将自定义损失函数从Numpy函数转换为TensorFlow函数
- np.where => tf.where 函数直接替换
- np.sum => K.sum
- 0.0 => tf.zeros_like(u)
- x_in => K.cast(x, “float32”) 先转换数据格式
- x_out => x.eval(session=tf.Session())
- s => s*tf.ones_like(u) 常数在tf中需要转换成相同大小的矩阵张sjgs量
转换前原始的计算方法
def pinball_loss_trucated_np(y_true, y_pred, quantile, s=0.5):
u = y_true - y_pred
pl_1 = np.where(u>=0, u, 0.0)
pl_2 = np.where(u<=-s, s*quantile, 0.0)
pl_3 = np.where((u>-s) & (u<0), -u*quantile, 0.0)
return np.sum(np.mean(pl_1+pl_2+pl_3, axis=0))
def pinball_loss_huberized_np(y_true, y_pred, quantile, sigma=0.5):
u = y_true - y_pred
pl_1 = np.where(u<1-sigma, 1 -u-sigma/2, 0.0)
pl_2 = np.where((u>=1-sigma) & (u<1), (1-u)**2/sigma/2, 0.0)
pl_3 = np.where((u>=1) & (u<1+sigma), (1-u)**2*quantile/2/sigma, 0.0)
pl_4 = np.where(u>=1+sigma, -quantile*(1-u+sigma/2), 0.0)
return np.sum(np.mean(pl_1+pl_2+pl_3+pl_4, axis=0))
转换后
def pinball_loss_trucated_tf(y_true, y_pred, quantile, s=0.1):
y_true = K.cast(y_true, "float32")
y_pred = K.cast(y_pred, "float32")
quantile = K.cast(quantile, "float32")
s = K.cast(s, "float32")
u = y_true - y_pred
pl_1 = tf.where(u>=0, u, tf.zeros_like(u))
pl_3 = tf.where((u>-s) & (u<0), -u*quantile, tf.zeros_like(u))
pl_2 = tf.where(u<=-s, s*quantile*tf.ones_like(u), tf.zeros_like(u))
return K.sum(K.mean(pl_1+pl_2+pl_3, axis=0))
plt = pinball_loss_trucated_tf(y_test, y_pred, quantiles).eval(session=sess)
def pinball_loss_huberized_tf(y_true, y_pred, quantile, sigma=0.5):
y_true = K.cast(y_true, "float32")
y_pred = K.cast(y_pred, "float32")
u = y_true - y_pred
pl_1 = tf.where(u<1-sigma, 1-u-sigma/2, tf.zeros_like(u))
pl_2 = tf.where((u>=1-sigma) & (u<1), (1-u)**2/sigma/2, tf.zeros_like(u))
pl_3 = tf.where((u>=1) & (u<1+sigma), (1-u)**2*quantile/2/sigma, tf.zeros_like(u))
pl_4 = tf.where(u>=1+sigma, -quantile*(1-u+sigma/2), tf.zeros_like(u))
return K.sum(K.mean(tf.add(pl_1, pl_2)+tf.add(pl_3, pl_4), axis=0))
导入损失函数：model.compile(loss = [lambda y_true, y_pred: self.loss(y_true, y_pred, self.quantiles)], optimizer = "adam")
