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
先查看sklearn自带digits手写数据集(1797*64)
- import numpy as np
- from sklearn.datasets import load_digits
- from sklearn.metrics import confusion_matrix, classification_report
- from sklearn.preprocessing import LabelBinarizer
- from NeuralNetwork import NeuralNetwork
- from sklearn.cross_validation import train_test_split
- digits = load_digits()
- X = digits.data
- y = digits.target
- X -= 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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DL之NN:调用神经网络sklearn、NeuralNetwor.py实现识别手写的10个数字(数据集为1797个样本)
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