我正在嘗試在TensorFlow 2.0中用GP實現WGAN。要計算梯度損失，您需要計算與輸入圖像相關的預測的梯度。現在，為了使它更易于處理，它不是計算相對于所有輸入圖像的預測梯度，而是沿著原始和假數據點的線計算插值數據點，并將其用作輸入。為了實現這一點，我首先開發(fā)了一個函數，它將進行一些預測并返回相對于某些輸入圖像的梯度。首先，我想過這樣做，但它在急切模式下不起作用。因此，我現在正試圖使用.compute_gradientstf.keras.backend.gradientsGradientTape以下是我用來測試內容的代碼：from tensorflow.keras import backend as Kfrom tensorflow.keras.layers import *from tensorflow.keras.models import *import tensorflow as tfimport numpy as np# Comes from Generative Deep Learning by David Fosterclass RandomWeightedAverage(tf.keras.layers.Layer):    def __init__(self, batch_size):        super().__init__()        self.batch_size = batch_size    """Provides a (random) weighted average between real and generated image samples"""    def call(self, inputs):        alpha = K.random_uniform((self.batch_size, 1, 1, 1))        return (alpha * inputs[0]) + ((1 - alpha) * inputs[1])# Dummy criticdef make_critic():    critic = Sequential()    inputShape = (28, 28, 1)    critic.add(Conv2D(32, (5, 5), padding="same", strides=(2, 2),        input_shape=inputShape))    critic.add(LeakyReLU(alpha=0.2))    critic.add(Conv2D(64, (5, 5), padding="same", strides=(2, 2)))    critic.add(LeakyReLU(alpha=0.2))    critic.add(Flatten())    critic.add(Dense(512))    critic.add(LeakyReLU(alpha=0.2))    critic.add(Dropout(0.3))    critic.add(Dense(1))    return critic# Gather dataset((X_train, _), (X_test, _)) = tf.keras.datasets.fashion_mnist.load_data()X_train = X_train.reshape(-1, 28, 28, 1)X_test = X_test.reshape(-1, 28, 28, 1)# Note that I am using test images as fake images for testing purposesinterpolated_img = RandomWeightedAverage(32)([X_train[0:32].astype("float"), X_test[32:64].astype("float")])# Compute gradients of the predictions with respect to the interpolated imagescritic = make_critic()with tf.GradientTape() as tape:    y_pred = critic(interpolated_img)gradients = tape.gradient(y_pred, interpolated_img)漸變即將成為 。我在這里錯過了什么嗎？None