python神经网络训练效果差_为什么我的神经网络验证精度比我的训练精度高，并且两者都变得恒定？

我已经建立了一个模型，当我训练它时，我的验证损失小于训练中的一个，验证准确性也比训练中的高。模型是否过拟合？难道我做错了什么？有人可以看看我的模型，看看有什么问题吗？谢谢。

input_text = Input(shape=(200,), dtype='int32', name='input_text')

meta_input = Input(shape=(2,), name='meta_input')

embedding = Embedding(input_dim=len(tokenizer.word_index) + 1,

output_dim=300,

input_length=200)(input_text)

lstm = Bidirectional(LSTM(units=128,

dropout=0.5,

recurrent_dropout=0.5,

return_sequences=True),

merge_mode='concat')(embedding)

pool = GlobalMaxPooling1D()(lstm)

dropout = Dropout(0.5)(pool)

text_output = Dense(n_codes, activation='sigmoid', name='aux_output')(dropout)

output = concatenate([text_output, meta_input])

output = Dense(n_codes, activation='relu')(output)

main_output = Dense(n_codes, activation='softmax', name='main_output')(output)

model = Model(inputs=[input_text,meta_input], outputs=[output])

optimer = Adam(lr=.001)

model.compile(optimizer='adam',

loss='binary_crossentropy',

metrics=['accuracy'])

model.summary()

model.fit([X1_train, X2_train], [y_train],

validation_data=([X1_valid,X2_valid], [y_valid]),

batch_size=64, epochs=20, verbose=1)

这是输出：

__________________________________________________________________________________________________

Layer (type) Output Shape Param # Connected to

==================================================================================================

input_text (InputLayer) [(None, 200)] 0

__________________________________________________________________________________________________

embedding (Embedding) (None, 200, 300) 889500 input_text[0][0]

__________________________________________________________________________________________________

bidirectional (Bidirectional) (None, 200, 256) 439296 embedding[0][0]

__________________________________________________________________________________________________

global_max_pooling1d (GlobalMax (None, 256) 0 bidirectional[0][0]

__________________________________________________________________________________________________

dropout (Dropout) (None, 256) 0 global_max_pooling1d[0][0]

__________________________________________________________________________________________________

aux_output (Dense) (None, 545) 140065 dropout[0][0]

__________________________________________________________________________________________________

meta_input (InputLayer) [(None, 2)] 0

__________________________________________________________________________________________________

concatenate (Concatenate) (None, 547) 0 aux_output[0][0]

meta_input[0][0]

__________________________________________________________________________________________________

dense (Dense) (None, 545) 298660 concatenate[0][0]

==================================================================================================

Total params: 1,767,521

Trainable params: 1,767,521

Non-trainable params: 0

__________________________________________________________________________________________________

Train on 11416 samples, validate on 2035 samples

Epoch 1/20

11416/11416 [==============================] - 158s 14ms/sample - loss: 0.0955 - accuracy: 0.9929 -