import pandas as pd from keras.models import Sequential from keras.layers import Dense import numpy as np from sklearn.metrics import accuracy_score file = pd.read_excel('/Users/zxh-mac/desktop/Edu-Data（A题数据）.xlsx') # 第二阶段：转化定性变量为定量变量 使用onehot函数 同时读取新的excel x = pd.get_dummies(file, dtype=int) x.to_excel('/Users/zxh-mac/desktop/Edu-Data(onehot_version).xlsx') data = pd.read_excel('/Users/zxh-mac/desktop/Edu-Data(onehot_version).xlsx') # 第三阶段：实现bp神经网络 train_data = data[:320] test_data = data[320:] train_features = train_data.drop('Class', axis=1).values train_labels = train_data['Class'].values test_features = test_data.drop('Class', axis=1).values test_labels = test_data['Class'].values model = Sequential() model.add(Dense(units=72, activation='relu', input_dim=train_features.shape[1])) model.add(Dense(units=72, activation='relu')) model.add(Dense(units=3, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(train_features, train_labels, epochs=10, batch_size=32, validation_data=(test_features, test_labels), verbose=1) predictions = model.predict(test_features) print(predictions)事实上，这段代码目前运行中的结果为进程已结束,退出代码132 (interrupted by signal 4: SIGILL)

根据你提供的信息，这段代码的运行结果是进程已结束，退出代码132，这可能是因为程序中使用了非法指令或者是内存访问越界等问题导致的程序中止。其中，信号4代表的是SIGILL错误，表示非法指令错误。

你可以尝试重新运行代码，或者检查你的代码中是否存在语法错误、内存泄漏等问题。此外，如果你使用的是Jupyter Notebook等交互式开发环境，可能会因为内存不足等问题导致程序运行失败，你可以尝试关闭一些不必要的程序或者增加内存空间来解决这个问题。

相关问题

import pandas as pd from keras.models import Sequential from keras.layers import Dense import numpy as np from sklearn.metrics import accuracy_score file = pd.read_excel('/Users/zxh-mac/desktop/Edu-Data（A题数据）.xlsx') # 第二阶段：转化定性变量为定量变量 使用onehot函数 同时读取新的excel x = pd.get_dummies(file, dtype=int) x.to_excel('/Users/zxh-mac/desktop/Edu-Data(onehot_version).xlsx') data = pd.read_excel('/Users/zxh-mac/desktop/Edu-Data(onehot_version).xlsx') # 第三阶段：实现bp神经网络 train_data = data[:320] test_data = data[320:] train_features = train_data.drop('Class', axis=1).values train_labels = train_data['Class'].values test_features = test_data.drop('Class', axis=1).values test_labels = test_data['Class'].values model = Sequential() model.add(Dense(units=72, activation='relu', input_dim=train_features.shape[1])) model.add(Dense(units=72, activation='relu')) model.add(Dense(units=3, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(train_features, train_labels, epochs=10, batch_size=32, validation_data=(test_features, test_labels), verbose=1) predictions = model.predict(test_features) print(predictions)就是这段 无法确定问题是什么

这段代码的格式有些混乱，建议您按照以下方式进行修改：

1. 将 "import" 语句放在文件开头，便于管理和查看。
2. 在 "import" 语句后加上空行，使代码更易读。
3. 将代码分成多行，以提高可读性。
4. 确认文件路径是否正确，可以使用绝对路径来避免路径错误的问题。
5. 确认您是否安装了 keras 和 sklearn 库。

修改后的代码如下：

import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import accuracy_score

# 读取文件
file = pd.read_excel('/Users/zxh-mac/desktop/Edu-Data（A题数据）.xlsx')

# 使用onehot函数转换定性变量为定量变量
x = pd.get_dummies(file, dtype=int)
x.to_excel('/Users/zxh-mac/desktop/Edu-Data(onehot_version).xlsx')

# 读取新的excel
data = pd.read_excel('/Users/zxh-mac/desktop/Edu-Data(onehot_version).xlsx')

# 划分训练集和测试集
train_data = data[:320]
test_data = data[320:]

# 提取特征和标签
train_features = train_data.drop('Class', axis=1).values
train_labels = train_data['Class'].values
test_features = test_data.drop('Class', axis=1).values
test_labels = test_data['Class'].values

# 建立模型
model = Sequential()
model.add(Dense(units=72, activation='relu', input_dim=train_features.shape[1]))
model.add(Dense(units=72, activation='relu'))
model.add(Dense(units=3, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
model.fit(train_features, train_labels, epochs=10, batch_size=32, validation_data=(test_features, test_labels), verbose=1)

# 预测测试集
predictions = model.predict(test_features)
print(predictions)

如果问题还存在，建议您给出具体的错误信息或者提示，以便我们更好地帮助您解决问题。

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.utils import shuffle from sklearn.preprocessing import scale df = pd.read_csv("C:\\boston.csv",header=0) ds = df.values from sklearn.datasets import load_boston boston = load_boston() X = boston.data y = boston.target from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense model = Sequential([ Dense(64, activation='relu', input_shape=(X_train.shape[1],)), Dense(64, activation='relu'), Dense(64, activation='relu'), Dense(1) ]) model.compile(loss='mean_squared_error', optimizer='adam') history = model.fit(X_train, y_train, validation_split=0.1, epochs=100, batch_size=32) from sklearn.metrics import mean_squared_error y_pred = model.predict(x_test)mse = mean_squared_error(y_test, y_pred)print('MSE:’, mse) import matplotlib.pyplot as plt plt.plot(history.history['accuracy'], label='train') plt.plot(history.history['val_accuracy'], label='validation') plt.legend() plt.show()

这段代码使用TensorFlow和Keras实现了一个神经网络模型，用于预测波士顿房价。以下是代码的解释：

首先，导入必要的库和模块：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.utils import shuffle
from sklearn.preprocessing import scale

然后，读取波士顿房价数据集并对其进行预处理：

df = pd.read_csv("C:\\boston.csv",header=0)
ds = df.values

接着，从sklearn.datasets模块中加载波士顿房价数据集，并将其分为训练集和测试集：

from sklearn.datasets import load_boston
boston = load_boston()
X = boston.data
y = boston.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

对训练集和测试集进行标准化处理：

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

定义一个包含4个Dense层的神经网络模型：

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
    Dense(64, activation='relu'),
    Dense(64, activation='relu'),
    Dense(1)
])

编译模型并训练：

model.compile(loss='mean_squared_error', optimizer='adam')

history = model.fit(X_train, y_train, validation_split=0.1, epochs=100, batch_size=32)

使用模型对测试集进行预测，并计算均方误差：

from sklearn.metrics import mean_squared_error
y_pred = model.predict(x_test)
mse = mean_squared_error(y_test, y_pred)
print('MSE:’, mse)

最后，绘制模型的训练和验证准确率曲线：

import matplotlib.pyplot as plt

plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='validation')

plt.legend()

plt.show()