mnist手写数字识别_手写数字识别
案例：
基于mnist数据集，建立mlp模型，实现0-9数字的十分类任务：
1.实现mnist数据载入，可视化图形数字
2.完成数据预处理：图像数据维度转换与归一化、输出结果格式转换
3.计算模型在预测数据集的准确率
4.模型结构：两层隐藏层，每层有392个神经元
mnist数据集介绍
机器学习领域中非常经典的一个数据集，由60000个训练样本和10000个测试样本组成，每个样本都是一个28*28像素的灰度手写数字图片。一共4个文件，训练集、训练集标签、测试集、测试集标签。
# 加载mnist数据from keras.datasets import mnist(X_train,y_train),(X_test,y_test) = mnist.load_data()but.....网络不行(*^_^*)
#下载mnist.npz文件本地加载import numpy as npf = np.load('mnist.npz')X_train, y_train = f['x_train'], f['y_train']X_test, y_test = f['x_test'], f['y_test']f.close()#查看数据维度print(type(X_train),X_train.shape) (60000, 28, 28)print(type(X_test),X_test.shape) (10000, 28, 28)#可视化训练集第一张图片img1 = X_train[0]%matplotlib inlinefrom matplotlib import pyplot as pltfig1 = plt.figure(figsize=(3,3))plt.imshow(img1)plt.title('image size: 28 X 28')plt.show()
#查看计算机中的格式img1array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0]], dtype=uint8)#输入数据格式化feature_size = img1.shape[0]*img1.shape[1]X_train_format = X_train.reshape(X_train.shape[0],feature_size)X_test_format = X_test.reshape(X_test.shape[0],feature_size)print(X_train_format.shape)(60000, 784)#输入数据归一化X_train_normal = X_train_format/255X_test_normal = X_test_format/255print(X_train_normal[0])[0. 0. 0. 0. 0. 0.
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0. 0. 0. 0. ]#输出数据(标签)格式化from keras.utils import to_categoricaly_train_format = to_categorical(y_train)y_test_format = to_categorical(y_test)print(y_train_format[0])[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]#查看输入数据，输出数据维度print(X_train_normal.shape,y_train_format.shape)(60000, 784) (60000, 10)
#建立模型from keras.models import Sequentialfrom keras.layers import Dense, Activationmlp = Sequential()mlp.add(Dense(units=392,activation='relu',input_dim=784))mlp.add(Dense(units=392,activation='relu'))mlp.add(Dense(units=10,activation='softmax'))mlp.summary()
#模型相关设置mlp.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['categorical_accuracy'])#训练模型mlp.fit(X_train_normal,y_train_format,epochs=10)
#模型预测y_train_predict = mlp.predict_classes(X_train_normal)print(type(y_train_predict))print(y_train_predict[0:10])[5 0 4 1 9 2 1 3 1 4]from sklearn.metrics import accuracy_scoreaccuracy_train = accuracy_score(y_train,y_train_predict)print(accuracy_train)0.9972666666666666y_test_predict = mlp.predict_classes(X_test_normal)accuracy_test = accuracy_score(y_test,y_test_predict)print(accuracy_test)0.9807img2 = X_test[100]fig2 = plt.figure(figsize=(3,3))plt.imshow(img2)plt.title(y_test_predict[100])plt.show()
# coding:utf-8import matplotlib as mlpfont2 = {
'family' : 'SimHei','weight' : 'normal','size' : 20,}mlp.rcParams['font.family'] = 'SimHei'mlp.rcParams['axes.unicode_minus'] = Falsea = [i for i in range(1,10)]fig4 = plt.figure(figsize=(5,5))for i in a: plt.subplot(3,3,i) plt.tight_layout() plt.imshow(X_test[i]) plt.title('predict:{}'.format(y_test_predict[i]),font2) plt.xticks([]) plt.yticks([])