首页定义一个损失函数用于一维Wasserstein AutoEncoders代码keras

定义一个损失函数用于一维Wasserstein AutoEncoders代码keras

时间: 2023-11-22 16:53:33 浏览: 48

Wasserstein AutoEncoders使用的损失函数是Wasserstein距离。一维Wasserstein AutoEncoders的代码如下： ```python from keras.models import Model from keras.layers import Input, Dense, Lambda from keras import backend as K from keras.optimizers import Adam import numpy as np def wasserstein_loss(y_true, y_pred): return K.mean(y_true * y_pred) def sampling(args): z_mean, z_log_var = args epsilon = K.random_normal(shape=(K.shape(z_mean)[0], 1), mean=0., stddev=1.) return z_mean + K.exp(z_log_var / 2) * epsilon def autoencoder(input_dim, latent_dim): inputs = Input(shape=(input_dim,)) encoded = Dense(128, activation='relu')(inputs) encoded = Dense(64, activation='relu')(encoded) z_mean = Dense(latent_dim)(encoded) z_log_var = Dense(latent_dim)(encoded) z = Lambda(sampling)([z_mean, z_log_var]) decoded = Dense(64, activation='relu')(z) decoded = Dense(128, activation='relu')(decoded) outputs = Dense(input_dim, activation='linear')(decoded) encoder = Model(inputs, [z_mean, z_log_var]) autoencoder = Model(inputs, outputs) def kl_loss(y_true, y_pred): kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var) kl_loss = -0.5 * K.sum(kl_loss, axis=-1) return kl_loss autoencoder.compile(optimizer=Adam(lr=0.0002), loss=[wasserstein_loss, kl_loss]) return autoencoder, encoder ``` 在这个代码中，我们使用了一个自定义的损失函数`wasserstein_loss`。这个损失函数可以直接用于优化Wasserstein AutoEncoders的参数。我们还定义了一个`kl_loss`函数，用于计算KL散度的损失。这个函数将被用于训练Encoder部分的参数。

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