keras建模的3种方式详解

import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
from keras.models import Sequential
from keras.models import load_model
from keras.layers import Dense
#加载数据
def read_data(path):
    mnist=input_data.read_data_sets(path,one_hot=True)
    train_x,train_y=mnist.train.images,mnist.train.labels,
    valid_x,valid_y=mnist.validation.images,mnist.validation.labels,
    test_x,test_y=mnist.test.images,mnist.test.labels
    return train_x,train_y,valid_x,valid_y,test_x,test_y
#序列模型
def DNN(train_x,train_y,valid_x,valid_y):
    #創建模型
    model=Sequential()
    model.add(Dense(64,input_dim=784,activation='relu'))
    model.add(Dense(128,activation='relu'))
    model.add(Dense(10,activation='softmax'))
    #查看网络模型
    model.summary()
    #编译模型
    model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
    #训练模型
    model.fit(train_x,train_y,batch_size=500,nb_epoch=100,verbose=1,validation_data=(valid_x,valid_y))
    #保存模型
    model.save('sequential.h5')
train_x,train_y,valid_x,valid_y,test_x,test_y=read_data('MNIST_data')
DNN(train_x,train_y,valid_x,valid_y)
model=load_model('sequential.h5')  #下载模型
pre=model.predict(test_x)  #测试验证
#计算验证集精度
a=np.argmax(pre,1)
b=np.argmax(test_y,1)
t=(a==b).astype(int)
acc=np.sum(t)/len(a)
print(acc)

import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
from keras.models import Model
from keras.models import load_model
from keras.layers import Input,Dense
#加载数据
def read_data(path):
    mnist=input_data.read_data_sets(path,one_hot=True)
    train_x,train_y=mnist.train.images,mnist.train.labels,
    valid_x,valid_y=mnist.validation.images,mnist.validation.labels,
    test_x,test_y=mnist.test.images,mnist.test.labels
    return train_x,train_y,valid_x,valid_y,test_x,test_y
#函数模型
def DNN(train_x,train_y,valid_x,valid_y):
    #创建模型
    inputs=Input(shape=(784,))
    x=Dense(64,activation='relu')(inputs)
    x=Dense(128,activation='relu')(x)
    output=Dense(10,activation='softmax')(x)
    model=Model(input=inputs,output=output)
    #查看网络结构
    model.summary()
    #编译模型
    model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
    #训练模型
    model.fit(train_x,train_y,batch_size=500,nb_epoch=100,verbose=1,validation_data=(valid_x,valid_y))
    #保存模型
    model.save('fun_model.h5')
train_x,train_y,valid_x,valid_y,test_x,test_y=read_data('MNIST_data')
DNN(train_x,train_y,valid_x,valid_y)
model=load_model('fun_model.h5')  #下载模型
pre=model.predict(test_x)  #验证数据集
#验证数据集准确度
a=np.argmax(pre,1)
b=np.argmax(test_y,1)
t=(a==b).astype(int)
acc=np.sum(t)/len(a)
print(acc)

import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
from keras.models import Model
from keras.layers import Dense
#加载数据
def read_data(path):
    mnist=input_data.read_data_sets(path,one_hot=True)
    train_x,train_y=mnist.train.images,mnist.train.labels,
    valid_x,valid_y=mnist.validation.images,mnist.validation.labels,
    test_x,test_y=mnist.test.images,mnist.test.labels
    return train_x,train_y,valid_x,valid_y,test_x,test_y
#子类模型
class DNN(Model):
    def __init__(self,train_x,train_y,valid_x,valid_y):
        super(DNN,self).__init__()
        #初始化网络模型
        self.dense1=Dense(64,input_dim=784,activation='relu')
        self.dense2=Dense(128,activation='relu')
        self.dense3=Dense(10,activation='softmax')
    def call(self,inputs):  #回调順序
        x=self.dense1(inputs)
        x=self.dense2(x)
        x=self.dense3(x)
        return x
train_x,train_y,valid_x,valid_y,test_x,test_y=read_data('MNIST_data')
model=DNN(train_x,train_y,valid_x,valid_y)
#编译模型（学习率、损失函数、模型评估）
model.compile(optimizer='adam(lr=0.001)',loss='categorical_crossentropy',metrics=['accuracy'])
#训练模型
model.fit(train_x,train_y,batch_size=500,nb_epoch=100,verbose=1,validation_data=(valid_x,valid_y))
#查看网络结构
model.summary()
pre=model.predict(test_x)  #验证数据集
#计算验证数据集的准确度
a=np.argmax(pre,1)
b=np.argmax(test_y,1)
t=(a==b).astype(int)
acc=np.sum(t)/len(a)
print(acc)