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python - 在 RNN 中过度拟合用于 S&P 预测

问题描述

我正在根据 Udemy 上的深度学习 AZ 课程中的模型做一个 RNN。对于谷歌股票的例子，我们使用了 5 年的每日股价。在讲座的最后，据说是用更多的数据进行测试，或者改变 RNN 的参数或结构。我的想法是，如果我能获得更多数据，RNN 就能获得更好的结果。我从 S&P 下载了从 01/01/2006 到今天的数据，除了最后 23 天之外的火车测试分开，23 天是我的预测测试。所以我很高兴看到我是否能得到一种有用的见解……让它在 100 个 epoch 中运行。

Epoch 1/100
3599/3599 [==============================] - 235s 65ms/step - loss: 0.0090
Epoch 2/100
3599/3599 [==============================] - 210s 58ms/step - loss: 0.0024
Epoch 3/100
3599/3599 [==============================] - 208s 58ms/step - loss: 0.0022
Epoch 4/100
3599/3599 [==============================] - 557s 155ms/step - loss: 0.0024
Epoch 5/100
3599/3599 [==============================] - 211s 59ms/step - loss: 0.0022
Epoch 6/100
3599/3599 [==============================] - 207s 58ms/step - loss: 0.0018
Epoch 7/100
3599/3599 [==============================] - 216s 60ms/step - loss: 0.0018
Epoch 8/100
3599/3599 [==============================] - 265s 74ms/step - loss: 0.0016
Epoch 9/100
3599/3599 [==============================] - 215s 60ms/step - loss: 0.0016
Epoch 10/100
3599/3599 [==============================] - 209s 58ms/step - loss: 0.0014
Epoch 11/100
3599/3599 [==============================] - 217s 60ms/step - loss: 0.0014
Epoch 12/100
3599/3599 [==============================] - 216s 60ms/step - loss: 0.0013
Epoch 13/100
3599/3599 [==============================] - 218s 60ms/step - loss: 0.0012
Epoch 14/100
3599/3599 [==============================] - 217s 60ms/step - loss: 0.0012
Epoch 15/100
3599/3599 [==============================] - 210s 58ms/step - loss: 0.0012
Epoch 16/100
3599/3599 [==============================] - 292s 81ms/step - loss: 0.0012
Epoch 17/100
3599/3599 [==============================] - 328s 91ms/step - loss: 0.0011
Epoch 18/100
3599/3599 [==============================] - 199s 55ms/step - loss: 9.8658e-04
Epoch 19/100
3599/3599 [==============================] - 199s 55ms/step - loss: 0.0010
Epoch 20/100
3599/3599 [==============================] - 286s 79ms/step - loss: 9.9106e-04

WOW 0,0010 相当不错......但从这里开始它太低了。我停在了 39 纪元...因为它花费的时间太长而且损失太小。

Epoch 39/100
2560/3599 [====================>.........] - ETA: 1:00 - **loss: 6.3598e-04**

这是结果

我是否过度拟合数据？或者过早停止是大错误的原因？我可以做些什么来优化运行 100 个 epoch 所需的时间？

代码如下：

  # Recurrent Neural Network



# Part 1 - Data Preprocessing

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout

# Importing the training set
dataset_train = pd.read_csv('S&P_Train.csv')
training_set = dataset_train.iloc[:, 1:2].values

# Feature Scaling

sc = MinMaxScaler(feature_range =  [0, 1])
training_set_sc = sc.fit_transform(training_set)

# Creating a data structure with 60 timesteps and 1 output
X_train = []
y_train = []
for i in range(60, 3659):
    X_train.append(training_set_sc[i-60:i, 0])
    y_train.append(training_set_sc[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)


# Reshaping
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))



# Part 2 - Building the RNN

# Importing the Keras libraries and packages


# Initialising the RNN
regressor = Sequential()


# Adding the first LSTM layer and some Dropout regularisation
rnn = regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
rnn = regressor.add(Dropout(0.2))

# Adding a second LSTM layer and some Dropout regularisation
rnn = regressor.add(LSTM(units = 50, return_sequences = True))
rnn = regressor.add(Dropout(0.2))

# Adding a third LSTM layer and some Dropout regularisation
rnn = regressor.add(LSTM(units = 50, return_sequences = True))
rnn = regressor.add(Dropout(0.2))

# Adding a fourth LSTM layer and some Dropout regularisation
rnn = regressor.add(LSTM(units = 50))
rnn = regressor.add(Dropout(0.2))

# Adding the output layer
rnn = regressor.add(Dense(units = 1))

# Compiling the RNN

rnn = regressor.compile(optimizer = 'Adam', loss = 'mean_squared_error')
# Fitting the RNN to the Training set
regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)



# Part 3 - Making the predictions and visualising the results
print('ok')

# Getting the real stock price of 2017
dataset_test = pd.read_csv('S&P_Test.csv')
real_stock_price = dataset_test.iloc[:, 1:2].values

# Getting the predicted stock price of 2017
dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1, 1)
inputs = sc.transform(inputs)
X_test = []
for i in range(60, 83):
    X_test.append(inputs[i-60:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
predicted_stock_price = regressor.predict(X_test)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)

# Visualising the results
plt.plot(real_stock_price, color = 'red', label = 'Real Stock Price')
plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Stock Price')
plt.title('Prediction of Stocks Values')
plt.xlabel('time')
plt.ylabel('Stock Price')
plt.legend()
plt.show()

标签： pythonkerasdeep-learningneural-network

解决方案

我是否过度拟合数据？

是的，你可能做到了，你可以通过 val_loss 进行检查，如果你的验证损失开始增加，你就过拟合了。您应该使用 validation_set 并检查 validation_error

我可以做些什么来优化运行 100 个 epoch 所需的时间？

您可以在使用来自 tensorflow api 的 Earlystopping 过度拟合数据之前停止训练，tf.keras.callbacks.EarlyStopping()

from tensorflow.keras.callbacks import EarlyStopping

early_stopping = EarlyStopping()

model.compile(...)
model.fit(..., epochs = 9999, callbacks=early_stopping)

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