首先你得有一個(gè)谷歌賬號(hào)!!!
參考: https://zhuanlan.zhihu.com/p/149233850
https://blog.csdn.net/lumingha/article/details/104825702
1 將數(shù)據(jù)傳送至谷歌云盤(pán)(云盤(pán)地址:https://drive.google.com/drive/my-drive)
創(chuàng)建文件夾 上傳數(shù)據(jù)到stock_data

2 跳轉(zhuǎn)到colab(新建—>更多–> google Colaboratory)


3 掛載網(wǎng)盤(pán)+ 切換路徑

from google.colab import driveimport os# 掛載網(wǎng)盤(pán)drive.mount('/content/drive/') # 切換路徑os.chdir('/content/drive/MyDrive/rnn')

4. 查看文件

!ls -ll # 和linux終端用法差不多 但是!(感嘆號(hào))不能少

5. 執(zhí)行代碼
我的代碼在本地是可以運(yùn)行的
直接粘貼進(jìn)去進(jìn)行了,代碼如下(這里用的是py文件,最好使用ipy)

# -*- coding: utf-8 -*- import datetimeimport numpy as np import tensorflow as tf from tensorflow.keras.layers import Dropout, Dense, SimpleRNN import matplotlib.pyplot as plt import os import pandas as pd from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error, mean_absolute_error import math# 歸一化 sc = MinMaxScaler(feature_range=(0, 1)) # 定義歸一化：歸一化到(0，1)之間def get_stock_data(file_path):maotai = pd.read_csv(file_path)training_set = maotai.iloc[0:2426 - 300, 2:3].valuestest_set = maotai.iloc[2426 - 300:, 2:3].valuestraining_set_scaled = sc.fit_transform(training_set)test_set_scaled = sc.transform(test_set)x_train = []y_train = []for i in range(60, len(training_set_scaled)):x_train.append(training_set_scaled[i - 60:i, 0])y_train.append(training_set_scaled[i, 0])np.random.seed(7)np.random.shuffle(x_train)np.random.seed(7)np.random.shuffle(y_train)x_train = np.array(x_train)y_train = np.array(y_train)x_train = np.reshape(x_train, (x_train.shape[0], 60, 1))x_test = []y_test = []for i in range(60, len(test_set_scaled)):x_test.append(test_set_scaled[i - 60:i, 0])y_test.append(test_set_scaled[i, 0])x_test = np.array(x_test)y_test = np.array(y_test)x_test = np.reshape(x_test, (x_test.shape[0], 60, 1))return (x_train, y_train), (x_test, y_test)def load_local_model(model_path):if os.path.exists(model_path + '/saved_model.pb'):print(datetime.datetime.now())local_model = tf.keras.models.load_model(model_path)else:local_model = tf.keras.Sequential([SimpleRNN(80, return_sequences=True),Dropout(0.2),SimpleRNN(100),Dropout(0.2),Dense(1)])local_model.compile(optimizer=tf.keras.optimizers.Adam(0.001),loss='mean_squared_error') # 損失函數(shù)用均方誤差return local_modeldef show_train_line(history):loss = history.history['loss']val_loss = history.history['val_loss']plt.plot(loss, label='Training Loss')plt.plot(val_loss, label='Validation Loss')plt.title('Training and Validation Loss')plt.legend()plt.show()def stock_predict(model, x_test, y_test):# 測(cè)試集輸入模型進(jìn)行預(yù)測(cè)predicted_stock_price = model.predict(x_test)# 對(duì)預(yù)測(cè)數(shù)據(jù)還原---從（0，1）反歸一化到原始范圍predicted_stock_price = sc.inverse_transform(predicted_stock_price)# 對(duì)真實(shí)數(shù)據(jù)還原---從（0，1）反歸一化到原始范圍real_stock_price = sc.inverse_transform(np.reshape(y_test, (y_test.shape[0], 1)))# 畫(huà)出真實(shí)數(shù)據(jù)和預(yù)測(cè)數(shù)據(jù)的對(duì)比曲線(xiàn)plt.plot(real_stock_price, color='red', label='MaoTai Stock Price')plt.plot(predicted_stock_price, color='blue', label='Predicted MaoTai Stock Price')plt.title('MaoTai Stock Price Prediction')plt.xlabel('Time')plt.ylabel('MaoTai Stock Price')plt.legend()plt.show()plt.savefig('./model/rnn/compare.jpg')mse = mean_squared_error(predicted_stock_price, real_stock_price)rmse = math.sqrt(mean_squared_error(predicted_stock_price, real_stock_price))mae = mean_absolute_error(predicted_stock_price, real_stock_price)print('均方誤差: %.6f' % mse)print('均方根誤差: %.6f' % rmse)print('平均絕對(duì)誤差: %.6f' % mae)if __name__ == '__main__':from google.colab import driveimport os# 掛載網(wǎng)盤(pán)drive.mount('/content/drive/')# 切換路徑os.chdir('/content/drive/MyDrive/rnn')'''# 查看當(dāng)前路徑!pwd # 查看當(dāng)前路徑下的文件夾 !ls -ll# 查看分配的機(jī)器!nvidia-smi '''file_path = './stock_data/SH600519.csv'(x_train, y_train), (x_test, y_test) = get_stock_data(file_path)model_path = "./model/rnn"model = load_local_model(model_path)history = model.fit(x_train, y_train, batch_size=64, epochs=100, validation_data=(x_test, y_test),validation_freq=1)show_train_line(history)model.summary()model.save(model_path, save_format="tf")stock_predict(model, x_test, y_test)

6.選擇gpu(代碼執(zhí)行程序–> 更改運(yùn)行時(shí)類(lèi)型–>硬件加速器選gpu)
查看當(dāng)前分配的硬件 可以使用!nvidia-smi

執(zhí)行結(jié)果如下

只是用來(lái)演示,本案例中運(yùn)行速度并不比本人筆記本快多少

總結(jié)

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