資料筆數約500筆
時間序列的資料，分成0，1兩類
想透過LSTM分類如下，
想請問為什麼最終訓練出的模型都把資料分為1類
但0完全沒預測出任何一筆，想請問怎麼優化
麻煩了

n_steps = 3
n_features = 10
model = Sequential()

model.add(Embedding(input_dim=20, output_dim=128))
model.add(LSTM(256, activation='relu', input_shape=(n_steps, n_features)))
model.add(Dense(128,activation="relu",name="FC2"))
model.add(Dense(64,activation="relu",name="FC3"))
model.add(Dense(32,activation="relu",name="FC4"))
model.add(Dense(1, activation='sigmoid'))
model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_1 (Embedding)      (None, None, 128)         2560      
_________________________________________________________________
lstm_1 (LSTM)                (None, 256)               394240    
_________________________________________________________________
FC2 (Dense)                  (None, 128)               32896     
_________________________________________________________________
FC3 (Dense)                  (None, 64)                8256      
_________________________________________________________________
FC4 (Dense)                  (None, 32)                2080      
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 33        
=================================================================
Total params: 440,065
Trainable params: 440,065
Non-trainable params: 0

model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy',f1_m,precision_m, recall_m])
hist = model.fit(X_train, y_train, batch_size=16, epochs=20, verbose=0)

![]

              precision    recall  f1-score   support

           0       0.00      0.00      0.00        17
           1       0.69      1.00      0.82        38

    accuracy                           0.69        55
   macro avg       0.35      0.50      0.41        55
weighted avg       0.48      0.69      0.56        55