做的8推1的时序预测，但是不管怎么修改模型，一直都是过拟合的状态，loss下降 val loss上升，val loss一点下降趋势也没有，直接往上走，
已经用了dropout(0.5)也试过用BatchNormalization()，但都没办法完全改善
想知道到底问题出在哪里
训练集和测试集（x，y）大小是(80000, 10, 8) (25000, 10, 8) (80000,) (25000,)
#数据整理
train = values[0:timestep*num]
valid =values[timestep*num:]
time_stamp = 10
scaled_data = scaler.fit_transform(train)
x_train, y_train = [], []
for i in range(time_stamp, len(train)):
x_train.append(scaled_data[i - time_stamp:i,0:8])
y_train.append(scaled_data[i, 8])
# y_train.append(scaled_data[i - time_stamp, 8])
# y_train.append(scaled_data[i - time_stamp:i, 8])
x_train, y_train = np.array(x_train), np.array(y_train)
scaled_data = scaler.fit_transform(valid)
x_valid, y_valid = [], []
for i in range(time_stamp, len(valid)):
x_valid.append(scaled_data[i - time_stamp:i,0:8])
y_valid.append(scaled_data[i, 8])
# y_valid.append(scaled_data[i - time_stamp, 8])
# y_valid.append(scaled_data[i - time_stamp:i, 8])
x_valid, y_valid = np.array(x_valid), np.array(y_valid)
print(x_train.shape,x_valid.shape, y_train.shape, y_valid.shape)
#lstm模型
epochs = 60
batch_size = 256
model = Sequential()
model.add(LSTM(units=128, return_sequences=True, input_dim=x_train.shape[-1], input_length=x_train.shape[1]))
model.add(Dropout(0.5))
model.add(LSTM(units=64))
model.add(Dropout(0.5))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='rmsprop')
history =model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size,validation_data=(x_valid,y_valid), verbose=2)
上图中蓝色为loss曲线，黄色为val loss
