C:\Users\马斌\Desktop\NGSIM_data_processing\80s\lstmtest.py:31: MatplotlibDeprecationWarning: Support for FigureCanvases without a required_interactive_framework attribute was deprecated in Matplotlib 3.6 and will be removed two minor releases later. plt.plot(history.history['loss']) C:\Users\马斌\Desktop\NGSIM_data_processing\80s\lstmtest.py:37: MatplotlibDeprecationWarning: Support for FigureCanvases without a required_interactive_framework attribute was deprecated in Matplotlib 3.6 and will be removed two minor releases later. plt.show()

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OSError: [Errno 22] Invalid argument: '"C:\\Users\\马斌\\Desktop\\NGSIM_data_processing\\NGSIM_Data.csv"'

这个错误通常是由于文件路径中的某些...5. 如果您使用的是Python，尝试使用原始字符串来表示文件路径，例如 r"C:\Users\马斌\Desktop\NGSIM_data_processing\NGSIM_Data.csv"。希望这些解决方案能够帮助您解决问题。

如何解决Traceback (most recent call last): File "C:\Users\马斌\Desktop\NGSIM_data_processing\80s\lstmtest.py", line 42, in <module> train_predict = scaler.inverse_transform(train_predict) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (611,1) doesn't match the broadcast shape (611,2)

1. 确保你的训练数据(train_data)的形状与缩放器(scaler)的形状匹配。如果不匹配，你可以尝试调整训练数据的形状或重新训练缩放器。 2. 如果你确定你的训练数据和缩放器的形状是匹配的，你可以尝试在调用inverse_...

如何解决D:\python\python3.9.5\python.exe C:/Users/马斌/Desktop/NGSIM_data_processing/30s/x-y.py C:\Users\马斌\Desktop\NGSIM_data_processing\30s\x-y.py:12: MatplotlibDeprecationWarning: Support for FigureCanvases without a required_interactive_framework attribute was deprecated in Matplotlib 3.6 and will be removed two minor releases later. plt.plot(x, y) C:\Users\马斌\Desktop\NGSIM_data_processing\30s\x-y.py:20: MatplotlibDeprecationWarning: Support for FigureCanvases without a required_interactive_framework attribute was deprecated in Matplotlib 3.6 and will be removed two minor releases later. plt.show() D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:68: UserWarning: Glyph 36724 (\N{CJK UNIFIED IDEOGRAPH-8F74}) missing from current font. self.figure.tight_layout() D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:68: UserWarning: Glyph 25968 (\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font. self.figure.tight_layout() D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:68: UserWarning: Glyph 25454 (\N{CJK UNIFIED IDEOGRAPH-636E}) missing from current font. self.figure.tight_layout() D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:68: UserWarning: Glyph 20998 (\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font. self.figure.tight_layout() D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:68: UserWarning: Glyph 26512 (\N{CJK UNIFIED IDEOGRAPH-6790}) missing from current font. self.figure.tight_layout() D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:69: UserWarning: Glyph 36724 (\N{CJK UNIFIED IDEOGRAPH-8F74}) missing from current font. FigureCanvasAgg.draw(self) D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:69: UserWarning: Glyph 25968 (\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font. FigureCanvasAgg.draw(self) D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:69: UserWarning: Glyph 25454 (\N{CJK UNIFIED IDEOGRAPH-636E}) missing from current font. FigureCanvasAgg.draw(self) D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:69: UserWarning: Glyph 20998 (\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font. FigureCanvasAgg.draw(self) D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:69: UserWarning: Glyph 26512 (\N{CJK UNIFIED IDEOGRAPH-6790}) missing from current font. FigureCanvasAgg.draw(self)

font = FontProperties(fname=r"C:\Windows\Fonts\simhei.ttf", size=14) # 在绘制图表前设置字体 plt.rcParams['font.family'] = ['SimHei'] # 绘制图表 plt.plot(x, y) plt.show() 其中，r"C:\Windows\...

D:\python\python3.9.5\python.exe C:/Users/马斌/Desktop/NGSIM_data_processing/30s/x-y.py C:\Users\马斌\Desktop\NGSIM_data_processing\30s\x-y.py:12: MatplotlibDeprecationWarning: Support for FigureCanvases without a required_interactive_framework attribute was deprecated in Matplotlib 3.6 and will be removed two minor releases later. plt.plot(x, y) C:\Users\马斌\Desktop\NGSIM_data_processing\30s\x-y.py:20: MatplotlibDeprecationWarning: Support for FigureCanvases without a required_interactive_framework attribute was deprecated in Matplotlib 3.6 and will be removed two minor releases later. plt.show() D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:68: UserWarning: Glyph 36724 (\N{CJK UNIFIED IDEOGRAPH-8F74}) missing from current font. self.figure.tight_layout() D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:68: UserWarning: Glyph 25968 (\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font. self.figure.tight_layout() D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:68: UserWarning: Glyph 25454 (\N{CJK UNIFIED IDEOGRAPH-636E}) missing from current font. self.figure.tight_layout() D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:68: UserWarning: Glyph 20998 (\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font. self.figure.tight_layout() D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:68: UserWarning: Glyph 26512 (\N{CJK UNIFIED IDEOGRAPH-6790}) missing from current font. self.figure.tight_layout() D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:69: UserWarning: Glyph 36724 (\N{CJK UNIFIED IDEOGRAPH-8F74}) missing from current font. FigureCanvasAgg.draw(self) D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:69: UserWarning: Glyph 25968 (\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font. FigureCanvasAgg.draw(self) D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:69: UserWarning: Glyph 25454 (\N{CJK UNIFIED IDEOGRAPH-636E}) missing from current font. FigureCanvasAgg.draw(self) D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:69: UserWarning: Glyph 20998 (\N{CJK UNIFIED IDEOGRAPH-5206}) missing from current font. FigureCanvasAgg.draw(self) D:\pyCharm\PyCharm 2020.2\plugins\python\helpers\pycharm_matplotlib_backend\backend_interagg.py:69: UserWarning: Glyph 26512 (\N{CJK UNIFIED IDEOGRAPH-6790}) missing from current font. FigureCanvasAgg.draw(self)

这是一个警告信息，提示你的字体库中缺少一些字符，可能会导致图形显示不完全或乱码。你可以通过更改字体或安装缺失的字体来解决这个问题。同时，还有一个关于 FigureCanvas 的警告，这意味着 Matplotlib 将不再支持...

D:\python\python3.9.5\python.exe C:/Users/马斌/Desktop/NGSIM_data_processing/80s/x-y.py Traceback (most recent call last): File "C:\Users\马斌\Desktop\NGSIM_data_processing\80s\x-y.py", line 6, in <module> data = pd.read_csv('车辆：1499序：2结果数据换算单位.csv') File "D:\python\python3.9.5\lib\site-packages\pandas\io\parsers\readers.py", line 912, in read_csv return _read(filepath_or_buffer, kwds) File "D:\python\python3.9.5\lib\site-packages\pandas\io\parsers\readers.py", line 577, in _read parser = TextFileReader(filepath_or_buffer, kwds) File "D:\python\python3.9.5\lib\site-packages\pandas\io\parsers\readers.py", line 1407, in init self._engine = self._make_engine(f, self.engine) File "D:\python\python3.9.5\lib\site-packages\pandas\io\parsers\readers.py", line 1679, in _make_engine return mapping[engine](f, self.options) File "D:\python\python3.9.5\lib\site-packages\pandas\io\parsers\c_parser_wrapper.py", line 93, in init self._reader = parsers.TextReader(src, **kwds) File "pandas\_libs\parsers.pyx", line 548, in pandas._libs.parsers.TextReader.cinit File "pandas\_libs\parsers.pyx", line 637, in pandas._libs.parsers.TextReader._get_header File "pandas\_libs\parsers.pyx", line 848, in pandas._libs.parsers.TextReader._tokenize_rows File "pandas\_libs\parsers.pyx", line 859, in pandas._libs.parsers.TextReader._check_tokenize_status File "pandas\_libs\parsers.pyx", line 2017, in pandas._libs.parsers.raise_parser_error UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb1 in position 0: invalid start byte Process finished with exit code 1

这是一个UnicodeDecodeError...data = pd.read_csv('车辆：1499序：2结果数据换算单位.csv', encoding='GB2312') 如果指定了正确的编码仍然无法解决问题，那么文件内容可能存在其他问题，需要进一步检查和处理。

import pandas as pd import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense data = pd.read_csv('车辆：274序：4结果数据.csv') x = data[['车头间距', '原车道前车速度']].values y = data['本车速度'].values train_size = int(len(x) * 0.7) test_size = len(x) - train_size x_train, x_test = x[0:train_size,:], x[train_size:len(x),:] y_train, y_test = y[0:train_size], y[train_size:len(y)] from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler(feature_range=(0, 1)) x_train = scaler.fit_transform(x_train) x_test = scaler.transform(x_test) model = Sequential() model.add(LSTM(50, input_shape=(2, 1))) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') history = model.fit(x_train.reshape(-1, 2, 1), y_train, epochs=100, batch_size=32, validation_data=(x_test.reshape(-1, 2, 1), y_test)) plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper right') plt.show() train_predict = model.predict(x_train.reshape(-1, 2, 1)) test_predict = model.predict(x_test.reshape(-1, 2, 1)) train_predict = scaler.inverse_transform(train_predict) train_predict = train_predict.reshape(-1) # 将结果变为一维数组 y_train = scaler.inverse_transform(y_train.reshape(-1, 1)).reshape(-1) # 将结果变为一维数组 test_predict = scaler.inverse_transform(test_predict) y_test = scaler.inverse_transform([y_test]) plt.plot(y_train[0], label='train') plt.plot(train_predict[:,0], label='train predict') plt.plot(y_test[0], label='test') plt.plot(test_predict[:,0], label='test predict') plt.legend() plt.show()报错Traceback (most recent call last): File "C:\Users\马斌\Desktop\NGSIM_data_processing\80s\lstmtest.py", line 42, in <module> train_predict = scaler.inverse_transform(train_predict) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (611,1) doesn't match the broadcast shape (611,2)

history = model.fit(x_train.reshape(-1, 2, 1), y_train, epochs=100, batch_size=32, validation_data=(x_test.reshape(-1, 2, 1), y_test)) plt.plot(history.history['loss']) plt.plot(history.history['...

Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn-lstm改.py", line 57, in <module> lstm_input[:-cut,:,:,:],lstm_output[:-cut:],lstm_input[-cut:,:,:,:],lstm_output[-cut:] IndexError: too many indices for array: array is 3-dimensional, but 4 were indexed

这个错误提示表明在对一个三维 Numpy 数组进行切片操作时，使用了四个索引。根据你提供的代码，可能是在以下这行代码中出现了问题： lstm_input[:-cut,:,:,:],lstm_output[:-cut:],lstm_input[-cut:,:,:,:],...

Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn-lstm改.py", line 46, in <module> data = np.array(data)/scale TypeError: unsupported operand type(s) for /: 'str' and 'float'

这个错误是因为你正在尝试将字符串类型的数据除以一个浮点数，这是不允许的。你需要先将字符串类型的...data = float(data) / scale 或者这样做： data = int(data) / scale 这样就可以避免这个错误了。

添加之后运行结果还是D:\python\python3.9.5\pythonProject\venv\Scripts\python.exe C:/Users/马斌/Desktop/cnn测试/IDM.py 最大加速度的rmse值为： nan Process finished with exit code 0

如果添加了判断之后还是出现了 NaN 的结果，那么很可能是数据中存在其他问题导致的。建议你检查一下数据中是否存在其他异常值，比如负数等等。另外，你可以在计算最大加速度的 rmse 值时，也加上判断时间差是否为 ...

Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn-lstm改.py", line 32, in <module> data = int(data) / scale TypeError: int() argument must be a string, a bytes-like object or a number, not 'DataFrame'

这个错误是因为你试图将一个DataFrame对象转换为整数类型...data = df.iloc[:, 0].astype(int) / scale 或者这样做： data = df.loc[:, 'column_name'].astype(int) / scale 这样就可以避免这个错误了。

import tensorflow as tf import my_cnn_lstm_model # 导入你自己定义的模型 # 加载模型的权重 model = MyModel() model.load_weights('my_cnn_lstm_model1.h5') # 进行预测 data= data[["本车速度",'车头间距',"原车道前车速度"]] data = data.dropna() print(data.tail())报错D:\python\python3.9.5\pythonProject\venv\Scripts\python.exe C:/Users/马斌/Desktop/model/model/sudu.py Traceback (most recent call last): File "C:\Users\马斌\Desktop\model\model\sudu.py", line 2, in <module> import my_cnn_lstm_model # 导入你自己定义的模型 ModuleNotFoundError: No module named 'my_cnn_lstm_model' Process finished with exit code 1

这个错误提示说明 Python 没有找到名为 my_cnn_lstm_model 的模块。请确保该模块存在，并且可以通过 import my_cnn_lstm_model 导入。此外，还要确保该模块所在的目录已被添加到 Python 的搜索路径。...

Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn改.py", line 48, in <module> train_predict = scaler.inverse_transform(train_predict) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (110,1) doesn't match the broadcast shape (110,4)

这个错误提示是说在进行逆归一化操作时，输出的数据形状与原始数据形状不匹配。具体来说，逆归一化输出的数据形状是(110,1)，而原始数据的形状是(110,4)，因此无法完成逆归一化操作。解决方法是，将逆归一化输出的...

train_predict = scaler.inverse_transform(train_predict.reshape(-1, 1)) y_train = scaler.inverse_transform([y_train.reshape(-1, 1)]) train_predict = np.tile(train_predict, (1, 4)) y_train = np.tile(y_train, (1, 4)) y_train = scaler.inverse_transform(y_train.T).reshape(110, 4) test_predict = scaler.inverse_transform(test_predict) y_test = scaler.inverse_transform([y_test])报错Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn改.py", line 48, in <module> train_predict = scaler.inverse_transform(train_predict.reshape(-1, 1)) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (110,1) doesn't match the broadcast shape (110,4)

这个报错信息和之前的一样，都是因为在进行逆归一化操作时，输出的形状与广播形状不匹配导致的。具体来说，你在对训练数据进行逆归一化操作时，复制了4次train_predict和y_train，让它们的形状变成了（110，4）和（1...

D:\python\python3.9.5\pythonProject\venv\Scripts\python.exe C:/Users/马斌/Desktop/model/model/sudu.py 2023-05-25 00:57:58.533488: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. Traceback (most recent call last): File "C:\Users\马斌\Desktop\model\model\sudu.py", line 7, in <module> predictions = model.predict(input_data) NameError: name 'input_data' is not defined Process finished with exit code 1

这个错误提示是因为在代码中没有定义 input_data 变量，导致模型无法预测。你需要检查代码中是否有定义 input_data 变量，并且是否在调用 model.predict() 方法之前对其进行了赋值。另外，你的 TensorFlow ...

Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn改.py", line 50, in <module> y_train = scaler.inverse_transform([y_train]) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing\_data.py", line 541, in inverse_transform X -= self.min_ ValueError: operands could not be broadcast together with shapes (1,110) (4,) (1,110)

这个错误通常是由于数组形状不兼容而导致的。在您的代码中，似乎您正在尝试将形状为（1，110）的数组与形状为（4，）的数组进行广播操作，从而导致此错误。您可以尝试使用 reshape() 方法来重新调整数组的形状，...

药房环境监测与远程控制系统开题报告(新模版).doc

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OSError: [Errno 22] Invalid argument: '"C:\\Users\\马斌\\Desktop\\NGSIM_data_processing\\NGSIM_Data.csv"'

Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn-lstm改.py", line 57, in <module> lstm_input[:-cut,:,:,:],lstm_output[:-cut:],lstm_input[-cut:,:,:,:],lstm_output[-cut:] IndexError: too many indices for array: array is 3-dimensional, but 4 were indexed

Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn-lstm改.py", line 46, in <module> data = np.array(data)/scale TypeError: unsupported operand type(s) for /: 'str' and 'float'

添加之后运行结果还是D:\python\python3.9.5\pythonProject\venv\Scripts\python.exe C:/Users/马斌/Desktop/cnn测试/IDM.py 最大加速度的rmse值为： nan Process finished with exit code 0

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