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

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 ...

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 ...

运行了结果为什么是这样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

这个问题很可能是由于数据中存在缺失值而导致的，导致计算结果为 NaN。建议你先检查一下数据中是否存在缺失值，并尝试对缺失值进行处理。另外，还有可能是由于计算中出现了除以 0 的情况，也会导致计算结果为 NaN...

你上面修改后的代码运行后显示：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 进程已完成，退出代码为 0

这个错误一般是由于计算过程中出现了 NaN（Not a Number）的值导致的。可能是由于某些数据缺失或者计算错误引起的。你可以检查一下数据是否完整，或者代码中是否存在除以 0 的操作等错误。你还可以在代码中加入一些...

pycharm咋样修改E:\python\python3.9.5\python.exe E:\python\study\uu.py

在您的情况下，路径是 "E:\python\python3.9.5\python.exe"。选择该文件并点击 "OK"。 9. 返回到 "Add Python Interpreter"（添加Python解释器）窗口，确保已正确显示您的Python解释器路径。点击 "OK"。 10. 返回...

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() 20/20 [==============================] - 0s 832us/step 9/9 [==============================] - 0s 0s/step 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)

这个错误的原因是在进行逆变换时，输入的数组的形状与原始数据的形状不匹配。具体来说，你的 train_predict 数组的形状为 (611, 1)，而 scaler.inverse_transform() 函数期望的形状是 (611, 2)。...

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_...

如何修改报错D:\python\python3.9.5\pythonProject\venv\Scripts\python.exe C:/Users/马斌/Desktop/cnn测试/IDM.py Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\IDM.py", line 29, in <module> df['expected_spacing'] = IDM_spacing(df['本车速度'], df['原车道前车速度'] - df['本车速度']) File "C:\Users\马斌\Desktop\cnn测试\IDM.py", line 19, in IDM_spacing return s0 + max(0, v * T + (v * dv) / (2 * np.sqrt(a * b))) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\pandas\core\generic.py", line 1466, in nonzero raise ValueError( ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

你可以在代码中使用....df['expected_spacing'] = df.apply(lambda x: IDM_spacing(x['本车速度'], x['原车道前车速度'] - x['本车速度']), axis=1) 这样就可以对每行数据进行计算了，axis=1表示按行进行计算。

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。可以使用...

Traceback (most recent call last): File "<string>", line 1, in <module> File "d:\python\python3.9.5\lib\multiprocessing\spawn.py", line 116, in spawn_main exitcode = _main(fd, parent_sentinel) File "d:\python\python3.9.5\lib\multiprocessing\spawn.py", line 126, in _main self = reduction.pickle.load(from_parent) File "d:\python\python3.9.5\lib\site-packages\torch\init.py", line 122, in <module> raise err OSError: [WinError 1455] 页面文件太小，无法完成操作。 Error loading "d:\python\python3.9.5\lib\site-packages\torch\lib\cufft64_10.dll" or one of its dependencies.

这个错误是由于页面文件太小而导致的，无法加载"cufft64_10.dll"或其依赖项之一。页面文件是操作系统用于管理虚拟内存的一部分。解决这个问题的一种方法是增大页面文件的大小。你可以按照以下步骤来增大页面文件的...

D:\python\python3.9.5\pythonProject\venv\Scripts\python.exe C:/Users/马斌/Desktop/cnn测试/cnn-lstm改.py 本车ID 时间总帧数全局时间 ... 原车道后车x坐标原车道后车y坐标原车道后车速度原车道后车加速度 1595 1499 7053 1604 1.113440e+12 ... 56.283 1602.157 15.27 -2.61 1596 1499 7054 1604 1.113440e+12 ... 56.294 1603.665 15.07 -1.53 1597 1499 7055 1604 1.113440e+12 ... 56.304 1605.152 14.99 0.24 1598 1499 7056 1604 1.113440e+12 ... 56.314 1606.649 15.03 0.64 1599 1499 7057 1604 1.113440e+12 ... 56.325 1608.171 14.95 -1.65 [5 rows x 27 columns] Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn-lstm改.py", line 97, in <module> myModel = cnn_lstm_model() # 建立模型 File "C:\Users\马斌\Desktop\cnn测试\cnn-lstm改.py", line 78, in cnn_lstm_model inputs = Input(shape=(windows, amount_of_features)) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 214, in init raise TypeError( TypeError: Dimension value must be integer or None or have an index method, got value '(4,)' with type '<class 'tuple'>' 本车加速度车头间距原车道前车速度本车速度 1554 0.00 43.58 17.68 11.75 1555 0.00 44.29 17.82 11.75 1556 0.00 45.07 17.82 11.75 1557 0.08 45.39 17.82 11.76 1558 0.43 45.72 17.82 11.79 (1401, 2, 4) (156, 2, 4) (1401,) (156,) Process finished with exit code 1

这段代码是一个程序的运行结果，其中包含了一些错误信息和数据信息。根据错误信息，可以看出是在创建模型时出现了问题，具体来说是在创建模型输入层时，出现了一个维度设置错误。根据错误信息中的提示，inputs = ...

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, 1) y_train = scaler.inverse_transform([y_train]) 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)

这个错误提示是因为在使用 scaler.inverse_transform() 函数时，输入的 train_predict 和 y_train 的维度不匹配，导致无法进行运算。具体来说，train_predict 是一个形状为 (611, 1) 的二维数组，而 y_...

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)

python train_predict = scaler.inverse_transform(train_predict) y_train = scaler.inverse_transform([y_train]) train_predict = np.tile(train_predict, (1, 4)) #将train_predict复制4次，变为(110,4) ...

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)

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_...

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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

运行了结果为什么是这样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

你上面修改后的代码运行后显示：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 进程已完成，退出代码为 0

pycharm咋样修改E:\python\python3.9.5\python.exe E:\python\study\uu.py

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