loss 加权_keras 自定义loss损失函数,sample在loss上的加权和metric详解

首先辨析一下概念:

1. loss是整体网络进行优化的目标, 是需要参与到优化运算,更新权值W的过程的

2. metric只是作为评价网络表现的一种“指标”, 比如accuracy,是为了直观地了解算法的效果,充当view的作用,并不参与到优化过程

在keras中实现自定义loss, 可以有两种方式,一种自定义 loss function,

例如:

# 方式一

def vae_loss(x, x_decoded_mean):

xent_loss = objectives.binary_crossentropy(x, x_decoded_mean)

kl_loss = - 0.5 * K.mean(1 + z_log_sigma - K.square(z_mean) - K.exp(z_log_sigma), axis=-1)

return xent_loss + kl_loss

vae.compile(optimizer="rmsprop", loss=vae_loss)

或者通过自定义一个keras的层(layer)来达到目的, 作为model的最后一层,最后令model.compile中的loss=None:

# 方式二

# Custom loss layer

class CustomVariationalLayer(Layer):

def __init__(self, **kwargs):

self.is_placeholder = True

super(CustomVariationalLayer, self).__init__(**kwargs)

def vae_loss(self, x, x_decoded_mean_squash):

x = K.flatten(x)

x_decoded_mean_squash = K.flatten(x_decoded_mean_squash)

xent_loss = img_rows * img_cols * metrics.binary_crossentropy(x, x_decoded_mean_squash)

kl_loss = - 0.5 * K.mean(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)

return K.mean(xent_loss + kl_loss)

def call(self, inputs):

x = inputs[0]

x_decoded_mean_squash = inputs[1]

loss = self.vae_loss(x, x_decoded_mean_squash)

self.add_loss(loss, inputs=inputs)

# We don"t use this output.

return x

y = CustomVariationalLayer()([x, x_decoded_mean_squash])

vae = Model(x, y)

vae.compile(optimizer="rmsprop", loss=None)

在keras中自定义metric非常简单,需要用y_pred和y_true作为自定义metric函数的输入参数 点击查看metric的设置

注意事项:

1. keras中定义loss,返回的是batch_size长度的tensor, 而不是像tensorflow中那样是一个scalar

2. 为了能够将自定义的loss保存到model, 以及可以之后能够顺利load model, 需要把自定义的loss拷贝到keras.losses.py 源代码文件下,否则运行时找不到相关信息,keras会报错

有时需要不同的sample的loss施加不同的权重,这时需要用到sample_weight,例如

# Class weights:

# To balance the difference in occurences of digit class labels.

# 50% of labels that the discriminator trains on are "fake".

# Weight = 1 / frequency

cw1 = {0: 1, 1: 1}

cw2 = {i: self.num_classes / half_batch for i in range(self.num_classes)}

cw2[self.num_classes] = 1 / half_batch

class_weights = [cw1, cw2] # 使得两种loss能够一样重要

discriminator.train_on_batch(imgs, [valid, labels], class_weight=class_weights)

补充知识:keras模型训练与保存的call_back的设置

1、模型训练

fit(x=None,

y=None,

你可能感兴趣的:(loss,加权)