我对keras非常陌生，我正在关注此doc以生成多输入和多输出模型。但是，在每个时代之后，结果保持不变。有人能指出我被困在哪里吗？
我的代码就像
main_input = Input(shape = (maxlen, ), name="main_input")
x = Embedding(94, 64)(main_input) # dic length = 94
lstm_out0 = LSTM(256, activation="relu", dropout=0.1,
recurrent_dropout=0.2, return_sequences=True)(x)
lstm_out = LSTM(256, activation="relu", dropout=0.1, recurrent_dropout=0.2)(lstm_out0)
auxiliary_input = Input(shape=(maxlen,), dtype="int32", name='aux_input')
aux_embed = Embedding(94, 64)(auxiliary_input)
aux_lstm_out = LSTM(256, activation="relu", dropout=0.2, recurrent_dropout=0.2)(aux_embed)
auxiliary_output = Dense(10, activation="softmax", name="aux_output")(lstm_out)
x = keras.layers.concatenate([aux_lstm_out, lstm_out])
x = Dense(64, activation='relu')(x)
main_output = Dense(1, activation='sigmoid', name='main_output')(x)
model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output, auxiliary_output])
model.compile(optimizer='rmsprop', loss={'main_output': 'binary_crossentropy', 'aux_output': 'categorical_crossentropy'},metrics=['accuracy'])
model.fit([X_train, X_aux_train], [train_label, aux_train_label],
validation_data=[[X_dev, X_aux_dev], [dev_label,aux_dev_label]],
epochs=10, batch_size=batch_size)
主输入是一系列字符，而主输出是二进制值。辅助输入也是一系列字符，而辅助输出是一个分类标签。
输出类似于
Train on 200000 samples, validate on 20000 samples
Epoch 1/10
200000/200000 [==============================] - 892s - loss: 7.3824 - main_output_loss: 5.8560 - aux_output_loss: 1.5264 - main_output_acc: 0.5186 - aux_output_acc: 0.5371 - val_loss: 9.5776 - val_main_output_loss: 8.0590 - val_aux_output_loss: 1.5186 - val_main_output_acc: 0.5000 - val_aux_output_acc: 0.5362
Epoch 2/10
200000/200000 [==============================] - 894s - loss: 9.5818 - main_output_loss: 8.0586 - aux_output_loss: 1.5233 - main_output_acc: 0.5000 - aux_output_acc: 0.5372 - val_loss: 9.5771 - val_main_output_loss: 8.0590 - val_aux_output_loss: 1.5181 - val_main_output_acc: 0.5000 - val_aux_output_acc: 0.5362
我跑了＆gt; 5个时代和结果几乎都是一样的。输入数据通过以下功能准备：sequence.pad_sequences标签：to_categorical（用于多类）