先吐槽一下这个基于theano的keras有多难装，反正我是在windows下折腾到不行，所以自己装了一个双系统。这才感到linux系统的强大之初，难怪大公司都是用这个做开发，妹的，谁用谁知道啊！！！！
先来介绍一下这个框架：我们都知道深度的神经网络，Python一开始有theano这个框架用来写神经网络，不过后来我们发现keras这个比theano更加容易构建，很适合初学者。×..×
以下是对应的英文网站：http://keras.io/#installation，英文好的话自己都可以看懂了。
一：先看安装
有两种：
１．ubuntu下直接用 sudo pip install keras 安装
２．又或者先安装以下的依赖项：
numpy, scipypyyamlTheanoHDF5 and h5py
Once you have the dependencies installed, clone the repo:
git clone https://github.com/fchollet/keras.git
二.模块简介
１．optimizers:
这个是用来选用优化方法的，里面有SGD，Adagrad，Adadelta，RMSprop，Adam可选
２．objectives 这个定义了用什么形式的误差来优化，有
mean_squared_error / mse：平均方差
mean_absolute_error / mae：绝对误差
mean_absolute_percentage_error / mape：平均绝对百分差
mean_squared_logarithmic_error / msle：对数误差
squared_hinge
hinge
binary_crossentropy: Also known as logloss.
categorical_crossentropy:使用这个目标函数需要设置label为二进制数组的形式。
３．model
model = keras.models.Sequential() 初始化一个神经网络
add 添加一层神经网
compile(optimizer, loss, class_mode=”categorical”):
参数：
optimizer: str (优化函数的名称) 或者优化对象.参考 optimizers.
loss: str (目标函数的名称) 或者目标函数. 参考 objectives.
class_mode: 值为”categorical”, “binary”. 用于计算分类正确率或调用 predict_classes方法.
theano_mode: A theano.compile.mode.Mode (reference).
fit(X, y, batch_size=128, nb_epoch=100, verbose=1, validation_split=0., validation_data =None, shuffle=True, show_accuracy=False): 固定的 epochs训练一个模型.
返回值：记录在字典中的训练成功的损失值，也可是验证损失值或精确度（适用的话）.
参数：
X: data.
y: labels.
batch_size: int. 每一次迭代的样本数目.
nb_epoch: int.
verbose: 0 表示不更新日志, 1 更新日志, 2 每个epoch一个进度行.
validation_split: float (0 < x < 1).验证集的一部分.
validation_data: tuple (X, y) 数据作为验证集. 将加载validation_split.
shuffle: boolean. 每个 epoch是否随机抽取样本.
show_accuracy: boolean. 每个epoch是否显示分类正确率.
evaluate(X, y, batch_size=128, show_accuracy=False, verbose=1): 通过验证集的数据显示model的性能.
Return: 返回数据的损失值.
Arguments: 和上面fit函数定义相同. verbose用作二进制标识(进度条或无).
predict(X, batch_size=128, verbose=1):
Return: 测试数据的预测数组.
Arguments: 和fit一样.
predict_classes(X, batch_size=128, verbose=1): 返回test data的类预测数组.
Return: 测试数据的标签数组.
Arguments: 和fit一样.
train(X, y, accuracy=False): 一个batch的梯度更新. if accuracy==False, return tuple (loss_on_batch, accuracy_on_batch). Else, return loss_on_batch.
Return: 损失值, 或者tuple (loss, accuracy) if accuracy=True.
test(X, y, accuracy=False): 一个batch的性能计算. if accuracy==False, return tuple (loss_on_batch, accuracy_on_batch). Else, return loss_on_batch.
Return: 损失值, 或 tuple (loss, accuracy) if accuracy=True.
save_weights(fname):保存所有层的权值到HDF5文件中.
load_weights(fname): 加载保存在save_weights中模型权值. 只能加载相同结构的文件.
下面是自己写的一个小程序
<code class="hljs python has-numbering" style="display: block; padding: 0px; color: inherit; box-sizing: border-box; font-family: 'Source Code Pro', monospace;font-size:undefined; white-space: pre; border-radius: 0px; word-wrap: normal; background: transparent;"><span class="hljs-comment" style="color: rgb(136, 0, 0); box-sizing: border-box;">#coding:utf-8</span><span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'''Created on 2015-9-12@author: zzq2015'''</span><span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">from</span> keras.models <span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">import</span> Sequential<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">from</span> keras.layers.core <span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">import</span> Dense, Dropout, Activation<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">import</span> scipy.io <span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">as</span> sio <span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">import</span> numpy <span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">as</span> np model = Sequential()model.add(Dense(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">4</span>, <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">200</span>, init=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'uniform'</span>))model.add(Activation(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'relu'</span>))model.add(Dropout(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0.5</span>))model.add(Dense(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">200</span>, <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">100</span>, init=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'uniform'</span>))model.add(Activation(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'relu'</span>))model.add(Dropout(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0.5</span>))model.add(Dense(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">100</span>, <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">50</span>, init=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'uniform'</span>))model.add(Activation(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'relu'</span>))model.add(Dropout(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0.5</span>))model.add(Dense(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">50</span>, <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">20</span>, init=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'uniform'</span>))model.add(Activation(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'relu'</span>))model.add(Dropout(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0.5</span>))model.add(Dense(<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">20</span>, <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">3</span>, init=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'uniform'</span>))model.add(Activation(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'softmax'</span>))model.compile(loss=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'binary_crossentropy'</span>, optimizer=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'adam'</span>, class_mode=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">"binary"</span>)matfn=<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">u'/media/zzq2015/学习/python/da/kerasTrain.mat'</span>data=sio.loadmat(matfn) data = np.array(data.get(<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'iris_train'</span>))trainDa = data[:<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">80</span>,:<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">4</span>]trainBl = data[:<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">80</span>,<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">4</span>:]testDa = data[<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">80</span>:,:<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">4</span>]testBl = data[<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">80</span>:,<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">4</span>:] model.fit(trainDa, trainBl, nb_epoch=<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">80</span>, batch_size=<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">20</span>)<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">print</span> model.evaluate(testDa, testBl, show_accuracy=<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">True</span>)<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">print</span> model.predict_classes(testDa)<span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">print</span> <span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">'真实标签：\n'</span><span class="hljs-keyword" style="color: rgb(0, 0, 136); box-sizing: border-box;">print</span> testBl</code><ul class="pre-numbering" style="box-sizing: border-box; position: absolute; width: 50px; top: 0px; left: 0px; margin: 0px; padding: 6px 0px 40px; border-right-width: 1px; border-right-style: solid; border-right-color: rgb(221, 221, 221); list-style: none; text-align: right; background-color: rgb(238, 238, 238);"><li style="box-sizing: border-box; padding: 0px 5px;">1</li><li style="box-sizing: border-box; padding: 0px 5px;">2</li><li style="box-sizing: border-box; padding: 0px 5px;">3</li><li style="box-sizing: border-box; padding: 0px 5px;">4</li><li style="box-sizing: border-box; padding: 0px 5px;">5</li><li style="box-sizing: border-box; padding: 0px 5px;">6</li><li style="box-sizing: border-box; padding: 0px 5px;">7</li><li style="box-sizing: border-box; padding: 0px 5px;">8</li><li style="box-sizing: border-box; padding: 0px 5px;">9</li><li style="box-sizing: border-box; padding: 0px 5px;">10</li><li style="box-sizing: border-box; padding: 0px 5px;">11</li><li style="box-sizing: border-box; padding: 0px 5px;">12</li><li style="box-sizing: border-box; padding: 0px 5px;">13</li><li style="box-sizing: border-box; padding: 0px 5px;">14</li><li style="box-sizing: border-box; padding: 0px 5px;">15</li><li style="box-sizing: border-box; padding: 0px 5px;">16</li><li style="box-sizing: border-box; padding: 0px 5px;">17</li><li style="box-sizing: border-box; padding: 0px 5px;">18</li><li style="box-sizing: border-box; padding: 0px 5px;">19</li><li style="box-sizing: border-box; padding: 0px 5px;">20</li><li style="box-sizing: border-box; padding: 0px 5px;">21</li><li style="box-sizing: border-box; padding: 0px 5px;">22</li><li style="box-sizing: border-box; padding: 0px 5px;">23</li><li style="box-sizing: border-box; padding: 0px 5px;">24</li><li style="box-sizing: border-box; padding: 0px 5px;">25</li><li style="box-sizing: border-box; padding: 0px 5px;">26</li><li style="box-sizing: border-box; padding: 0px 5px;">27</li><li style="box-sizing: border-box; padding: 0px 5px;">28</li><li style="box-sizing: border-box; padding: 0px 5px;">29</li><li style="box-sizing: border-box; padding: 0px 5px;">30</li><li style="box-sizing: border-box; padding: 0px 5px;">31</li><li style="box-sizing: border-box; padding: 0px 5px;">32</li><li style="box-sizing: border-box; padding: 0px 5px;">33</li><li style="box-sizing: border-box; padding: 0px 5px;">34</li><li style="box-sizing: border-box; padding: 0px 5px;">35</li><li style="box-sizing: border-box; padding: 0px 5px;">36</li><li style="box-sizing: border-box; padding: 0px 5px;">37</li><li style="box-sizing: border-box; padding: 0px 5px;">38</li><li style="box-sizing: border-box; padding: 0px 5px;">39</li><li style="box-sizing: border-box; padding: 0px 5px;">40</li><li style="box-sizing: border-box; padding: 0px 5px;">41</li><li style="box-sizing: border-box; padding: 0px 5px;">42</li></ul><ul class="pre-numbering" style="box-sizing: border-box; position: absolute; width: 50px; top: 0px; left: 0px; margin: 0px; padding: 6px 0px 40px; border-right-width: 1px; border-right-style: solid; border-right-color: rgb(221, 221, 221); list-style: none; text-align: right; background-color: rgb(238, 238, 238);"><li style="box-sizing: border-box; padding: 0px 5px;">1</li><li style="box-sizing: border-box; padding: 0px 5px;">2</li><li style="box-sizing: border-box; padding: 0px 5px;">3</li><li style="box-sizing: border-box; padding: 0px 5px;">4</li><li style="box-sizing: border-box; padding: 0px 5px;">5</li><li style="box-sizing: border-box; padding: 0px 5px;">6</li><li style="box-sizing: border-box; padding: 0px 5px;">7</li><li style="box-sizing: border-box; padding: 0px 5px;">8</li><li style="box-sizing: border-box; padding: 0px 5px;">9</li><li style="box-sizing: border-box; padding: 0px 5px;">10</li><li style="box-sizing: border-box; padding: 0px 5px;">11</li><li style="box-sizing: border-box; padding: 0px 5px;">12</li><li style="box-sizing: border-box; padding: 0px 5px;">13</li><li style="box-sizing: border-box; padding: 0px 5px;">14</li><li style="box-sizing: border-box; padding: 0px 5px;">15</li><li style="box-sizing: border-box; padding: 0px 5px;">16</li><li style="box-sizing: border-box; padding: 0px 5px;">17</li><li style="box-sizing: border-box; padding: 0px 5px;">18</li><li style="box-sizing: border-box; padding: 0px 5px;">19</li><li style="box-sizing: border-box; padding: 0px 5px;">20</li><li style="box-sizing: border-box; padding: 0px 5px;">21</li><li style="box-sizing: border-box; padding: 0px 5px;">22</li><li style="box-sizing: border-box; padding: 0px 5px;">23</li><li style="box-sizing: border-box; padding: 0px 5px;">24</li><li style="box-sizing: border-box; padding: 0px 5px;">25</li><li style="box-sizing: border-box; padding: 0px 5px;">26</li><li style="box-sizing: border-box; padding: 0px 5px;">27</li><li style="box-sizing: border-box; padding: 0px 5px;">28</li><li style="box-sizing: border-box; padding: 0px 5px;">29</li><li style="box-sizing: border-box; padding: 0px 5px;">30</li><li style="box-sizing: border-box; padding: 0px 5px;">31</li><li style="box-sizing: border-box; padding: 0px 5px;">32</li><li style="box-sizing: border-box; padding: 0px 5px;">33</li><li style="box-sizing: border-box; padding: 0px 5px;">34</li><li style="box-sizing: border-box; padding: 0px 5px;">35</li><li style="box-sizing: border-box; padding: 0px 5px;">36</li><li style="box-sizing: border-box; padding: 0px 5px;">37</li><li style="box-sizing: border-box; padding: 0px 5px;">38</li><li style="box-sizing: border-box; padding: 0px 5px;">39</li><li style="box-sizing: border-box; padding: 0px 5px;">40</li><li style="box-sizing: border-box; padding: 0px 5px;">41</li><li style="box-sizing: border-box; padding: 0px 5px;">42</li></ul>
输出结果如下：
<code class="hljs lua has-numbering" style="display: block; padding: 0px; color: inherit; box-sizing: border-box; font-family: 'Source Code Pro', monospace;font-size:undefined; white-space: pre; border-radius: 0px; word-wrap: normal; background: transparent;">Epoch <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">79</span><span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">20</span>/<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">80</span> [======>.......................] - ETA: <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0</span>s - loss: <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0.1042</span><span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">40</span>/<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">80</span> [==============>...............] - ETA: <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0</span>s - loss: <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0.0857</span><span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">60</span>/<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">80</span> [=====================>........] - ETA: <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0</span>s - loss: <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0.0826</span><span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">80</span>/<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">80</span> [==============================] - <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0</span>s - loss: <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0.1216</span> <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">10</span>/<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">10</span> [==============================] - <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0</span>s[<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0.15986641560148043</span>, <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">1.0</span>]<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">10</span>/<span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">10</span> [==============================] - <span class="hljs-number" style="color: rgb(0, 102, 102); box-sizing: border-box;">0</span>s<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">[[0 0 1] [0 0 1] [0 0 1] [0 0 1] [0 0 1] [0 0 1] [0 0 1] [0 0 1] [0 0 1] [0 0 1]]</span>真实标签：<span class="hljs-string" style="color: rgb(0, 136, 0); box-sizing: border-box;">[[ 0. 0. 1.] [ 0. 0. 1.] [ 0. 0. 1.] [ 0. 0. 1.] [ 0. 0. 1.] [ 0. 0. 1.] [ 0. 0. 1.] [ 0. 0. 1.] [ 0. 0. 1.] [ 0. 0. 1.]]</span></code><ul class="pre-numbering" style="box-sizing: border-box; position: absolute; width: 50px; top: 0px; left: 0px; margin: 0px; padding: 6px 0px 40px; border-right-width: 1px; border-right-style: solid; border-right-color: rgb(221, 221, 221); list-style: none; text-align: right; background-color: rgb(238, 238, 238);"><li style="box-sizing: border-box; padding: 0px 5px;">1</li><li style="box-sizing: border-box; padding: 0px 5px;">2</li><li style="box-sizing: border-box; padding: 0px 5px;">3</li><li style="box-sizing: border-box; padding: 0px 5px;">4</li><li style="box-sizing: border-box; padding: 0px 5px;">5</li><li style="box-sizing: border-box; padding: 0px 5px;">6</li><li style="box-sizing: border-box; padding: 0px 5px;">7</li><li style="box-sizing: border-box; padding: 0px 5px;">8</li><li style="box-sizing: border-box; padding: 0px 5px;">9</li><li style="box-sizing: border-box; padding: 0px 5px;">10</li><li style="box-sizing: border-box; padding: 0px 5px;">11</li><li style="box-sizing: border-box; padding: 0px 5px;">12</li><li style="box-sizing: border-box; padding: 0px 5px;">13</li><li style="box-sizing: border-box; padding: 0px 5px;">14</li><li style="box-sizing: border-box; padding: 0px 5px;">15</li><li style="box-sizing: border-box; padding: 0px 5px;">16</li><li style="box-sizing: border-box; padding: 0px 5px;">17</li><li style="box-sizing: border-box; padding: 0px 5px;">18</li><li style="box-sizing: border-box; padding: 0px 5px;">19</li><li style="box-sizing: border-box; padding: 0px 5px;">20</li><li style="box-sizing: border-box; padding: 0px 5px;">21</li><li style="box-sizing: border-box; padding: 0px 5px;">22</li><li style="box-sizing: border-box; padding: 0px 5px;">23</li><li style="box-sizing: border-box; padding: 0px 5px;">24</li><li style="box-sizing: border-box; padding: 0px 5px;">25</li><li style="box-sizing: border-box; padding: 0px 5px;">26</li><li style="box-sizing: border-box; padding: 0px 5px;">27</li><li style="box-sizing: border-box; padding: 0px 5px;">28</li><li style="box-sizing: border-box; padding: 0px 5px;">29</li><li style="box-sizing: border-box; padding: 0px 5px;">30</li><li style="box-sizing: border-box; padding: 0px 5px;">31</li><li style="box-sizing: border-box; padding: 0px 5px;">32</li><li style="box-sizing: border-box; padding: 0px 5px;">33</li></ul><ul class="pre-numbering" style="box-sizing: border-box; position: absolute; width: 50px; top: 0px; left: 0px; margin: 0px; padding: 6px 0px 40px; border-right-width: 1px; border-right-style: solid; border-right-color: rgb(221, 221, 221); list-style: none; text-align: right; background-color: rgb(238, 238, 238);"><li style="box-sizing: border-box; padding: 0px 5px;">1</li><li style="box-sizing: border-box; padding: 0px 5px;">2</li><li style="box-sizing: border-box; padding: 0px 5px;">3</li><li style="box-sizing: border-box; padding: 0px 5px;">4</li><li style="box-sizing: border-box; padding: 0px 5px;">5</li><li style="box-sizing: border-box; padding: 0px 5px;">6</li><li style="box-sizing: border-box; padding: 0px 5px;">7</li><li style="box-sizing: border-box; padding: 0px 5px;">8</li><li style="box-sizing: border-box; padding: 0px 5px;">9</li><li style="box-sizing: border-box; padding: 0px 5px;">10</li><li style="box-sizing: border-box; padding: 0px 5px;">11</li><li style="box-sizing: border-box; padding: 0px 5px;">12</li><li style="box-sizing: border-box; padding: 0px 5px;">13</li><li style="box-sizing: border-box; padding: 0px 5px;">14</li><li style="box-sizing: border-box; padding: 0px 5px;">15</li><li style="box-sizing: border-box; padding: 0px 5px;">16</li><li style="box-sizing: border-box; padding: 0px 5px;">17</li><li style="box-sizing: border-box; padding: 0px 5px;">18</li><li style="box-sizing: border-box; padding: 0px 5px;">19</li><li style="box-sizing: border-box; padding: 0px 5px;">20</li><li style="box-sizing: border-box; padding: 0px 5px;">21</li><li style="box-sizing: border-box; padding: 0px 5px;">22</li><li style="box-sizing: border-box; padding: 0px 5px;">23</li><li style="box-sizing: border-box; padding: 0px 5px;">24</li><li style="box-sizing: border-box; padding: 0px 5px;">25</li><li style="box-sizing: border-box; padding: 0px 5px;">26</li><li style="box-sizing: border-box; padding: 0px 5px;">27</li><li style="box-sizing: border-box; padding: 0px 5px;">28</li><li style="box-sizing: border-box; padding: 0px 5px;">29</li><li style="box-sizing: border-box; padding: 0px 5px;">30</li><li style="box-sizing: border-box; padding: 0px 5px;">31</li><li style="box-sizing: border-box; padding: 0px 5px;">32</li><li style="box-sizing: border-box; padding: 0px 5px;">33</li></ul>
0.15是损失值，１是准确率
原文链接：http://blog.csdn.net/star_bob/article/details/48598417
