From 35d6107fc497c769ed83fbf4c8da5f1da6dde2ec Mon Sep 17 00:00:00 2001
From: xiaoting <31891223+tink2123@users.noreply.github.com>
Date: Wed, 5 Jun 2019 14:35:25 +0800
Subject: [PATCH] update 02/09 book img url (#734)
* change img
* change img
---
02.recognize_digits/README.cn.md | 10 +++++-----
02.recognize_digits/image/01.gif | Bin 0 -> 1155 bytes
02.recognize_digits/image/01.png | Bin 16479 -> 0 bytes
02.recognize_digits/image/02.gif | Bin 0 -> 939 bytes
02.recognize_digits/image/02.png | Bin 12768 -> 0 bytes
02.recognize_digits/image/03.gif | Bin 0 -> 1478 bytes
02.recognize_digits/image/03.png | Bin 22126 -> 0 bytes
02.recognize_digits/image/04.gif | Bin 0 -> 955 bytes
02.recognize_digits/image/04.png | Bin 13873 -> 0 bytes
02.recognize_digits/image/05.gif | Bin 0 -> 920 bytes
02.recognize_digits/image/05.png | Bin 13604 -> 0 bytes
02.recognize_digits/index.cn.html | 10 +++++-----
09.gan/README.cn.md | 12 +++++++++++-
09.gan/image/01.gif | Bin 0 -> 2299 bytes
09.gan/index.cn.html | 12 +++++++++++-
15 files changed, 32 insertions(+), 12 deletions(-)
create mode 100644 02.recognize_digits/image/01.gif
delete mode 100644 02.recognize_digits/image/01.png
create mode 100644 02.recognize_digits/image/02.gif
delete mode 100644 02.recognize_digits/image/02.png
create mode 100644 02.recognize_digits/image/03.gif
delete mode 100644 02.recognize_digits/image/03.png
create mode 100644 02.recognize_digits/image/04.gif
delete mode 100644 02.recognize_digits/image/04.png
create mode 100644 02.recognize_digits/image/05.gif
delete mode 100644 02.recognize_digits/image/05.png
create mode 100644 09.gan/image/01.gif
diff --git a/02.recognize_digits/README.cn.md b/02.recognize_digits/README.cn.md
index ab11f7c..70b7abf 100644
--- a/02.recognize_digits/README.cn.md
+++ b/02.recognize_digits/README.cn.md
@@ -46,12 +46,12 @@ MNIST吸引了大量的科学家基于此数据集训练模型,1998年,LeCun
输入层的数据$X$传到输出层,在激活操作之前,会乘以相应的权重 $W$ ,并加上偏置变量 $b$ ,具体如下:
-
+
其中
-
+
图2为softmax回归的网络图,图中权重用蓝线表示、偏置用红线表示、+1代表偏置参数的系数为1。
@@ -66,7 +66,7 @@ MNIST吸引了大量的科学家基于此数据集训练模型,1998年,LeCun
在分类问题中,我们一般采用交叉熵代价损失函数(cross entropy loss),公式如下:
-
+
@@ -135,13 +135,13 @@ Softmax回归模型采用了最简单的两层神经网络,即只有输入层
- sigmoid激活函数:
-
+
- tanh激活函数:
-
+
实际上,tanh函数只是规模变化的sigmoid函数,将sigmoid函数值放大2倍之后再向下平移1个单位:tanh(x) = 2sigmoid(2x) - 1 。
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