keras中自定义loss层如何接受输入值的案例

2025-01-20 技术教程 151

小编给大家分享一下keras中自定义loss层如何接受输入值的案例，希望大家阅读完这篇文章后大所收获，下面让我们一起去探讨方法吧！

loss函数如何接受输入值

keras封装的比较厉害，官网给的例子写的云里雾里，

在stackoverflow找到了答案

You can wrap the loss function as a inner function and pass your input tensor to it (as commonly done when passing additional arguments to the loss function).

def custom_loss_wrapper(input_tensor): def custom_loss(y_true, y_pred): return K.binary_crossentropy(y_true, y_pred) + K.mean(input_tensor) return custom_loss

input_tensor = Input(shape=(10,))hidden = Dense(100, activation='relu')(input_tensor)out = Dense(1, activation='sigmoid')(hidden)model = Model(input_tensor, out)model.compile(loss=custom_loss_wrapper(input_tensor), optimizer='adam')

You can verify that input_tensor and the loss value will change as different X is passed to the model.

X = np.random.rand(1000, 10)y = np.random.randint(2, size=1000)model.test_on_batch(X, y) # => 1.1974642X *= 1000model.test_on_batch(X, y) # => 511.15466

fit_generator

fit_generator ultimately calls train_on_batch which allows for x to be a dictionary.

Also, it could be a list, in which casex is expected to map 1:1 to the inputs defined in Model(input=[in1, …], …)

### generatoryield [inputX_1,inputX_2],y### modelmodel = Model(inputs=[inputX_1,inputX_2],outputs=...)

补充知识：keras中自定义 loss损失函数和修改不同样本的loss权重(样本权重、类别权重)

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

一、keras自定义损失函数

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 layerclass 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

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

二、keras中的样本权重

# Importimport numpy as npfrom sklearn.utils import class_weight # Example modelmodel = Sequential()model.add(Dense(32, activation='relu', input_dim=100))model.add(Dense(1, activation='sigmoid')) # Use binary crossentropy lossmodel.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy']) # Calculate the weights for each class so that we can balance the dataweights = class_weight.compute_class_weight('balanced', np.unique(y_train), y_train) # Add the class weights to the training model.fit(x_train, y_train, epochs=10, batch_size=32, class_weight=weights)