From 00865d7f367dc9ad16344df441c041630508f5e4 Mon Sep 17 00:00:00 2001 From: foochane Date: Thu, 14 Mar 2019 23:04:01 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B7=B1=E5=BA=A6=E5=AD=A6=E4=B9=A0=E7=9A=84?= =?UTF-8?q?=E5=AE=9E=E7=94=A8=E5=B1=82=E9=9D=A2=20(Practical=20aspects=20o?= =?UTF-8?q?f=20Deep=20Learning)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...spects of Deep Learning)-checkpoint.ipynb" | 356 +++++++++++++++++- ...Practical aspects of Deep Learning).ipynb" | 356 +++++++++++++++++- image/0410.png | Bin 0 -> 10680 bytes image/0411.png | Bin 0 -> 53719 bytes image/0412.png | Bin 0 -> 208748 bytes image/0413.png | Bin 0 -> 166486 bytes image/0414.png | Bin 0 -> 35083 bytes image/0415.png | Bin 0 -> 38446 bytes image/0416.png | Bin 0 -> 103095 bytes image/0417.png | Bin 0 -> 36216 bytes image/0418.png | Bin 0 -> 3695 bytes image/0419.png | Bin 0 -> 22018 bytes image/0420.png | Bin 0 -> 7415 bytes 13 files changed, 710 insertions(+), 2 deletions(-) create mode 100644 image/0410.png create mode 100644 image/0411.png create mode 100644 image/0412.png create mode 100644 image/0413.png create mode 100644 image/0414.png create mode 100644 image/0415.png create mode 100644 image/0416.png create mode 100644 image/0417.png create mode 100644 image/0418.png create mode 100644 image/0419.png create mode 100644 image/0420.png diff --git "a/02 \346\224\271\345\226\204\346\267\261\345\261\202\347\245\236\347\273\217\347\275\221\347\273\234\357\274\232\350\266\205\345\217\202\346\225\260\350\260\203\350\257\225\343\200\201\346\255\243\345\210\231\345\214\226\344\273\245\345\217\212\344\274\230\345\214\226/.ipynb_checkpoints/01\347\254\254\344\270\200\345\221\250 \346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\345\256\236\347\224\250\345\261\202\351\235\242 (Practical aspects of Deep Learning)-checkpoint.ipynb" "b/02 \346\224\271\345\226\204\346\267\261\345\261\202\347\245\236\347\273\217\347\275\221\347\273\234\357\274\232\350\266\205\345\217\202\346\225\260\350\260\203\350\257\225\343\200\201\346\255\243\345\210\231\345\214\226\344\273\245\345\217\212\344\274\230\345\214\226/.ipynb_checkpoints/01\347\254\254\344\270\200\345\221\250 \346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\345\256\236\347\224\250\345\261\202\351\235\242 (Practical aspects of Deep Learning)-checkpoint.ipynb" index f4f53c9..aa05317 100644 --- "a/02 \346\224\271\345\226\204\346\267\261\345\261\202\347\245\236\347\273\217\347\275\221\347\273\234\357\274\232\350\266\205\345\217\202\346\225\260\350\260\203\350\257\225\343\200\201\346\255\243\345\210\231\345\214\226\344\273\245\345\217\212\344\274\230\345\214\226/.ipynb_checkpoints/01\347\254\254\344\270\200\345\221\250 \346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\345\256\236\347\224\250\345\261\202\351\235\242 (Practical aspects of Deep Learning)-checkpoint.ipynb" +++ "b/02 \346\224\271\345\226\204\346\267\261\345\261\202\347\245\236\347\273\217\347\275\221\347\273\234\357\274\232\350\266\205\345\217\202\346\225\260\350\260\203\350\257\225\343\200\201\346\255\243\345\210\231\345\214\226\344\273\245\345\217\212\344\274\230\345\214\226/.ipynb_checkpoints/01\347\254\254\344\270\200\345\221\250 \346\267\261\345\272\246\345\255\246\344\271\240\347\232\204\345\256\236\347\224\250\345\261\202\351\235\242 (Practical aspects of Deep Learning)-checkpoint.ipynb" @@ -86,7 +86,361 @@ "\n", "因此,选择合适大小的λλ值,就能够同时避免high bias和high variance,得到最佳模型。\n", "\n", - "还有另外一个直观的例子来解释为什么正则化能够避免发生过拟合。假设激活函数是tanh函数。tanh函数的特点是在z接近零的区域,函数近似是线性的,而当|z|很大的时候,函数非线性且变化缓慢。当使用正则化,λ较大,即对权重$w^{[l]}$的惩罚较大,$w^{[l]}$减小。因为$z^{[l]}=w^{[l]}a^{[l]}+b^{[l]}$。当$w^{[l]}$减小的时候,$z^{[l]}$也会减小。则此时的$z^{[l]}$分布在tanh函数的近似线性区域。那么这个神经元起的作用就相当于是linear regression。如果每个神经元对应的权重$w^{[l]}$都比较小,那么整个神经网络模型相当于是多个linear regression的组合,即可看成一个linear network。得到的分类超平面就会比较简单,不会出现过拟合现象。\n" + "还有另外一个直观的例子来解释为什么正则化能够避免发生过拟合。假设激活函数是tanh函数。tanh函数的特点是在z接近零的区域,函数近似是线性的,而当|z|很大的时候,函数非线性且变化缓慢。当使用正则化,λ较大,即对权重$w^{[l]}$的惩罚较大,$w^{[l]}$减小。因为$z^{[l]}=w^{[l]}a^{[l]}+b^{[l]}$。当$w^{[l]}$减小的时候,$z^{[l]}$也会减小。则此时的$z^{[l]}$分布在tanh函数的近似线性区域。那么这个神经元起的作用就相当于是linear regression。如果每个神经元对应的权重$w^{[l]}$都比较小,那么整个神经网络模型相当于是多个linear regression的组合,即可看成一个linear network。得到的分类超平面就会比较简单,不会出现过拟合现象。\n", + "\n", + "![](../image/0410.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.6 dropout 正则化(Dropout Regularization)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "除了L2 regularization之外,还有另外一种防止过拟合的有效方法:Dropout。\n", + "\n", + "Dropout是指在深度学习网络的训练过程中,对于每层的神经元,按照一定的概率将其暂时从网络中丢弃。也就是说,每次训练时,每一层都有部分神经元不工作,起到简化复杂网络模型的效果,从而避免发生过拟合。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](../image/0411.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dropout有不同的实现方法,接下来介绍一种常用的方法:Inverted dropout。假设对于第ll层神经元,设定保留神经元比例概率keep_prob=0.8,即该层有20%的神经元停止工作。dldl为dropout向量,设置dldl为随机vector,其中80%的元素为1,20%的元素为0。在python中可以使用如下语句生成dropout vector:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "dl = np.random.rand(al.shape[0],al.shape[1])`Ytcp}|yUwwXfa zdCGjA{ky(D&vmZvpYL4f{K3V3*M8r3t@S+jbKm!~!tbF}D9YWl#18yOM@|a5!E~>BS`R)5!#;e`mjMsRgeZ3JU5$j08#(bT;ncpwj5 z7L)b9v==V$=V_%UP_an1uK)g;R2 z9LMqM-G9r&WpVsExfTt|h|}90bYuuFm8kfzFf#u8A42Gb3`9HkALMH*$;kR0dAXP{ zBiP8a*b(!-zcdoBwqY7h)C*|I2=g6}X%TG>OM^wF7HzaiGH7g}VI|6Ab!=gWhafC8 zftQ-Sd{)AFO8z4EgE}Ag;R?IN?S(!S-NHv4TXS7|2b+jH|9SFAdw(pJIYTG+ZtTI% z653Y{;TW`zl*XEUCCR^^6)H2xXjNoT@u&80^9zbLUwydhXxL~z((7jJStLB^f9mUE zOcj7+mkva@Y|rkeR(!5{e>B^!68i$UEw%B^4IGV0TD^-{?^>eMmQaBw*To# zZ>5uIfnBnkzn@g`crBt<$%~LxKs!6kexg?Y{Vk`flh3za|Jv#gTBp2tGk$ZnqoYgi zRIti*F2Q^2uRhHSt6%Sj!^V2DRM5T)@Eglh9YlSHuJ-mMaZP&L!3D&BrcRgsW~|Ic=mQ0|(h zW~juRF;XR(wIV>u_n^E^mu>0fXn%{*qW&8dCi&Uc$mnbrW@A9lRPFmK268Pqc4-3S 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