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'apply',P_test,inputps);% 測試集[Tn_train,outputps] = mapminmax(T_train);Tn_test = mapminmax('apply',T_test,outputps);%% ELM創建/訓練[IW,B,LW,TF,TYPE] = elmtrain(Pn_train,Tn_train,30,'sig',

# Split data into training, validation, and testing sets X_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size=0.2, random_state=42) X_train, X_val, y_train, y_val = train_test_split

:end,end)';train_x=train_x'train_y=train_y';test_x=test_x';test_y=test_y';%% 數據預處理% 數據預處理,將訓練集和測試集歸一化到[0,1]區間[mtrain,ntrain] = size(train_x);[mtest,ntest] = size(test_x);

np.linalg.norm(u_star, 2) 計算u_star的2范數u_train_noisy = u_train + noise * np.std(u_train) * np.random.randn(u_train.shape[0], u_train.shape[1]) 噪聲影響，np.std(u_train)是u_train的標準差，np.random.randn

, ps_input] = mapminmax(P_train, 0, 1);p_test = mapminmax('apply', P_test, ps_input );t_train = T_train;t_test = T_test ;%% 特征選擇后的數據集p_train = p_train(save_index, :);p_test = p_test (save_index, :)

= train_df.drop('quality',axis=1) X_val = val_df.drop('quality',axis=1) y_train = train_df['quality']

+1:end), :);X_valid_labels = X_labels(perm_idx(n_train+1:end));X = X(perm_idx(1:n_train), :);X_labels = X_labels(perm_idx(1:n_train));layers = [size(X,2), 500, 500, 10];n_layers = length(layers);blayers

# Load in the data cifar10 = tf.keras.datasets.cifar10 # Distribute it to train and test set (x_train, y_train), (x_test, y_test) = cifar10.load_data() print(x_train.shape, y_train.shape, x_test.shape

= [] val_losses = [] for epoch in range(num_epochs): model.train()#啟用訓練模式 optimizer.zero_grad()#清除梯度 output_train = model(x_train)#前向傳播 loss_train = criterion(output_train, y_train

開始網絡訓練訓練的參數較多，均在train.py中，大家可以在下載庫后仔細看注釋，其中最重要的部分依然是train.py里的classes_path。classes_path用于指向檢測類別所對應的txt，這個txt和voc_annotation.py里面的txt一樣！訓練自己的數據集必須要修改！

= double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% [train_x,train_y,test_x,test_y,RealTrainYlabel,RealTestLabel] = TrainTestSplit(

(train_images, train_labels), (test_images, test_labels) = cifar10.load_data() train_images, test_images = train_images / 255.0, test_images / 255.0 train_labels = to_categorical(train_labels, 10) test_labels

Train-Test 拆分 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

train_X, test_X, train_Y, test_Y = train_test_split(balanced_data['text'],

) scaled_train = scaler.transform(train) scaled_test = scaler.transform(test)

本文列述了Ansys Ensight Scripting Training 2021沒有涉及的內容，并重點介紹了作者認為學習Ensight Scripting重點需要知道的一些知識。

://www.yqgqt.org.cn/training/details/fluent）；LSDYNA模塊聚焦瞬態沖擊熱應力問題，結合15+實戰項目（如汽車保險杠碰撞熱應力、電池包擠壓熱失控仿真），講解顯式求解器參數配置與沖擊載荷耦合施加方法（鏈接：https://www.yqgqt.org.cn/training/details/lsdyna）；NCode模塊則實現從“應力分析”到“壽命預測”

Evaluation說明：結果值均使用F1結果均只用該數據集的train訓練，在test上評估得到的表現，沒用外部數據shibing624/bert4ner-base-chinese模型達到同級別參數量SOTA效果，是用BertSoftmax方法訓練， 運行examples/training_ner_model_file_demo.py代碼可在各中文數據集復現結果