Keras框架神经网络算法训练MNIST分类准确率(实验) CNN-手写体识别

MNIST数据集信息参考：/exdb/mnist/index.html

MNIST是手写数字0~10图片数据集，每一张图片包含28*28个像素。

MNIST训练数据集包含：（1.）60000张图片的像素信息，因为神经网络的输入层表示特征的维度，所以将图像表示成一个[60000,28,28]的pixel张量；（2.）60000张图片的标签信息，表示成一个[60000,10]的矩阵，因为图片的标签是介于0-9的数字，把标签分为10类进行one-hot编码(一个one-hot向量除了某一位数字是1以外，其余维度数字都是0)，比如标签0将表示为([1,0,0,0,0,0,0,0,0,0])。

MNIST测试数据集包含10000张图片的像素信息，同训练数据集一样，将这10000张图片的像素信息，表示成一个[10000,28,28]的张量，将10000张图片的标签表示成[10000,10]的矩阵。

此外，还需要把每张图片的像素信息转化成向量形式，即将[28,28]的矩阵按行拉平成[1,784]的向量，即把数据集转成 [60000, 784]才能放到网络中训练。 第一个维度数字用来索引图片，第二个维度数字用来索引每张图片中的像素点。 一般还需要把图片中的像素数组归一化为0-1之间。

1. 修改模型编译compile信息

import numpy as npfrom keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.optimizers import SGD#载入数据(x_train,y_train),(x_test,y_test)=mnist.load_data()#60000,28,28print('x_shape:',x_train.shape)print('y_shape:',y_train.shape)#Output:#x_shape: (60000, 28, 28)#y_shape: (60000,)#重新定义数据格式#60000,784及归一化x_train=x_train.reshape(x_train.shape[0],-1)/255.0x_test=x_test.reshape(x_test.shape[0],-1)/255.0#转one-hot标签y_train=np_utils.to_categorical(y_train,num_classes=10)y_test=np_utils.to_categorical(y_test,num_classes=10)#创建模型,输入784个神经元,输出10个神经元model=Sequential([Dense(units=10,input_dim=784,bias='one',activation='softmax')])#定义优化器sgd=SGD(lr=0.2)#定义loss pile(optimizer=sgd,loss='mse',metrics=['accuracy'])#训练模型model.fit(x_train,y_train,batch_size=32,epochs=10)#评估模型loss,accuracy=model.evaluate(x_test,y_test)print('loss:',loss)print('accuracy:',accuracy)

1.1. 修改目标函数

loss=’mse’ ——> loss=’categorical_crossentropy’

import numpy as npfrom keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.optimizers import SGD#载入数据(x_train,y_train),(x_test,y_test)=mnist.load_data()#重新定义数据格式#60000,784及归一化x_train=x_train.reshape(x_train.shape[0],-1)/255.0x_test=x_test.reshape(x_test.shape[0],-1)/255.0#转one-hot标签y_train=np_utils.to_categorical(y_train,num_classes=10)y_test=np_utils.to_categorical(y_test,num_classes=10)#创建模型,输入784个神经元,输出10个神经元model=Sequential([Dense(units=10,input_dim=784,bias='one',activation='softmax')])#定义优化器sgd=SGD(lr=0.2)#定义loss func#crossentropy比mse收敛快pile(optimizer=sgd,loss='categorical_crossentropy',metrics=['accuracy'])#训练模型model.fit(x_train,y_train,batch_size=32,epochs=10)#评估模型loss,accuracy=model.evaluate(x_test,y_test)print('loss:',loss)print('accuracy:',accuracy)

1.2. 修改优化器

optimizer=sgd ——> optimizer=adam

import numpy as npfrom keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.optimizers import SGD,Adam #导入不同优化器#载入数据(x_train,y_train),(x_test,y_test)=mnist.load_data()#重新定义数据格式#60000,784及归一化x_train=x_train.reshape(x_train.shape[0],-1)/255.0x_test=x_test.reshape(x_test.shape[0],-1)/255.0#转one-hot标签y_train=np_utils.to_categorical(y_train,num_classes=10)y_test=np_utils.to_categorical(y_test,num_classes=10)#创建模型,输入784个神经元,输出10个神经元model=Sequential([Dense(units=10,input_dim=784,bias='one',activation='softmax')])#定义优化器sgd=SGD(lr=0.2)adam=Adam(lr=0.001)#定义loss func#crossentropy比mse收敛快pile(optimizer=adam,loss='categorical_crossentropy',metrics=['accuracy'])#训练模型model.fit(x_train,y_train,batch_size=32,epochs=10)#评估模型loss,accuracy=model.evaluate(x_test,y_test)print('loss:',loss)print('accuracy:',accuracy)

2. 修改模型网络层信息

import numpy as npfrom keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.optimizers import SGD#载入数据(x_train,y_train),(x_test,y_test)=mnist.load_data()#重新定义数据格式#60000,784及归一化x_train=x_train.reshape(x_train.shape[0],-1)/255.0x_test=x_test.reshape(x_test.shape[0],-1)/255.0#转one-hot标签y_train=np_utils.to_categorical(y_train,num_classes=10)y_test=np_utils.to_categorical(y_test,num_classes=10)#创建模型,输入784个神经元,输出10个神经元model=Sequential([Dense(units=200,input_dim=784,bias='one',activation='tanh'),Dense(units=100,bias='one',activation='tanh'),Dense(units=10,bias='one',activation='softmax')])#定义优化器sgd=SGD(lr=0.2)#定义loss pile(optimizer=sgd,loss='mse',metrics=['accuracy'])#训练模型model.fit(x_train,y_train,batch_size=32,epochs=10)#评估模型loss,accuracy=model.evaluate(x_test,y_test)print('test loss:',loss)print('test accuracy:',accuracy)print('train loss:',loss)print('train accuracy:',accuracy)

2.1. 防止过拟合，选用Dropout策略

import numpy as npfrom keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Dense,Dropoutfrom keras.optimizers import SGD#载入数据(x_train,y_train),(x_test,y_test)=mnist.load_data()#重新定义数据格式#60000,784及归一化x_train=x_train.reshape(x_train.shape[0],-1)/255.0x_test=x_test.reshape(x_test.shape[0],-1)/255.0#转one-hot标签y_train=np_utils.to_categorical(y_train,num_classes=10)y_test=np_utils.to_categorical(y_test,num_classes=10)#创建模型,输入784个神经元,输出10个神经元(Dropout=0.4表示让前一层40%神经元不工作)model=Sequential([Dense(units=200,input_dim=784,bias='one',activation='tanh'),Dropout(0.4),Dense(units=100,bias='one',activation='tanh'),Dropout(0.4),Dense(units=10,bias='one',activation='softmax')])#定义优化器sgd=SGD(lr=0.2)#定义loss pile(optimizer=sgd,loss='mse',metrics=['accuracy'])#训练模型model.fit(x_train,y_train,batch_size=32,epochs=10)#评估模型loss,accuracy=model.evaluate(x_test,y_test)print('test loss:',loss)print('test accuracy:',accuracy)print('train loss:',loss)print('train accuracy:',accuracy)

2.2. 防止过拟合，选用正则化方法

import numpy as npfrom keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.optimizers import SGDfrom keras.regularizers import l2#载入数据(x_train,y_train),(x_test,y_test)=mnist.load_data()#重新定义数据格式#60000,784及归一化x_train=x_train.reshape(x_train.shape[0],-1)/255.0x_test=x_test.reshape(x_test.shape[0],-1)/255.0#转one-hot标签y_train=np_utils.to_categorical(y_train,num_classes=10)y_test=np_utils.to_categorical(y_test,num_classes=10)#创建模型,输入784个神经元,输出10个神经元model=Sequential([Dense(units=200,input_dim=784,bias='one',activation='tanh',kernel_regularizer=l2(0.003)),Dense(units=100,bias='one',activation='tanh',kernel_regularizer=l2(0.003)),Dense(units=10,bias='one',activation='softmax',kernel_regularizer=l2(0.003))])#定义优化器sgd=SGD(lr=0.2)#定义loss pile(optimizer=sgd,loss='mse',metrics=['accuracy'])#训练模型model.fit(x_train,y_train,batch_size=32,epochs=10)#评估模型loss,accuracy=model.evaluate(x_test,y_test)print('test loss:',loss)print('test accuracy:',accuracy)print('train loss:',loss)print('train accuracy:',accuracy)

3. 更优模型探索

3.1. RNN

import numpy as npfrom keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.layers.recurrent import SimpleRNNfrom keras.optimizers import Adam# 数据长度-一行有28个像素input_size = 28# 序列长度-一共有28行time_steps = 28# 隐藏层cell个数cell_size = 50 # 载入数据(x_train,y_train),(x_test,y_test) = mnist.load_data()# (60000,28,28)x_train = x_train/255.0x_test = x_test/255.0# 换one hot格式y_train = np_utils.to_categorical(y_train,num_classes=10)y_test = np_utils.to_categorical(y_test,num_classes=10)#one hot# 创建模型model = Sequential()# 循环神经网络model.add(SimpleRNN(units = cell_size, # 输出input_shape = (time_steps,input_size), #输入))# 输出层model.add(Dense(10,activation='softmax'))# 定义优化器adam = Adam(lr=1e-4)# 定义优化器，loss function，训练过程中计算准确率pile(optimizer=adam,loss='categorical_crossentropy',metrics=['accuracy'])# 训练模型model.fit(x_train,y_train,batch_size=64,epochs=10)# 评估模型loss,accuracy = model.evaluate(x_test,y_test)print('test loss',loss)print('test accuracy',accuracy)

3.2. CNN

import numpy as npfrom keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Dense,Dropout,Convolution2D,MaxPooling2D,Flattenfrom keras.optimizers import Adam# 载入数据(x_train,y_train),(x_test,y_test) = mnist.load_data()# (60000,28,28)->(60000,28,28,1)x_train = x_train.reshape(-1,28,28,1)/255.0x_test = x_test.reshape(-1,28,28,1)/255.0# 换one hot格式y_train = np_utils.to_categorical(y_train,num_classes=10)y_test = np_utils.to_categorical(y_test,num_classes=10)# 定义顺序模型model = Sequential()# 第一个卷积层# input_shape 输入平面# filters 卷积核/滤波器个数# kernel_size 卷积窗口大小# strides 步长# padding padding方式 same/valid# activation 激活函数model.add(Convolution2D(input_shape = (28,28,1),filters = 32,kernel_size = 5,strides = 1,padding = 'same',activation = 'relu'))# 第一个池化层model.add(MaxPooling2D(pool_size = 2,strides = 2,padding = 'same',))# 第二个卷积层model.add(Convolution2D(64,5,strides=1,padding='same',activation = 'relu'))# 第二个池化层model.add(MaxPooling2D(2,2,'same'))# 把第二个池化层的输出扁平化为1维model.add(Flatten())# 第一个全连接层model.add(Dense(1024,activation = 'relu'))# Dropoutmodel.add(Dropout(0.5))# 第二个全连接层model.add(Dense(10,activation='softmax'))# 定义优化器adam = Adam(lr=1e-4)# 定义优化器，loss function，训练过程中计算准确率pile(optimizer=adam,loss='categorical_crossentropy',metrics=['accuracy'])# 训练模型model.fit(x_train,y_train,batch_size=64,epochs=10)# 评估模型loss,accuracy = model.evaluate(x_test,y_test)print('test loss',loss)print('test accuracy',accuracy)

手写数字复杂网络层抽特征可视化工具http://scs.ryerson.ca/~aharley/vis/conv/