python - 训练 mlp ， val_accuracy 始终等于 0

Question

我使用 doc2vec 特征向量来训练 MLP，但训练 MLP 总是等于 0 每个 epoch。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

训练数据为：

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训练结果为：

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

其余的代码是：

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)

如何修改 MLP 模型以准确地接受 doc2vec 输入？请帮助。