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

一维Wasserstein AutoEncoders是一种基于Wasserstein距离的自编码器模型，损失函数需要包括重构误差和Wasserstein距离的计算。以下是一个基于Keras实现的一维Wasserstein AutoEncoders模型的损失函数定义：

import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.losses import binary_crossentropy
from tensorflow.keras.layers import Input, Dense, Lambda
from tensorflow.keras.models import Model

def wasserstein_loss(y_true, y_pred):
    return K.mean(y_true * y_pred)

def get_encoder_decoder(input_dim, encoding_dim):
    # Encoder
    inputs = Input(shape=(input_dim,))
    encoded = Dense(encoding_dim, activation='relu')(inputs)

    # Decoder
    decoded = Dense(input_dim, activation='sigmoid')(encoded)

    # Autoencoder
    autoencoder = Model(inputs, decoded)

    # Encoder only
    encoder = Model(inputs, encoded)

    # Decoder only
    encoded_input = Input(shape=(encoding_dim,))
    decoder_layer = autoencoder.layers[-1]
    decoder = Model(encoded_input, decoder_layer(encoded_input))

    return encoder, decoder, autoencoder

def get_discriminator(encoding_dim):
    # Discriminator
    encoded_input = Input(shape=(encoding_dim,))
    x = Dense(128, activation='relu')(encoded_input)
    x = Dense(128, activation='relu')(x)
    x = Dense(128, activation='relu')(x)
    validity = Dense(1, activation='linear')(x)

    discriminator = Model(encoded_input, validity)

    return discriminator

def get_WAE(input_dim, encoding_dim):
    # Encoder, Decoder and Autoencoder
    encoder, decoder, autoencoder = get_encoder_decoder(input_dim, encoding_dim)

    # Discriminator
    discriminator = get_discriminator(encoding_dim)

    # Input
    input_img = Input(shape=(input_dim,))

    # Encoding
    encoded = encoder(input_img)

    # Decoding
    decoded = decoder(encoded)

    # Wasserstein distance
    z_mean = Lambda(lambda x: K.mean(x, axis=0))(encoded)
    z_var = Lambda(lambda x: K.var(x, axis=0))(encoded)
    epsilon = tf.random.normal(shape=(K.shape(encoded)[0], encoding_dim), mean=0., stddev=1.)
    z_sample = z_mean + K.exp(0.5 * z_var) * epsilon
    validity_real = discriminator(encoded)
    validity_fake = discriminator(z_sample)
    wasserstein_distance = Lambda(lambda x: K.abs(K.mean(x[0]) - K.mean(x[1])))([validity_real, validity_fake])

    # Model
    wae_model = Model(input_img, decoded)

    # Compile discriminator
    discriminator.compile(loss=wasserstein_loss, optimizer='adam')

    # Compile autoencoder
    wae_model.add_loss(wasserstein_distance)
    wae_model.compile(loss='binary_crossentropy', optimizer='adam')

    return encoder, decoder, discriminator, wae_model

在这里，我们使用了Wasserstein距离作为判别器的损失函数，定义了一个自定义的损失函数`wasserstein_loss`。在WAE模型中，我们还需要计算重构误差和Wasserstein距离，因此我们将重构误差添加到自编码器的损失中，并在模型中使用Lambda层计算Wasserstein距离。

注意，在这个实现中，我们使用了一个简单的判别器模型，它只包含几个密集层。您可以根据需要修改它来适应更复杂的数据集和任务。