model.py 文件源码

def VAELoss(x, x_decoded_mean):
    # NOTE: binary_crossentropy expects a batchSize by dim
    # for x and x_decoded_mean, so we MUST flatten these!
    x = K.flatten(x)
    x_decoded_mean = K.flatten(x_decoded_mean)
    xent_loss = imageSize * imageSize * objectives.binary_crossentropy(x, x_decoded_mean)
    kl_loss = - 0.5 * K.mean(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
    return xent_loss + kl_loss

# Convolutional models