Tensorflow 如何在keras贴合过程中平衡重量损失？

我的模型：

class MultiAutoencoder:
def decoder_category_branch(encoder_GRU):
    decoded = RepeatVector(max_len_str, name = 'bottleneck-2')(encoder_GRU)
    decoder_BLSTM = Bidirectional(LSTM(25, return_sequences=True, name='Decoder-BLSTM'))(decoded)
    decoder_drop = Dropout(0.1)(decoder_BLSTM)
    decoder_GRU = GRU(100, activation='tanh', return_sequences=False, name='Decoder-GRU')(decoder_drop)
 
    decoder_output = Dense(max_len_str, activation='softmax', name='decoder-output')(decoder_GRU)

    return decoder_output

def encoder_binary_branch(emb_norm):
    encoder_BLSTM = Bidirectional(LSTM(25, activation='tanh', return_sequences=True, name='Encoder-LSTM'))(emb_norm)
    encoder_drop = Dropout(0.1)(encoder_BLSTM)
    encoder_GRU = GRU(25, activation='tanh', return_sequences=False, name = 'bottleneck-1')(encoder_drop)
    encoder_dense_1 = Dense(20, activation='relu')(encoder_GRU)
    encoder_dense_2 = Dense(10, activation='relu')(encoder_dense_1)
    encoder_dense_3 = Dense(5, activation='relu')(encoder_dense_2)
    encoder_output = Dense(1, activation='sigmoid', name = 'encoder-output')(encoder_dense_3)
    return encoder_GRU, encoder_output

def build():
    encoder_inputs = Input(shape=(max_len_str,), name='Encoder-Input')
    emb_layer = Embedding(input_dim=len(word_index)+1, output_dim=100, input_length = max_len_str, name='Embedding', mask_zero=False)(encoder_inputs)
    emb_drop = SpatialDropout1D(0.1)(emb_layer)
    emb_norm = BatchNormalization()(emb_drop)
    encoder_GRU, encoder_output = MultiAutoencoder.encoder_binary_branch(emb_norm)
    decoder_output = MultiAutoencoder.decoder_category_branch(encoder_GRU)


    model = Model(inputs=encoder_inputs, outputs=[encoder_output, decoder_output], name="MultiAutoencoder")
    return model

和损失：

losses = {
"decoder-output": "categorical_crossentropy",
"encoder-output": "binary_crossentropy",
}

loss_weights = {
    'decoder-output': 0.001,
    'encoder-output': 70 
}

metrics={'decoder-output': [keras.metrics.CategoricalAccuracy()],
        'encoder-output': [keras.metrics.BinaryAccuracy()]}

我的目标是提高编码器的准确性。但在学习过程中，解码器输出的损失增加：

Epoch 23/30
169/169 [==============================] - 55s 327ms/step - loss: 29.3175 - encoder-output_loss: 0.0190 - decoder-output_loss: 27989.1639 - encoder-output_binary_accuracy: 0.9970 - decoder-output_categorical_accuracy: 0.0451 - val_loss: 30.8555 - val_encoder-output_loss: 0.0358 - val_decoder-output_loss: 28350.3809 - val_encoder-output_binary_accuracy: 0.9937 - val_decoder-output_categorical_accuracy: 0.0407
Epoch 24/30
169/169 [==============================] - 55s 326ms/step - loss: 29.9731 - encoder-output_loss: 0.0238 - decoder-output_loss: 28307.4726 - encoder-output_binary_accuracy: 0.9959 - decoder-output_categorical_accuracy: 0.0464 - val_loss: 31.3778 - val_encoder-output_loss: 0.0356 - val_decoder-output_loss: 28886.6445 - val_encoder-output_binary_accuracy: 0.9937 - val_decoder-output_categorical_accuracy: 0.0407
Epoch 25/30
169/169 [==============================] - 56s 329ms/step - loss: 30.5244 - encoder-output_loss: 0.0222 - decoder-output_loss: 28972.7803 - encoder-output_binary_accuracy: 0.9964 - decoder-output_categorical_accuracy: 0.0448 - val_loss: 31.9176 - val_encoder-output_loss: 0.0355 - val_decoder-output_loss: 29430.2871 - val_encoder-output_binary_accuracy: 0.9936 - val_decoder-output_categorical_accuracy: 0.0407
Epoch 26/30
169/169 [==============================] - 56s 333ms/step - loss: 31.2009 - encoder-output_loss: 0.0236 - decoder-output_loss: 29546.3366 - encoder-output_binary_accuracy: 0.9960 - decoder-output_categorical_accuracy: 0.0465 - val_loss: 32.3901 - val_encoder-output_loss: 0.0353 - val_decoder-output_loss: 29916.2539 - val_encoder-output_binary_accuracy: 0.9938 - val_decoder-output_categorical_accuracy: 0.0407
Epoch 27/30
169/169 [==============================] - 56s 330ms/step - loss: 31.4490 - encoder-output_loss: 0.0227 - decoder-output_loss: 29856.7499 - encoder-output_binary_accuracy: 0.9961 - decoder-output_categorical_accuracy: 0.0442 - val_loss: 32.9726 - val_encoder-output_loss: 0.0352 - val_decoder-output_loss: 30508.6621 - val_encoder-output_binary_accuracy: 0.9935 - val_decoder-output_categorical_accuracy: 0.0407
Epoch 28/30
169/169 [==============================] - 56s 330ms/step - loss: 32.5321 - encoder-output_loss: 0.0212 - decoder-output_loss: 31044.8867 - encoder-output_binary_accuracy: 0.9966 - decoder-output_categorical_accuracy: 0.0457 - val_loss: 33.4694 - val_encoder-output_loss: 0.0344 - val_decoder-output_loss: 31062.6191 - val_encoder-output_binary_accuracy: 0.9938 - val_decoder-output_categorical_accuracy: 0.0407
Epoch 29/30
169/169 [==============================] - 56s 332ms/step - loss: 33.0040 - encoder-output_loss: 0.0235 - decoder-output_loss: 31359.5384 - encoder-output_binary_accuracy: 0.9961 - decoder-output_categorical_accuracy: 0.0454 - val_loss: 33.9589 - val_encoder-output_loss: 0.0347 - val_decoder-output_loss: 31532.6406 - val_encoder-output_binary_accuracy: 0.9936 - val_decoder-output_categorical_accuracy: 0.0407
Epoch 30/30
169/169 [==============================] - 56s 330ms/step - loss: 33.4066 - encoder-output_loss: 0.0252 - decoder-output_loss: 31645.2180 - encoder-output_binary_accuracy: 0.9956 - decoder-output_categorical_accuracy: 0.0443 - val_loss: 34.6286 - val_encoder-output_loss: 0.0355 - val_decoder-output_loss: 32143.4551 - val_encoder-output_binary_accuracy: 0.9936 - val_decoder-output_categorical_accuracy: 0.0407

如何在训练期间校准减重？请帮我完成代码