 'num_features' matrix of input features.
  vocab_size: The number of classes in the dataset.

Returns:
  A dictionary with a tensor containing the probability predictions of the
  model in the 'predictions' key. The dimensions of the tensor are
  batch_size x num_classes.
def create_model(self, model_input, vocab_size, l2_penalty=1e-08, **unused_params):
    "Creates a logistic model.\n\n    Args:\n      model_input: 'batch' x 'num_features' matrix of input features.\n      vocab_size: The number of classes in the dataset.\n\n    Returns:\n      A dictionary with a tensor containing the probability predictions of the\n      model in the 'predictions' key. The dimensions of the tensor are\n      batch_size x num_classes.\n    "
    output = slim.fully_connected(model_input, vocab_size, activation_fn=tf.nn.sigmoid, weights_regularizer=slim.l2_regularizer(l2_penalty))
    return {'predictions': output}