【python-keras深度学习-基本卷积神经网络mnist数字识别】
搭建基本卷积神经网络进行数字识别
mnist数据集中有0-9共10个数字，如何使用卷积神经网络进行识别，除了keras封装好的函数外，还需要进行one-hot编码，将类别特征转化为数值变量，比如我要识别的数字为1，除了1的位置为1，其他9个位置则为0，如此就可以将类别问题转化为识别概率问题。
搭建步骤
1：库的导入
使用keras高级API进行网络搭建时，需要导入必须的包，比如keras，网络使用的模型model，激活函数，优化函数，矩阵计算包，网络层。
from __future__ import print_function
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense,Activation
from keras.optimizers import SGD
from keras.utils import np_utils
np.random.seed(1671)
2.参数设置
需要设置网络的训练轮次，每次训练的批次数据，训练、验证比例等
#参数设置
epoch = 200
batch_size = 128
verbose = 1
NB_CLASSES = 10
OPTIMIZER = SGD()
hidden = 128
val = 0.2
3.数据处理
导入网络中的数据指定不是你要识别的图片，而是图像的矩阵数据（张量），所以需要进行数据处理和归一化，将标签数据转化为二值类别矩阵。
#数据处理
(x_train,y_train),(x_test,y_test) = mnist.load_data()
a=784
x_train = x_train.reshape(60000,a)
x_test = x_test.reshape(10000,a)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /=255
print(x_train.shape[0],'train samples')
print(x_test.shape[0],'test sammples')
y_train = np_utils.to_categorical(y_train,NB_CLASSES)
y_test = np_utils.to_categorical(y_test,NB_CLASSES)
4.模型的建立
数据准备好后，需要进行网络模型的搭建，其中可以按照自己的需求进行网络搭建，比如卷积、池化等。
model = Sequential()
model.add(Dense(NB_CLASSES,input_shape=(a,)))
model.add(Activation('softmax'))
5.查看网络结构，可以看到参数、计算量和网络结构
model.summary()
6.网络模型的编译和运行
#模型编译
model.compile(loss = 'categorical_crossentropy',optimizer = OPTIMIZER,metrics = ['accuracy'])
#运行设置
history = model.fit(x_train,y_train,batch_size = batch_size,epochs = epoch,verbose = verbose,validation_split = val)
score = model.evaluate(x_test,y_test,verbose = verbose)
print("test score:",score[0])
print('test accuracy',score[1])
运行结束，可以查看训练的准确率为0.9225
完整代码
from __future__ import print_function
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense,Activation
from keras.optimizers import SGD
from keras.utils import np_utils
np.random.seed(1671)
#参数设置
epoch = 200
batch_size = 128
verbose = 1
NB_CLASSES = 10
OPTIMIZER = SGD()
hidden = 128
val = 0.2
#数据处理
(x_train,y_train),(x_test,y_test) = mnist.load_data()
a=784
x_train = x_train.reshape(60000,a)
x_test = x_test.reshape(10000,a)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /=255
print(x_train.shape[0],'train samples')
print(x_test.shape[0],'test sammples')
y_train = np_utils.to_categorical(y_train,NB_CLASSES)
y_test = np_utils.to_categorical(y_test,NB_CLASSES)
#模型建立
model = Sequential()
model.add(Dense(NB_CLASSES,input_shape=(a,)))
model.add(Activation('softmax'))
#查看网络结构
model.summary()
#模型编译
model.compile(loss = 'categorical_crossentropy',optimizer = OPTIMIZER,metrics = ['accuracy'])
#运行设置
history = model.fit(x_train,y_train,batch_size = batch_size,epochs = epoch,verbose = verbose,validation_split = val)
score = model.evaluate(x_test,y_test,verbose = verbose)
print("test score:",score[0])
print('test accuracy',score[1])