train_predict = model.predict(x_train.reshape(-1, 2, 1))

在这段代码中，`x_train` 是一个二维数组，它的形状为 `(n_samples, n_features)`，其中 `n_samples` 是样本数，`n_features` 是每个样本的特征数。但是，在将数据传递给模型进行预测之前，您需要将其重新调整为适当的形状。由于您正在使用卷积神经网络，所以您需要将数据转换为三维数组，其形状为 `(n_samples, n_timesteps, n_features)`。其中，`n_timesteps` 是时间步数，对应于卷积操作的大小。

在这里，您使用了 `reshape()` 方法来将 `x_train` 转换为三维数组。具体来说，`reshape(-1, 2, 1)` 将 `x_train` 转换为一个形状为 `(n_samples, 2, 1)` 的三维数组，其中 `2` 是时间步数，`1` 是每个时间步的特征数。然后，您可以将转换后的数据传递给模型进行预测。

相关问题

y_train_predict = mlp.predict(X_train) a = np.ones(275) b = a / 2 c = np.insert(y_train_predict, 0, b, axis=1) y_train_predict = np.argmax(c, axis=1) y_train_predict = y_train_predict.reshape(275, 1) from sklearn.metrics import accuracy_score accuracy_train = accuracy_score(y_train, y_train_predict) print(accuracy_train)

The code snippet you provided calculates the training accuracy of a multi-layer perceptron (MLP) model. Here's the modified code with the accuracy calculation and printing:

y_train_predict = mlp.predict(X_train)
a = np.ones(275)
b = a / 2
c = np.insert(y_train_predict, 0, b, axis=1)
y_train_predict = np.argmax(c, axis=1)
y_train_predict = y_train_predict.reshape(275, 1)
from sklearn.metrics import accuracy_score
accuracy_train = accuracy_score(y_train, y_train_predict)
print(accuracy_train)

This code computes the predictions `y_train_predict` of the MLP model on the training data `X_train`. It then creates an array `b` filled with ones and divides it by 2 to get an array of half ones. This array is inserted as the first column of `y_train_predict` using `np.insert()`. The `argmax()` function is then applied to `y_train_predict` to get the predicted class labels. Finally, the accuracy between the true training labels `y_train` and the predicted labels `y_train_predict` is calculated using `accuracy_score()` from the `sklearn.metrics` module, and printed out.

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)

这个错误发生在将归一化后的预测结果进行逆归一化时。根据错误提示，训练集的预测结果和真实值的形状为 (611,)，而测试集的预测结果和真实值的形状为 (1, 611)。这是因为在进行逆归一化时，要求预测结果和真实值的形状必须一致，即都为一维数组。

你可以将 y_test 的形状改为 (611,)，方法是将 y_test 的数据类型从二维数组转换为一维数组，就像对 y_train 做的那样。修改后的代码如下：

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.reshape(-1, 1)).reshape(-1) # 将结果变为一维数组

plt.plot(y_train, label='train') 
plt.plot(train_predict, label='train predict') 
plt.plot(y_test, label='test') 
plt.plot(test_predict, label='test predict') 
plt.legend() 
plt.show()