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  • tensorflow笔记

    tensoflow笔记

    from tensorflow.keras.models import Sequential
    from tensorflow.keras.layers import Input, Dense, Activation, Model
    
    #方法一:
    layers = [Dense(32, input_shape = (784,)),
       Activation('relu'),
       Dense(10),
       Activation('softmax')]
    model = Sequential(layers)
    
    #方法二:
    model = Sequential()
    model.add(Dense(32, input_shape = (784,)))
    model.add(Activation('relu'))
    model.add(Dense(10))
    model.add(Activation('softmax'))
    
    #方法三:
    # 定义输入层,确定输入维度
    input = Input(shape = (784, ))
    # 2个隐含层,每个都有64个神经元,使用relu激活函数,且由上一层作为参数
    x = Dense(64, activation='relu')(input)
    x = Dense(64, activation='relu')(x)
    # 输出层
    y = Dense(10, activation='softmax')(x)
    # 定义模型,指定输入输出
    model = Model(input=input, output=y)
    # 编译模型,指定优化器,损失函数,度量
    model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
    # 模型拟合,即训练
    model.fit(data, labels)
    

      

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  • 原文地址:https://www.cnblogs.com/iupoint/p/14579534.html
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