from sklearn.model_selection import train_test_split X_train,X_validation,y_train,y_validation=train_test_split(X,y,test_size=0.1,random_state=42, stratify=y) 读入数据3 X_train=torch.tensor(X_train.values, dtype=torch.float32).reshape(X_train.shape[0],1,28,28) X_validation=torch.ten...
%% STEP 1: Load data load digitData trainData = train_x'; [~, trainLabels] = max(train_y, [], 2); %%% 增加数据 %%% ZCA白化 像素值范围变化 [] % trainData = ZCAWhite(trainData); %% STEP 2: Train the first sparse autoencoder sae1Theta = initializeParameters(hiddenSizeL1, inputS...
data_train.head() 运行上述代码后,会出现 该代码的意思是查看data_train的前五行数据,可以发现,第一列是label,即该行是什么数字,后面pixel 0到pixel 783为[0,255]的像素值,每一张数字图片都是28×28的尺寸,此处是将其拉长一行向量,因此有28×28=784个元素。 接下来 data_train.info() 运行后出现以下结果...
data_train data_validation .gitattributes .gitignore Pipfile main.py readme.md Repository files navigation README Label Recognizer Download finetuned weights Download safetensors into ./checkpoints/moondream-ft for using. https://mega.nz/file/l0NDTY7A#2-7vx7pY02S-78Qj0YcX-AFf6tT...
train_lable = train_data['label'] x = train_data.drop(columns=['label'])max=0max_k =5# k取值从5,15用K折交叉验证算出正确率分数forkinrange(5,15): clf = KNeighborsClassifier(n_neighbors=k)# cv为2折scores = cross_val_score(clf, x, train_lable, cv=2, scoring='accuracy') ...
https://www.kaggle.com/c/digit-recognizer 首先看一下提供的训练文件train.csv 根据他的描述可以知道label是指数字是几 pixel是指784个像素点 共42000个数据 首先尝试用kNN算法 点击打开kNN.py 首先先让前41900个数据当训练集 后100个用作测试 看看正确率 输出结果: 看正确率还不错 直接让train.csv作为训练...
An Azure Storage blob container's SAS URI. A container URI (without SAS) can be used if the container is public or has a managed identity configured. For more information on setting up a training data set, see:https://aka.ms/azsdk/formrecognizer/buildtrainingset. ...
():trainData,trainLabel=loadTrainData()testData=loadTestData()testLabel=loadTestResult()m,n=shape(testData)errorCount=0resultList=[]foriinrange(m):print("classify: ",i)classifierResult=classify(testData[i],trainData[0:20000],trainLabel.transpose()[0:20000],5)resultList.append(classifier...
(28, 28), Image.Resampling.BILINEAR) image_data = np.array(image.convert('L'),'f') # plt.imshow(image_data, cmap='gray') # plt.grid(False) #plt.show() recognizer = MINSTRecognizer.MINSTRecognizer() output = recognizer.recognize(image_data) self.label.config(text="识别结果为"+str(...
the datatrain=pd.read_csv("../input/train.csv")test=pd.read_csv("../input/test.csv")Y_train=train["label"]X_train=train.drop(labels=["label"],axis=1)# free some spacedeltraing=sns.countplot(Y_train)Y_train.value_counts()14684744013435194188241776413704132440728406353795Name:label,dtype:...