DL之DNN：利用DNN算法对mnist手写数字图片识别数据集(sklearn自带,1797*64)训练、预测(95%)
目录
数据集展示
输出结果
设计代码
数据集展示
先查看sklearn自带digits手写数据集(1797*64)
输出结果
设计代码
import numpy as npfrom sklearn.datasets import load_digits from sklearn.metrics import confusion_matrix, classification_report from sklearn.preprocessing import LabelBinarizer from NeuralNetwork import NeuralNetworkfrom sklearn.cross_validation import train_test_split digits = load_digits() X = digits.data y = digits.targetX -= X.min() X /= X.max()nn = NeuralNetwork([64, 100, 10], 'logistic') X_train, X_test, y_train, y_test = train_test_split(X, y) labels_train = LabelBinarizer().fit_transform(y_train)labels_test = LabelBinarizer().fit_transform(y_test)print ("start fitting")nn.fit(X_train, labels_train, epochs=3000) predictions = [] for i in range(X_test.shape[0]): o = nn.predict(X_test[i]) predictions.append(np.argmax(o)) print (confusion_matrix(y_test, predictions) )print (classification_report(y_test, predictions) )
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