我使用doc2vec特征向量来训练MLP，但是训练MLP始终等于每个时期0。 MLP模型是：

def MySimpleMLP(X_train=None):
    lengths = sorted([len(X) for X in X_train])
    percentile = 0.90
    seq_cutoff = lengths[int(len(lengths) * percentile)]

    vocab = 50
    EMBEDDING_DIM = 50

    N = 256
    size = 3

    seq_indices = Input(shape=(seq_cutoff,),name='seq_input')
    seq_embedded = Embedding(input_dim=vocab + 1,output_dim=EMBEDDING_DIM,input_length=seq_cutoff)(seq_indices)
    seq_conv = Conv1D(N,size,activation='relu')(Dropout(0.2)(seq_embedded))
    max_conv = GlobalMaxPooling1D()(seq_conv)
    hidden_repr = Dense(N,activation='relu')(max_conv)
    sentiment = Dense(1,activation='sigmoid')(Dropout(0.2)(hidden_repr))

    model = Model(inputs=[seq_indices],outputs=[sentiment])

    model.summary()
    model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
    return model

培训数据为：

Doc2VecArrayFilePath = "../data/mpk/tools/Doc2VecArray.pkl"
with open(Doc2VecArrayFilePath,"rb+") as f:
    X_train,Y_train = pickle.load(f)

X_train = [25000,50]

 [[ 1.54979062  0.99996233  0.10931063 ... -1.12303877 -1.30322146
  -0.57274193]
 [ 0.90919989 -1.39264524 -1.69380188 ... -0.35270166  1.00891471
   1.25304925]
 [-0.66494519  0.76236057  1.37783039 ...  0.69574219  1.99134898
  -0.38097638]
 ...
 [ 1.08792138  0.00841406 -0.27354664 ... -0.18176237  0.76443428
   0.67993295]
 [ 0.78027207 -0.80181849 -1.21321726 ... -0.14031847  0.55475223
  -0.01875231]
 [ 0.59591568 -0.57823026 -0.91873246 ... -0.22376266  1.16658998
  -0.02456926]]
25000
50

MLP培训结果是：

Epoch 2/10
 - 2s - loss: 0.6586 - accuracy: 0.6250 - val_loss: 0.9066 - val_accuracy: 0.0000e+00
Epoch 3/10
 - 2s - loss: 0.6588 - accuracy: 0.6250 - val_loss: 0.8222 - val_accuracy: 0.0000e+00
Epoch 4/10
 - 2s - loss: 0.6582 - accuracy: 0.6250 - val_loss: 0.9356 - val_accuracy: 0.0000e+00
Epoch 5/10
 - 2s - loss: 0.6563 - accuracy: 0.6250 - val_loss: 0.8692 - val_accuracy: 0.0000e+00

其余代码为：

mlp = MySimpleMLP(X_train=X_train)
mlp.fit(np.array(X_train,dtype='int32'),Y_train,validation_split=0.2,epochs=10,batch_size=64,verbose=2)

请问如何修改MLP模型以接受具有良好准确性的doc2vec输入？