nonunit_matrix_regression_model.py 文件源码

def create_model(self, model_input, vocab_size, l2_penalty=1e-8, original_input=None, epsilon=1e-5, **unused_params):
    """Creates a non-unified matrix regression model.

    Args:
      model_input: 'batch' x 'num_features' x 'num_methods' 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."""

    num_features = model_input.get_shape().as_list()[-2]
    num_methods = model_input.get_shape().as_list()[-1]

    log_model_input = tf.stop_gradient(tf.log((epsilon + model_input) / (1.0 + epsilon - model_input)))

    weight = tf.get_variable("ensemble_weight", 
        shape=[num_features, num_methods],
        regularizer=slim.l2_regularizer(l2_penalty))
    weight = tf.nn.softmax(weight)

    output = tf.nn.sigmoid(tf.einsum("ijk,jk->ij", log_model_input, weight))
    return {"predictions": output}