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：http://yann.lecun.com/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 np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
#载入数据
(x_train,y_train),(x_test,y_test)=mnist.load_data()
#60000,28,28
print('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)/255.0
x_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
model.compile(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 np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from 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)/255.0
x_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
model.compile(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 np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from 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)/255.0
x_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
model.compile(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 np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from 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)/255.0
x_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 func
model.compile(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 np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense,Dropout
from 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)/255.0
x_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 func
model.compile(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 np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from 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)/255.0
x_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 func
model.compile(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 np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.layers.recurrent import SimpleRNN
from 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/255.0
x_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
model.compile(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 np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense,Dropout,Convolution2D,MaxPooling2D,Flatten
from 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)/255.0
x_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'))
# Dropout
model.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
model.compile(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/