神经网络（tensroflow)

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我是靠谱客的博主可靠八宝粥，最近开发中收集的这篇文章主要介绍神经网络（tensroflow)，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

卷积神经网络

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class Baseline(Model):
	def __init__(self):
		super(Baseline, self).__init__()
		sefl.c1 = Conv2D(filters=6, kernel_size=(5, 5), padding='same')
		self.b1 = BatchNormalization()
		self.a1 = Activation('relu')
		self.p1 = MaxPool2D(pool_size=(2,2), strides=2, padding='same')
		self.d1 = Dropout(0.2)

LeNet

1998年由Yann LeCun提出，卷积神经网络开篇之作，通过共享卷积核减少网络参数。
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class LeNet5(Model):
    def __init__(self):
        super(LeNet5, self).__init__()
        self.c1 = Conv2D(filters=6, kernel_size=(5, 5),
                         activation='sigmoid')
        self.p1 = MaxPool2D(pool_size=(2, 2), strides=2)

        self.c2 = Conv2D(filters=16, kernel_size=(5, 5),
                         activation='sigmoid')
        self.p2 = MaxPool2D(pool_size=(2, 2), strides=2)

        self.flatten = Flatten()
        self.f1 = Dense(120, activation='sigmoid')
        self.f2 = Dense(84, activation='sigmoid')
        self.f3 = Dense(10, activation='softmax')

    def call(self, x):
        x = self.c1(x)
        x = self.p1(x)

        x = self.c2(x)
        x = self.p2(x)
        x = self.flatten(x)
        x = self.f1(x)
        x = self.f2(x)
        y = self.f3(x)
        return y

AlexNet

诞生于2012年，作者Hinton
1.使用relu激活函数提升训练速度
2.使用Dropout缓解了过拟合
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class AlexNet8(Model):
    def __init__(self):
        super(AlexNet8, self).__init__()
        self.c1 = Conv2D(filters=96, kernel_size=(3, 3))
        self.b1 = BatchNormalization()
        self.a1 = Activation('relu')
        self.p1 = MaxPool2D(pool_size=(3, 3), strides=2)

        self.c2 = Conv2D(filters=256, kernel_size=(3, 3))
        self.b2 = BatchNormalization()
        self.a2 = Activation('relu')
        self.p2 = MaxPool2D(pool_size=(3, 3), strides=2)

        self.c3 = Conv2D(filters=384, kernel_size=(3, 3), padding='same',
                         activation='relu')
                         
        self.c4 = Conv2D(filters=384, kernel_size=(3, 3), padding='same',
                         activation='relu')
                         
        self.c5 = Conv2D(filters=256, kernel_size=(3, 3), padding='same',
                         activation='relu')
        self.p3 = MaxPool2D(pool_size=(3, 3