我制作了带有监督学习的LSTM（RNN）神经网络，用于数据库存预测。问题是为什么它会根据自己的训练数据预测错误？（注意：可复制的示例以下可）
我创建了一个简单的模型来预测未来5天的股价：
model = Sequential()
model.add(LSTM(32, activation='sigmoid', input_shape=(x_train.shape[1], x_train.shape[2])))
model.add(Dense(y_train.shape[1]))
model.compile(optimizer='adam', loss='mse')
es = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)
model.fit(x_train, y_train, batch_size=64, epochs=25, validation_data=(x_test, y_test), callbacks=[es])正确的结果以y_test（5个值）表示，因此对模型进行训练，可以回顾90天的前几天，然后使用以下方法从最佳（val_loss=0.0030）结果中恢复权重patience=3：
Train on 396 samples, validate on 1 samples
Epoch 1/25
396/396 [==============================] - 1s 2ms/step - loss: 0.1322 - val_loss: 0.0299
Epoch 2/25
396/396 [==============================] - 0s 402us/step - loss: 0.0478 - val_loss: 0.0129
Epoch 3/25
396/396 [==============================] - 0s 397us/step - loss: 0.0385 - val_loss: 0.0178
Epoch 4/25
396/396 [==============================] - 0s 399us/step - loss: 0.0398 - val_loss: 0.0078
Epoch 5/25
396/396 [==============================] - 0s 391us/step - loss: 0.0343 - val_loss: 0.0030
Epoch 6/25
396/396 [==============================] - 0s 391us/step - loss: 0.0318 - val_loss: 0.0047
Epoch 7/25
396/396 [==============================] - 0s 389us/step - loss: 0.0308 - val_loss: 0.0043
Epoch 8/25
396/396 [==============================] - 0s 393us/step - loss: 0.0292 - val_loss: 0.0056预测结果非常棒，不是吗？
这是因为算法从＃5时代恢复了最佳权重。Okey，让我们现在将该模型保存到.h5文件中，移回-10天并预测最后5天（在第一个示例中，我们在4月17日至23日（包括休息日）制作了模型并进行了验证，现在让我们在4月2日至8日进行测试）。结果：
它显示了绝对错误的方向。如我们所见，这是因为模型经过训练，最适合验证的时间是＃5纪元，时间是4月17日至23日，而不是2月8日。如果我尝试进行更多的训练，尝试选择哪个纪元，无论我做什么，过去总是会有很多错误的预测时间间隔。
为什么模型在其训练有素的数据上显示错误的结果？我训练数据时，必须记住如何在这组数据上预测数据，但是预测错误。我还尝试了什么：
- 使用行数超过5万，股价20年的大型数据集，添加或多或少的功能
- 创建不同类型的模型，例如添加更多的隐藏层，不同的batch_size，不同的层激活，退出，批量归一化
- 创建自定义的EarlyStopping回调，从许多验证数据集中获取平均val_loss并选择最佳
也许我想念什么？我可以改善什么？
这是非常简单且可重现的示例。yfinance下载S＆P 500股票数据。
"""python 3.7.7
tensorflow 2.1.0
keras 2.3.1"""
import numpy as np
import pandas as pd
from keras.callbacks import EarlyStopping, Callback
from keras.models import Model, Sequential, load_model
from keras.layers import Dense, Dropout, LSTM, BatchNormalization
from sklearn.preprocessing import MinMaxScaler
import plotly.graph_objects as go
import yfinance as yf
np.random.seed(4)
num_prediction = 5
look_back = 90
new_s_h5 = True # change it to False when you created model and want test on other past dates
df = yf.download(tickers="^GSPC", start='2018-05-06', end='2020-04-24', interval="1d")
data = df.filter(['Close', 'High', 'Low', 'Volume'])
# drop last N days to validate saved model on past
df.drop(df.tail(0).index, inplace=True)
print(df)
class EarlyStoppingCust(Callback):
def __init__(self, patience=0, verbose=0, validation_sets=None, restore_best_weights=False):
super(EarlyStoppingCust, self).__init__()
self.patience = patience
self.verbose = verbose
self.wait = 0
self.stopped_epoch = 0
self.restore_best_weights = restore_best_weights
self.best_weights = None
self.validation_sets = validation_sets
def on_train_begin(self, logs=None):
self.wait = 0
self.stopped_epoch = 0
self.best_avg_loss = (np.Inf, 0)
def on_epoch_end(self, epoch, logs=None):
loss_ = 0
for i, validation_set in enumerate(self.validation_sets):
predicted = self.model.predict(validation_set[0])
loss = self.model.evaluate(validation_set[0], validation_set[1], verbose = 0)
loss_ += loss
if self.verbose > 0:
print('val' + str(i + 1) + '_loss: %.5f' % loss)
avg_loss = loss_ / len(self.validation_sets)
print('avg_loss: %.5f' % avg_loss)
if self.best_avg_loss[0] > avg_loss:
self.best_avg_loss = (avg_loss, epoch + 1)
self.wait = 0
if self.restore_best_weights:
print('new best epoch = %d' % (epoch + 1))
self.best_weights = self.model.get_weights()
else:
self.wait += 1
if self.wait >= self.patience or self.params['epochs'] == epoch + 1:
self.stopped_epoch = epoch
self.model.stop_training = True
if self.restore_best_weights:
if self.verbose > 0:
print('Restoring model weights from the end of the best epoch')
self.model.set_weights(self.best_weights)
def on_train_end(self, logs=None):
print('best_avg_loss: %.5f (#%d)' % (self.best_avg_loss[0], self.best_avg_loss[1]))
def multivariate_data(dataset, target, start_index, end_index, history_size, target_size, step, single_step=False):
data = []
labels = []
start_index = start_index + history_size
if end_index is None:
end_index = len(dataset) - target_size
for i in range(start_index, end_index):
indices = range(i-history_size, i, step)
data.append(dataset[indices])
if single_step:
labels.append(target[i+target_size])
else:
labels.append(target[i:i+target_size])
return np.array(data), np.array(labels)
def transform_predicted(pr):
pr = pr.reshape(pr.shape[1], -1)
z = np.zeros((pr.shape[0], x_train.shape[2] - 1), dtype=pr.dtype)
pr = np.append(pr, z, axis=1)
pr = scaler.inverse_transform(pr)
pr = pr[:, 0]
return pr
step = 1
# creating datasets with look back
scaler = MinMaxScaler()
df_normalized = scaler.fit_transform(df.values)
dataset = df_normalized[:-num_prediction]
x_train, y_train = multivariate_data(dataset, dataset[:, 0], 0,len(dataset) - num_prediction + 1, look_back, num_prediction, step)
indices = range(len(dataset)-look_back, len(dataset), step)
x_test = np.array(dataset[indices])
x_test = np.expand_dims(x_test, axis=0)
y_test = np.expand_dims(df_normalized[-num_prediction:, 0], axis=0)
# creating past datasets to validate with EarlyStoppingCust
number_validates = 50
step_past = 5
validation_sets = [(x_test, y_test)]
for i in range(1, number_validates * step_past + 1, step_past):
indices = range(len(dataset)-look_back-i, len(dataset)-i, step)
x_t = np.array(dataset[indices])
x_t = np.expand_dims(x_t, axis=0)
y_t = np.expand_dims(df_normalized[-num_prediction-i:len(df_normalized)-i, 0], axis=0)
validation_sets.append((x_t, y_t))
if new_s_h5:
model = Sequential()
model.add(LSTM(32, return_sequences=False, activation = 'sigmoid', input_shape=(x_train.shape[1], x_train.shape[2])))
# model.add(Dropout(0.2))
# model.add(BatchNormalization())
# model.add(LSTM(units = 16))
model.add(Dense(y_train.shape[1]))
model.compile(optimizer = 'adam', loss = 'mse')
# EarlyStoppingCust is custom callback to validate each validation_sets and get average
# it takes epoch with best "best_avg" value
# es = EarlyStoppingCust(patience = 3, restore_best_weights = True, validation_sets = validation_sets, verbose = 1)
# or there is keras extension with built-in EarlyStopping, but it validates only 1 set that you pass through fit()
es = EarlyStopping(monitor = 'val_loss', patience = 3, restore_best_weights = True)
model.fit(x_train, y_train, batch_size = 64, epochs = 25, shuffle = True, validation_data = (x_test, y_test), callbacks = [es])
model.save('s.h5')
else:
model = load_model('s.h5')
predicted = model.predict(x_test)
predicted = transform_predicted(predicted)
print('predicted', predicted)
print('real', df.iloc[-num_prediction:, 0].values)
print('val_loss: %.5f' % (model.evaluate(x_test, y_test, verbose=0)))
fig = go.Figure()
fig.add_trace(go.Scatter(
x = df.index[-60:],
y = df.iloc[-60:,0],
mode='lines+markers',
name='real',
line=dict(color='#ff9800', width=1)
))
fig.add_trace(go.Scatter(
x = df.index[-num_prediction:],
y = predicted,
mode='lines+markers',
name='predict',
line=dict(color='#2196f3', width=1)
))
fig.update_layout(template='plotly_dark', hovermode='x', spikedistance=-1, hoverlabel=dict(font_size=16))
fig.update_xaxes(showspikes=True)
fig.update_yaxes(showspikes=True)
fig.show()
3
如今，可重现的示例如此罕见（与没有类似问题的公告相比），可以说是在您的帖子开始时宣传其存在的好主意（已添加；）；
—
Desertnaut
问题可能只是您期望股票市场有太多可预测性。如果您以一百万次硬币翻转的序列训练模型，然后尝试使其预测硬币翻转，那么即使模型翻转来自训练数据，该模型也会出错，这并不奇怪。不应记住其训练数据并反省它。
—
user2357112支持Monica
除了@ user2357112supportsMonica所说的之外，您的模型还算是对的，这实际上是我希望这样的模型能够真正实现的所有结果（至少具有任何一致性），并且您期望在5天内数据。您确实需要更多的数据才能有意义地说明模型中的错误是什么。
—
亚伦
还有更多参数可以调整模型。我尝试了其中的一些操作，例如提前停止（耐心= 20），增加次数，将lstm单位从32增加到64等。结果要好得多。在这里检查github.com/jvishnuvardhan/Stackoverflow_Questions/blob/master/…。正如@sirjay提到的，添加更多功能（当前仅4个），添加更多层（lstm，batchnorm，dropout等），运行超参数优化将带来更好的性能。
—
Vishnuvardhan Janapati
@VishnuvardhanJanapati感谢您的检查。我编译了代码，保存了模型，然后设置
—
sirjay，
df.drop(df.tail(10).index, inplace=True)，结果显示出与我相同的不良结果。
