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0001-keras规范类的基本模型

0001-keras规范类的基本模型

作者: 小新学算法 | 来源:发表于2019-08-20 14:05 被阅读0次

    代码如下

    from keras.layers import Input, Dense
    from keras.models import Model
    from keras import models
    class Arg:
        input_size = 784
        #模型参数
        #训练参数
        epochs = 1000    
        #度量参数    
    class LinearModel:
        def __init__(self,args):
            #参数
            self.input_size = args.input_size
            self.epochs = args.epochs
            #创建和编译模型
            self.model = self._build_model()        
        def _build_model(self):             
            inputs = Input(shape=(self.input_size,))        
            x = Dense(64, activation='relu')(inputs)
            x = Dense(64, activation='relu')(x)
            predictions = Dense(10, activation='softmax')(x)        
            model = Model(inputs=inputs, outputs=predictions)
            model.compile(optimizer='rmsprop',loss='categorical_crossentropy', metrics=['accuracy'])        
            return model       
        
        def fit(self,X,y):
            history = self.model.fit(X,y,epochs=self.epochs)
            return history        
        def predict(self,X):
            return self.model.predict(X)              
        def save_model(self,path):
            return self.model.save(path)        
        def load_model(self,path):
            return models.load_model(path)    
        def load_model_structure(self):
            return self.model.summary()
            
    if __name__ == "__main__":
        from keras.utils import to_categorical    
        import numpy as np
        X = np.linspace(start=20, stop=100, num=100*784).reshape(100,784)
        y = np.random.randint(10,size=(100,))
        y = to_categorical(y,num_classes=10)    
        args = Arg()    
        model = LinearModel(args)    
        history = model.fit(X,y)    
        y_hat = model.predict(X)    
        model.save_model("H:/model.h5")
        model = model.load_model("H:/model.h5")
        model.predict(X)        
        ```
        
    

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