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我创建了一个自动编码器模型来最小化 PAPR 和 BER 指标，同时我对 BER 使用分类交叉熵，对 PAPR 减少使用函数“PAPRLoss”我对形状有问题

def paprLoss(y_true, y_pred):
sigR = tf.math.real(y_pred)
sigI = tf.math.imag(y_pred)
sigR = tf.reshape(sigR,(1,(72*btch)))
sigI = tf.reshape(sigI,(1,(72*btch)))
sigRI = tf.concat((sigR, sigI), 0)
yPower = K.sqrt(K.sum(K.square(sigRI), axis=-1))
yMax = K.max(yPower, axis=-1)
yMean = K.mean(yPower, axis=-1)
yPAPR = 10 * tf.experimental.numpy.log10(yMax/yMean)
return yMax

#generating data of size N
N = 1024000
label = np.random.randint(M,size=N)

# creating one hot encoded vectors
data = []
for i in label:
    temp = np.zeros(M)
    temp[i] = 1
    data.append(temp)    
data = np.array(data)

n_channel=2
R = k/n_channel

input_signal = Input(shape=(M,))
encoded = Dense(10*M, activation='relu')(input_signal)
encoded1 = Dense(10*M, activation='relu')(encoded)
encoded1 = Dense(n_channel, activation='linear')(encoded1)
encoded2=Lambda(lambda x:x / K.sqrt(K.mean(x**2)))(encoded1)#Average Power constraint
encoded3 = Lambda(lambda x: OFDM_mod(x, Mfft, CP))(encoded2)
SNRdB_train = 55
SNR_Linear = 10**(SNRdB_train/10)
encoded51 = Lambda(lambda x: OFDM_demod(x, Mfft, CP))(encoded3)
decoded0 = Dense(10*M,activation='relu')(encoded51)
decoded = Dense(10*M,activation='relu')(decoded0)
decoded1 = Dense(M, activation='softmax')(decoded)

autoencoder= Model(inputs=input_signal, outputs=[decoded1,encoded3], name="PAPRnet_Encoder")

autoencoder.compile(optimizer='adamax', loss=['categorical_crossentropy',paprLoss],loss_weights=[1.0,0.1], metrics=['accuracy'])
print (autoencoder.summary())

#training phase
history=autoencoder.fit(data, [data,data],
                epochs=100,
                batch_size=btch*Mfft,
                validation_freq=1,)

我收到以下错误。y_true 和 y_pred 的形状不相等

Dimensions must be equal, but are 512000 and 8000 for '{{node Equal_1}} = Equal[T=DT_INT64, incompatible_shape_error=true](ArgMax_2, ArgMax_3)' with input shapes: [512000], [8000].
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