Machine learning keras fit解码器，然后安装自动编码器

我有一个模型：

class MultiAutoencoder:
def decoder_category_brunch(encoder_dense_2):
    decoded = RepeatVector(max_len_str, name = 'bottleneck-2')(encoder_dense_2)
    decoder_LSTM_1 = LSTM(25, activation='tanh', return_sequences=True)(decoded)
    decoder_LSTM_2 = LSTM(50, activation='tanh', return_sequences=True)(decoder_LSTM_1)
    decoder_GRU = GRU(200, activation='tanh', return_sequences=True, name='Decoder-GRU')(decoder_LSTM_2)
    decoder_drop = Dropout(0.1)(decoder_GRU)
    decoder_LSTM_3 = Bidirectional(LSTM(150, return_sequences=False, name='Decoder-LSTM-2'))(decoder_drop)
    decoder_output = Dense(max_len_str, activation='softmax', name='decoder-output')(decoder_LSTM_3)

    return decoder_output

def encoder_binary_brunch(emb_norm):
    encoder_LSTM = Bidirectional(LSTM(100, activation='tanh', return_sequences=True, name='Encoder-LSTM'))(emb_norm)
    encoder_drop = Dropout(0.1)(encoder_LSTM)
    encoder_GRU = GRU(50, activation='tanh', return_sequences=False, name = 'bottleneck-1')(encoder_drop)
    encoder_dense_1 = Dense(25, activation='relu')(encoder_GRU)
    encoder_dense_2 = Dense(5, activation='relu')(encoder_dense_1)
    encoder_output = Dense(1, activation='softmax', name = 'encoder-output')(encoder_dense_2)
    return encoder_dense_2, 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=300, 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_dense_2, encoder_output = MultiAutoencoder.encoder_binary_brunch(emb_norm)
    decoder_output = MultiAutoencoder.decoder_category_brunch(encoder_dense_2)


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

一般资料：

 __________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
Encoder-Input (InputLayer)      [(None, 699)]        0                                            
__________________________________________________________________________________________________
Embedding (Embedding)           (None, 699, 300)     36300       Encoder-Input[0][0]              
__________________________________________________________________________________________________
spatial_dropout1d (SpatialDropo (None, 699, 300)     0           Embedding[0][0]                  
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 699, 300)     1200        spatial_dropout1d[0][0]          
__________________________________________________________________________________________________
bidirectional (Bidirectional)   (None, 699, 200)     320800      batch_normalization[0][0]        
__________________________________________________________________________________________________
dropout (Dropout)               (None, 699, 200)     0           bidirectional[0][0]              
__________________________________________________________________________________________________
bottleneck-1 (GRU)              (None, 50)           37800       dropout[0][0]                    
__________________________________________________________________________________________________
dense (Dense)                   (None, 25)           1275        bottleneck-1[0][0]               
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 5)            130         dense[0][0]                      
__________________________________________________________________________________________________
bottleneck-2 (RepeatVector)     (None, 699, 5)       0           dense_1[0][0]                    
__________________________________________________________________________________________________
lstm (LSTM)                     (None, 699, 25)      3100        bottleneck-2[0][0]               
__________________________________________________________________________________________________
lstm_1 (LSTM)                   (None, 699, 50)      15200       lstm[0][0]                       
__________________________________________________________________________________________________
Decoder-GRU (GRU)               (None, 699, 200)     151200      lstm_1[0][0]                     
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 699, 200)     0           Decoder-GRU[0][0]                
__________________________________________________________________________________________________
bidirectional_1 (Bidirectional) (None, 300)          421200      dropout_1[0][0]                  
__________________________________________________________________________________________________
encoder-output (Dense)          (None, 1)            6           dense_1[0][0]                    
__________________________________________________________________________________________________
decoder-output (Dense)          (None, 699)          210399      bidirectional_1[0][0]            
==================================================================================================
Total params: 1,198,610
Trainable params: 1,198,010
Non-trainable params: 600

我想在类和解码器产生的内容（潜在表示）上训练编码器模型

当我训练模型时，解码器损耗不断增加：

Epoch 1/100
169/169 [==============================] - 6360s 38s/step - loss: 20740.2245 - encoder-output_loss: 0.8943 - decoder-output_loss: 20739.7780 - encoder-output_categorical_accuracy: 1.0000 - decoder-output_categorical_accuracy: 0.0374 - val_loss: 21028.6387 - val_encoder-output_loss: 0.8073 - val_decoder-output_loss: 21028.2383 - val_encoder-output_categorical_accuracy: 1.0000 - val_decoder-output_categorical_accuracy: 0.0561
Epoch 2/100
 60/169 [=========>....................] - ETA: 1:01:58 - loss: 26872 - encoder-output_loss: 0.6741 - decoder-output_loss: 26872.3450 - encoder-output_categorical_accuracy: 1.0000 - decoder-output_categorical_accuracy: 0.0512
....
loss: 226872 - encoder-output_loss: 0.0741 - decoder-output_loss: 226872.3450 

是否可以在keras中训练模型的一部分（解码器）一段时间，保存，然后用这些训练的部分初始化权重，然后进一步一起训练（编码器-解码器）

Epoch 1/100
169/169 [==============================] - 6360s 38s/step - loss: 20740.2245 - encoder-output_loss: 0.8943 - decoder-output_loss: 20739.7780 - encoder-output_categorical_accuracy: 1.0000 - decoder-output_categorical_accuracy: 0.0374 - val_loss: 21028.6387 - val_encoder-output_loss: 0.8073 - val_decoder-output_loss: 21028.2383 - val_encoder-output_categorical_accuracy: 1.0000 - val_decoder-output_categorical_accuracy: 0.0561
Epoch 2/100
 60/169 [=========>....................] - ETA: 1:01:58 - loss: 26872 - encoder-output_loss: 0.6741 - decoder-output_loss: 26872.3450 - encoder-output_categorical_accuracy: 1.0000 - decoder-output_categorical_accuracy: 0.0512
....
loss: 226872 - encoder-output_loss: 0.0741 - decoder-output_loss: 226872.3450