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%)

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%)

---

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 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) )

相关文章
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)