用Keras搭建神经网络 简单模版（二）——Classifier分类（手写数字识别）

# -*- coding: utf-8 -*-
import numpy as np
np.random.seed(1337) #for reproducibility再现性
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential#按层
from keras.layers import Dense, Activation#全连接层
import matplotlib.pyplot as plt
from keras.optimizers import RMSprop

从mnist下载手写数字图片数据集，图片为28*28，将每个像素的颜色（0到255）改为（0倒1），将标签y变为10个长度，若为1，则在1处为1，剩下的都标为0。

#dowmload the mnisst the path '~/.keras/datasets/' if it is the first time to be called
#x shape (60000 28*28),y shape(10000,)
(x_train,y_train),(x_test,y_test) = mnist.load_data()#0-9的图片数据集

#data pre-processing
x_train = x_train.reshape(x_train.shape[0],-1)/255 #normalize 到【0,1】
x_test = x_test.reshape(x_test.shape[0],-1)/255
y_train = np_utils.to_categorical(y_train, num_classes=10) #把标签变为10个长度，若为1，则在1处为1，剩下的都标为0
y_test = np_utils.to_categorical(y_test,num_classes=10)

搭建神经网络，Activation为激活函数。由于第一个Dense传出32.所以第二个的Dense默认传进32，不用特意设置。

#Another way to build neural net
model = Sequential([
        Dense(32,input_dim=784),#传出32
        Activation('relu'),
        Dense(10),
        Activation('softmax')
        ])

#Another way to define optimizer
rmsprop = RMSprop(lr=0.001,rho=0.9,epsilon=1e-08,decay=0.0)

# We add metrics to get more results you want to see
model.compile( #编译
        optimizer = rmsprop,
        loss = 'categorical_crossentropy',
        metrics=['accuracy'], #在更新时同时计算一下accuracy
        )

训练和测试

print("Training~~~~~~~~")
#Another way to train the model
model.fit(x_train,y_train, epochs=2, batch_size=32) #训练2大批，每批32个

print("
Testing~~~~~~~~~~")
#Evalute the model with the  metrics we define earlier
loss,accuracy = model.evaluate(x_test,y_test)

print('test loss:',loss)
print('test accuracy:', accuracy)

全代码：

# -*- coding: utf-8 -*-
import numpy as np
np.random.seed(1337) #for reproducibility再现性
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential#按层
from keras.layers import Dense, Activation#全连接层
import matplotlib.pyplot as plt
from keras.optimizers import RMSprop

#dowmload the mnisst the path '~/.keras/datasets/' if it is the first time to be called
#x shape (60000 28*28),y shape(10000,)
(x_train,y_train),(x_test,y_test) = mnist.load_data()#0-9的图片数据集

#data pre-processing
x_train = x_train.reshape(x_train.shape[0],-1)/255 #normalize 到【0,1】
x_test = x_test.reshape(x_test.shape[0],-1)/255
y_train = np_utils.to_categorical(y_train, num_classes=10) #把标签变为10个长度，若为1，则在1处为1，剩下的都标为0
y_test = np_utils.to_categorical(y_test,num_classes=10)

#Another way to build neural net
model = Sequential([
        Dense(32,input_dim=784),#传出32
        Activation('relu'),
        Dense(10),
        Activation('softmax')
        ])

#Another way to define optimizer
rmsprop = RMSprop(lr=0.001,rho=0.9,epsilon=1e-08,decay=0.0)

# We add metrics to get more results you want to see
model.compile( #编译
        optimizer = rmsprop,
        loss = 'categorical_crossentropy',
        metrics=['accuracy'], #在更新时同时计算一下accuracy
        )

print("Training~~~~~~~~")
#Another way to train the model
model.fit(x_train,y_train, epochs=2, batch_size=32) #训练2大批，每批32个

print("
Testing~~~~~~~~~~")
#Evalute the model with the  metrics we define earlier
loss,accuracy = model.evaluate(x_test,y_test)

print('test loss:',loss)
print('test accuracy:', accuracy)

结果为：