katy

losses_keras2.py

def correlation_coefficient(y_true, y_pred):
    '''
    This function is for singleduration model.
    '''
    max_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.max(K.max(y_pred, axis=1), axis=1), axis=1),
                                                                   shape_r_out, axis=1), axis=2), shape_c_out, axis=2)
    y_pred /= max_y_pred

    sum_y_true = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_true, axis=1), axis=1), axis=1),
                                                                   shape_r_out, axis=1), axis=2), shape_c_out, axis=2)
    sum_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_pred, axis=1), axis=1), axis=1),
                                                                   shape_r_out, axis=1), axis=2), shape_c_out, axis=2)
    y_true /= (sum_y_true + K.epsilon())
    y_pred /= (sum_y_pred + K.epsilon())

    N = shape_r_out * shape_c_out
    sum_xy = K.sum(K.sum(y_true * y_pred, axis=1), axis=1) / N
    sum_x = K.sum(K.sum(y_true, axis=1), axis=1)
    sum_y = K.sum(K.sum(y_pred, axis=1), axis=1)
    sum_x2 = K.sum(K.sum(K.square(y_true), axis=1), axis=1)
    sum_y2 = K.sum(K.sum(K.square(y_pred), axis=1), axis=1)

    num = sum_xy - sum_x * sum_y
    den = K.sqrt(sum_x2 - K.square(sum_x)) * K.sqrt(sum_y2 - K.square(sum_y))

    return num / den