機器學習實戰-神經網絡-手寫數字識別項目

# pip install scikit-learn --upgrade from sklearn.neural_network import MLPClassifier from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import classification_report,confusion_matrix digits = load_digits()#載入數據 x_data = digits.data #數據 y_data = digits.target #標簽# 標準化 scaler = StandardScaler() x_data = scaler.fit_transform(x_data) x_train,x_test,y_train,y_test = train_test_split(x_data,y_data) #分割數據1/4為測試數據，3/4為訓練數據 mlp = MLPClassifier(hidden_layer_sizes=(100,50) ,max_iter=500) mlp.fit(x_train,y_train )

predictions = mlp.predict(x_test)

print(confusion_matrix(y_test,predictions))

機器學習實戰-神經網絡-葡萄酒分類

import numpy as np from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier from sklearn.metrics import classification_report,confusion_matrix # 載入數據 data = np.genfromtxt("wine_data.csv", delimiter=",") x_data = data[:,1:] y_data = data[:,0] print(x_data.shape) print(y_data.shape)

# 數據切分 x_train, x_test, y_train, y_test = train_test_split(x_data, y_data) # 數據標準化 scaler = StandardScaler() x_train = scaler.fit_transform(x_train) x_test = scaler.fit_transform(x_test) # 建模 mlp = MLPClassifier(hidden_layer_sizes=(100,50),max_iter=500) # 訓練 mlp.fit(x_train,y_train)

# 評估 predictions = mlp.predict(x_test) print(classification_report(y_test,predictions))

print(confusion_matrix(y_test,predictions))

總結

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