File "C:\Users\lenovo\PycharmProjects\pythonProject\Guiyi.py", line 11, in <module> df1.to_excel(file, index=False)
时间: 2023-09-19 22:10:30 浏览: 61
This error message indicates that there is an issue with the code on line 11 of the file "Guiyi.py". Specifically, it appears that the code is attempting to write a pandas DataFrame (df1) to an Excel file, but encountering an error in doing so. The specific error message may provide more information about what went wrong (e.g. a file permissions issue, a missing library or module, an incompatible data type in the DataFrame, etc.). To resolve the issue, you may need to review the code on line 11 and surrounding lines, and/or consult the pandas and openpyxl documentation for guidance on how to properly write DataFrame data to an Excel file.
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from keras.datasets import mnist from pyexpat import model import numpy as np from keras.models import Sequential from keras.layers import Dense from keras.optimizers import SGD import matplotlib.pyplot as plt from keras.utils.np_utils import to_categorical (X_train,Y_train),(X_test,Y_test)=mnist.load_data() print("X_train.shape:"+str(X_train.shape)) print("Y_train.shape:"+str(Y_train.shape)) print("X_test.shape:"+str(X_test.shape)) print("Y_test.shape:"+str(Y_test.shape)) print(Y_train[0]) #print label plt.imshow(X_train[0],cmap='gray') plt.show() X_train=X_train.reshape(60000,784)/255.0 X_test=X_test.reshape(10000,784)/255.0 #guiyi 255huiduzuidazhi Y_train = to_categorical(Y_train,10)#durebianma Y_test= to_categorical(Y_test,10) model =Sequential() model.add(Dense(units=256,activation='relu',input_dim=784)) model.add(Dense(units=256,activation='relu')) model.add(Dense(units=256,activation='relu')) model.add(Dense(units=10,activation='softmax')) #model.add(Dense(units=1,activation='sigmoid')) model.compile(loss='categorical_crossentropy',optimizer=SGD(lr=0.05),metrics=['accuracy']) model.fit(X_train,Y_train,epochs=100,batch_size=128) loss,accuracy=model.evaluate(X_test,Y_test) print("loss"+str(loss)) print("loss"+str(accuracy))
这段代码是使用Keras库来构建一个神经网络模型,用于手写数字识别。它使用了MNIST数据集来训练和测试模型。
首先,导入了必要的库:mnist数据集、numpy、matplotlib.pyplot、keras中的Sequential模型、Dense层和SGD优化器。
然后,使用mnist.load_data()函数加载MNIST数据集,并将训练集和测试集分别赋值给变量X_train, Y_train, X_test, Y_test。
接着,打印出了训练集和测试集的形状,并且显示了训练集中的第一个样本及其对应的标签。
接下来,对训练集和测试集进行了预处理,将像素值归一化到[0,1]之间,并对标签进行了one-hot编码。
然后,创建了一个Sequential模型,并添加了几个全连接层(Dense层),其中激活函数使用的是ReLU。最后一层输出层使用softmax激活函数,用于多分类任务。
接着,编译了模型,设置了损失函数为交叉熵(categorical_crossentropy),优化器为SGD,并指定了评估指标为准确率。
然后,使用模型.fit()函数进行训练,设置了训练的epochs(迭代次数)为100,批次大小为128。
最后,使用模型.evaluate()函数对测试集进行评估,得到了损失值和准确率,并将其打印出来。
这段代码的目的是训练一个神经网络模型来进行手写数字识别,并输出模型在测试集上的准确率。
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