今天的深度学习训练我们要练习一下让计算机识别手写数字。其说是深度学习，我们搭建的神经层只有两层，输入层，输出层。但不要小看只有两层，我们的正确率已经达到了90+%。
废话不多说，先看代码
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras import optimizers
from keras.datasets import mnist
from keras.utils.np_utils import to_categorical
import matplotlib.pyplot as plt
# collecting the data
(x_train, y_train), (x_test, y_test) = mnist.load_data('mnist.pkl.gz')
x = x_test[6]/125
x_train = x_train.reshape(60000, 784)/125
x_test = x_test.reshape(x_test.shape[0], 784)/125
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
# design the model
model = Sequential()
model.add(Dense(32, input_dim=784))
model.add(Activation('relu'))
model.add(Dense(10))
model.add(Activation('softmax'))
# compile the model and pick the loss function and the optimizer
rms = optimizers.RMSprop(lr=0.01, epsilon=1e-8, rho=0.9)
model.compile(optimizer=rms, loss='categorical_crossentropy', metrics=['accuracy'])
# training the model
model.fit(x_train, y_train, batch_size=20, epochs=2, shuffle=True)
# test the model
score = model.evaluate(x_test, y_test, batch_size=500)
h = model.predict_classes(x.reshape(1, -1), batch_size=1)
print 'loss:\t', score[0], '\naccuracy:\t', score[1]
print '\nclass:\t', h
plt.imshow(x)
plt.show()
好的，老生常谈先说第一段代码
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras import optimizers
from keras.datasets import mnist
from keras.utils.np_utils import to_categorical
import matplotlib.pyplot as plt
- 在前几个文章中提到的，我们就不说了，因为前几篇文章介绍的很详细。如果有那些我没有说的，但没看懂请点击一下链接。线性回归|第一天的Keras、分类训练|第二天的Keras、多元分类|第三天的Keras.
- 好了，今天新导入的模块是mnist，这个模块是keras的数据集。我们倒入这个模块是为了获取mnist的训练数据，和测试数据。下面代码会详解。
- 另一个新的模块是to_categorical,这是一个函数，主要是把，我们的标签转换成一个二维向量。例如我们一共有5类，我们的标签分别是0，1，2，3，4。通过to_categorical我们就会把标签转换成下面的形式【0，0，0，0，1】（代表标签4）
- 还有一个新的模块 matplotlib.pyplot。matplotlib是一个python的包，主要是绘制表格等使用。我们今天用它来可视化我们的训练数据。建议安装此包是下载anaconda，管理工具。这样可以很好的解决依赖问题，特别是在windows下。一般我们google，或者baidu都可以，看到如何安装anaconda，或者直接看官网。当然pip安装matplotlib也可以。
收集数据
# collecting the data
(x_train, y_train), (x_test, y_test) = mnist.load_data('mnist.pkl.gz')
x = x_test[6]
x_train = x_train.reshape(60000, 784)/125
x_test = x_test.reshape(x_test.shape[0], 784)/125
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
- 通过mnist.load_data()我们可以获得我们需要的训练数据。这里有个东西需要注意一下。（）里面的参数可以不填，但第一次我的下载失败了，文件破损，所以我就改为他的另一个数据包的名称。他的包一共有两个。另一个‘mnist.pnz'
- 因为我们得到的数据的shape是（60000，28，28）x_train。所以我们要改变一下其shape让其适合训练。自然是改称28*28维的向量了。所以reshape（60000，784）60000是指有60000个量，784是指每个量都是784维的。x_test也类似。x_test.shape[0]也就是原来的量的个数。
- 还有一点我们为什么要除上125呢？其实除不除，除多少看个人心意吧，因为x_train,每个元素都是介于0～255之间的数，为了训练起来方便我除上125，就变成了0～2之间的数字。
- 下一个我们用到的就是to_categorical了。上面已经说清楚了，就不废话了。
设计我们的模型
# design the model
model = Sequential()
model.add(Dense(32, input_dim=784))
model.add(Activation('relu'))
model.add(Dense(10))
model.add(Activation('softmax'))
好了，没什么新意。我前面集篇文章都讲的比较多。
选取优化器、损失函数，并编译我们的模型
# compile the model and pick the loss function and the optimizer
rms = optimizers.RMSprop(lr=0.01, epsilon=1e-8, rho=0.9)
model.compile(optimizer=rms, loss='categorical_crossentropy', metrics=['accuracy'])
- 这里唯一一个比较新的是我们选择了一个新的优化器RMSprop。详情请阅读优化器比较
训练我们的模型
# training the model
model.fit(x_train, y_train, batch_size=20, epochs=2, shuffle=True)
同样跟前几篇文章没什么不同。另外提议下batch_size的意义，就是加速我们的训练（GPU），增加更新次数。
测试我们的模型
# test the model
score = model.evaluate(x_test, y_test, batch_size=500)
h = model.predict_classes(x.reshape(1, -1), batch_size=1)
print 'loss:\t', score[0], '\naccuracy:\t', score[1]
print '\nclass:\t', h
plt.imshow(x)
plt.show()
- 首先evaluate一下我们的训练结果，得到了loss，和accuracy。
- 然后使用predict_classes（），输入一个图片，并将此图片shape成可输入的矩阵，并经过相同的预处理。注意的一点是这里我们reshape有一个参数是-1，什么意思呢？就是把数组分成x个量，每个量多少维根据数组大小自行调整。还是不理解事不。举个例子。
>>> data
array([[ 0.21033279, 0.50292265, 0.71271164, 0.47520358, 0.73701694,
0.82117798, 0.44008419, 0.59581689, 0.07051911, 0.57959824,
0.76565127, 0.51984695, 0.27255042, 0.59663595, 0.48586599,
0.234282 , 0.71937941, 0.99956487, 0.46895412, 0.37871936,
0.81054667, 0.09787709, 0.14693726, 0.81571586, 0.08852998,
0.73211671, 0.29407735, 0.37332085, 0.00451808, 0.60411745,
0.20248406, 0.36436494, 0.25961514, 0.22623853, 0.66947677,
0.54229594, 0.49394167, 0.47603329, 0.90753314, 0.04755629,
0.22703817, 0.69693293, 0.07821929, 0.14584769, 0.69374338,
0.22148599, 0.64267874, 0.79070401, 0.91767048, 0.95359906,
0.17062022, 0.64134807, 0.35871884, 0.86993997, 0.63867876,
0.39333417, 0.06902379, 0.68998664, 0.78482029, 0.94321673]])
>>> data.reshape(20,-1)
array([[ 0.21033279, 0.50292265, 0.71271164],
[ 0.47520358, 0.73701694, 0.82117798],
[ 0.44008419, 0.59581689, 0.07051911],
[ 0.57959824, 0.76565127, 0.51984695],
[ 0.27255042, 0.59663595, 0.48586599],
[ 0.234282 , 0.71937941, 0.99956487],
[ 0.46895412, 0.37871936, 0.81054667],
[ 0.09787709, 0.14693726, 0.81571586],
[ 0.08852998, 0.73211671, 0.29407735],
[ 0.37332085, 0.00451808, 0.60411745],
[ 0.20248406, 0.36436494, 0.25961514],
[ 0.22623853, 0.66947677, 0.54229594],
[ 0.49394167, 0.47603329, 0.90753314],
[ 0.04755629, 0.22703817, 0.69693293],
[ 0.07821929, 0.14584769, 0.69374338],
[ 0.22148599, 0.64267874, 0.79070401],
[ 0.91767048, 0.95359906, 0.17062022],
[ 0.64134807, 0.35871884, 0.86993997],
[ 0.63867876, 0.39333417, 0.06902379],
[ 0.68998664, 0.78482029, 0.94321673]])
>>> data.reshape(20,3)
array([[ 0.21033279, 0.50292265, 0.71271164],
[ 0.47520358, 0.73701694, 0.82117798],
[ 0.44008419, 0.59581689, 0.07051911],
[ 0.57959824, 0.76565127, 0.51984695],
[ 0.27255042, 0.59663595, 0.48586599],
[ 0.234282 , 0.71937941, 0.99956487],
[ 0.46895412, 0.37871936, 0.81054667],
[ 0.09787709, 0.14693726, 0.81571586],
[ 0.08852998, 0.73211671, 0.29407735],
[ 0.37332085, 0.00451808, 0.60411745],
[ 0.20248406, 0.36436494, 0.25961514],
[ 0.22623853, 0.66947677, 0.54229594],
[ 0.49394167, 0.47603329, 0.90753314],
[ 0.04755629, 0.22703817, 0.69693293],
[ 0.07821929, 0.14584769, 0.69374338],
[ 0.22148599, 0.64267874, 0.79070401],
[ 0.91767048, 0.95359906, 0.17062022],
[ 0.64134807, 0.35871884, 0.86993997],
[ 0.63867876, 0.39333417, 0.06902379],
[ 0.68998664, 0.78482029, 0.94321673]])
- 然后下面就是把我们的数据打印出来。
- plt 是先用plt.imshow()把我们的图片传进取，然后通过plt.show()展示出来。
效果如下：
4
好了，看看运行的结果
/home/kroossun/miniconda2/bin/python /home/kroossun/PycharmProjects/ML/matp.py
Using TensorFlow backend.
Epoch 1/2
2017-09-07 12:57:55.376184: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-07 12:57:55.376250: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-07 12:57:55.376256: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-09-07 12:57:55.376260: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-07 12:57:55.376265: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
20/60000 [..............................] - ETA: 400s - loss: 2.5463 - acc: 0.0500
920/60000 [..............................] - ETA: 11s - loss: 1.1999 - acc: 0.6359
1740/60000 [..............................] - ETA: 7s - loss: 0.9247 - acc: 0.7195
2620/60000 [>.............................] - ETA: 6s - loss: 0.8059 - acc: 0.7637
3540/60000 [>.............................] - ETA: 5s - loss: 0.7262 - acc: 0.7870
4460/60000 [=>............................] - ETA: 4s - loss: 0.6716 - acc: 0.8043
5400/60000 [=>............................] - ETA: 4s - loss: 0.6472 - acc: 0.8128
6180/60000 [==>...........................] - ETA: 4s - loss: 0.6289 - acc: 0.8215
7020/60000 [==>...........................] - ETA: 4s - loss: 0.6008 - acc: 0.8296
7860/60000 [==>...........................] - ETA: 3s - loss: 0.5804 - acc: 0.8340
8760/60000 [===>..........................] - ETA: 3s - loss: 0.5643 - acc: 0.8388
9660/60000 [===>..........................] - ETA: 3s - loss: 0.5418 - acc: 0.8450
10440/60000 [====>.........................] - ETA: 3s - loss: 0.5324 - acc: 0.8489
11260/60000 [====>.........................] - ETA: 3s - loss: 0.5189 - acc: 0.8529
12160/60000 [=====>........................] - ETA: 3s - loss: 0.5135 - acc: 0.8552
13040/60000 [=====>........................] - ETA: 3s - loss: 0.5084 - acc: 0.8571
13820/60000 [=====>........................] - ETA: 3s - loss: 0.5000 - acc: 0.8607
14600/60000 [======>.......................] - ETA: 3s - loss: 0.4921 - acc: 0.8634
15420/60000 [======>.......................] - ETA: 3s - loss: 0.4859 - acc: 0.8654
16280/60000 [=======>......................] - ETA: 2s - loss: 0.4855 - acc: 0.8663
17040/60000 [=======>......................] - ETA: 2s - loss: 0.4778 - acc: 0.8684
17860/60000 [=======>......................] - ETA: 2s - loss: 0.4710 - acc: 0.8704
18720/60000 [========>.....................] - ETA: 2s - loss: 0.4685 - acc: 0.8715
19600/60000 [========>.....................] - ETA: 2s - loss: 0.4651 - acc: 0.8728
20500/60000 [=========>....................] - ETA: 2s - loss: 0.4612 - acc: 0.8741
21280/60000 [=========>....................] - ETA: 2s - loss: 0.4577 - acc: 0.8752
22100/60000 [==========>...................] - ETA: 2s - loss: 0.4530 - acc: 0.8767
22840/60000 [==========>...................] - ETA: 2s - loss: 0.4513 - acc: 0.8780
23680/60000 [==========>...................] - ETA: 2s - loss: 0.4479 - acc: 0.8793
24420/60000 [===========>..................] - ETA: 2s - loss: 0.4466 - acc: 0.8802
25260/60000 [===========>..................] - ETA: 2s - loss: 0.4435 - acc: 0.8811
26140/60000 [============>.................] - ETA: 2s - loss: 0.4414 - acc: 0.8826
27000/60000 [============>.................] - ETA: 2s - loss: 0.4388 - acc: 0.8833
27800/60000 [============>.................] - ETA: 2s - loss: 0.4363 - acc: 0.8842
28640/60000 [=============>................] - ETA: 2s - loss: 0.4352 - acc: 0.8848
29460/60000 [=============>................] - ETA: 1s - loss: 0.4356 - acc: 0.8856
30340/60000 [==============>...............] - ETA: 1s - loss: 0.4324 - acc: 0.8867
31120/60000 [==============>...............] - ETA: 1s - loss: 0.4331 - acc: 0.8870
31900/60000 [==============>...............] - ETA: 1s - loss: 0.4311 - acc: 0.8878
32780/60000 [===============>..............] - ETA: 1s - loss: 0.4303 - acc: 0.8884
33660/60000 [===============>..............] - ETA: 1s - loss: 0.4289 - acc: 0.8890
34400/60000 [================>.............] - ETA: 1s - loss: 0.4281 - acc: 0.8894
35240/60000 [================>.............] - ETA: 1s - loss: 0.4281 - acc: 0.8900
36100/60000 [=================>............] - ETA: 1s - loss: 0.4247 - acc: 0.8910
36980/60000 [=================>............] - ETA: 1s - loss: 0.4238 - acc: 0.8916
37860/60000 [=================>............] - ETA: 1s - loss: 0.4221 - acc: 0.8922
38660/60000 [==================>...........] - ETA: 1s - loss: 0.4219 - acc: 0.8927
39460/60000 [==================>...........] - ETA: 1s - loss: 0.4205 - acc: 0.8932
40320/60000 [===================>..........] - ETA: 1s - loss: 0.4184 - acc: 0.8941
41180/60000 [===================>..........] - ETA: 1s - loss: 0.4185 - acc: 0.8943
41940/60000 [===================>..........] - ETA: 1s - loss: 0.4176 - acc: 0.8948
42760/60000 [====================>.........] - ETA: 1s - loss: 0.4163 - acc: 0.8953
43660/60000 [====================>.........] - ETA: 1s - loss: 0.4173 - acc: 0.8955
44540/60000 [=====================>........] - ETA: 0s - loss: 0.4172 - acc: 0.8957
45340/60000 [=====================>........] - ETA: 0s - loss: 0.4156 - acc: 0.8961
46160/60000 [======================>.......] - ETA: 0s - loss: 0.4157 - acc: 0.8964
47040/60000 [======================>.......] - ETA: 0s - loss: 0.4153 - acc: 0.8967
47860/60000 [======================>.......] - ETA: 0s - loss: 0.4138 - acc: 0.8970
48720/60000 [=======================>......] - ETA: 0s - loss: 0.4125 - acc: 0.8975
49480/60000 [=======================>......] - ETA: 0s - loss: 0.4124 - acc: 0.8977
50300/60000 [========================>.....] - ETA: 0s - loss: 0.4121 - acc: 0.8981
51160/60000 [========================>.....] - ETA: 0s - loss: 0.4101 - acc: 0.8984
52060/60000 [=========================>....] - ETA: 0s - loss: 0.4081 - acc: 0.8990
52840/60000 [=========================>....] - ETA: 0s - loss: 0.4077 - acc: 0.8992
53680/60000 [=========================>....] - ETA: 0s - loss: 0.4062 - acc: 0.8997
54520/60000 [==========================>...] - ETA: 0s - loss: 0.4061 - acc: 0.9002
55380/60000 [==========================>...] - ETA: 0s - loss: 0.4063 - acc: 0.9004
56160/60000 [===========================>..] - ETA: 0s - loss: 0.4062 - acc: 0.9007
56980/60000 [===========================>..] - ETA: 0s - loss: 0.4055 - acc: 0.9011
57820/60000 [===========================>..] - ETA: 0s - loss: 0.4036 - acc: 0.9015
58680/60000 [============================>.] - ETA: 0s - loss: 0.4025 - acc: 0.9018
59480/60000 [============================>.] - ETA: 0s - loss: 0.4015 - acc: 0.9022
60000/60000 [==============================] - 3s - loss: 0.4007 - acc: 0.9025
Epoch 2/2
20/60000 [..............................] - ETA: 179s - loss: 0.4713 - acc: 0.9500
840/60000 [..............................] - ETA: 7s - loss: 0.2679 - acc: 0.9369
1580/60000 [..............................] - ETA: 5s - loss: 0.2831 - acc: 0.9411
2360/60000 [>.............................] - ETA: 5s - loss: 0.2772 - acc: 0.9445
3160/60000 [>.............................] - ETA: 4s - loss: 0.2998 - acc: 0.9383
3980/60000 [>.............................] - ETA: 4s - loss: 0.2976 - acc: 0.9384
4820/60000 [=>............................] - ETA: 4s - loss: 0.3141 - acc: 0.9349
5620/60000 [=>............................] - ETA: 4s - loss: 0.3278 - acc: 0.9324
6420/60000 [==>...........................] - ETA: 3s - loss: 0.3158 - acc: 0.9335
7280/60000 [==>...........................] - ETA: 3s - loss: 0.3245 - acc: 0.9327
8140/60000 [===>..........................] - ETA: 3s - loss: 0.3245 - acc: 0.9323
8920/60000 [===>..........................] - ETA: 3s - loss: 0.3175 - acc: 0.9337
9740/60000 [===>..........................] - ETA: 3s - loss: 0.3217 - acc: 0.9332
10620/60000 [====>.........................] - ETA: 3s - loss: 0.3181 - acc: 0.9339
11420/60000 [====>.........................] - ETA: 3s - loss: 0.3248 - acc: 0.9331
12160/60000 [=====>........................] - ETA: 3s - loss: 0.3259 - acc: 0.9334
12960/60000 [=====>........................] - ETA: 3s - loss: 0.3226 - acc: 0.9334
13760/60000 [=====>........................] - ETA: 3s - loss: 0.3196 - acc: 0.9342
14620/60000 [======>.......................] - ETA: 3s - loss: 0.3173 - acc: 0.9342
15400/60000 [======>.......................] - ETA: 2s - loss: 0.3161 - acc: 0.9343
16200/60000 [=======>......................] - ETA: 2s - loss: 0.3158 - acc: 0.9348
17060/60000 [=======>......................] - ETA: 2s - loss: 0.3131 - acc: 0.9349
17940/60000 [=======>......................] - ETA: 2s - loss: 0.3118 - acc: 0.9347
18660/60000 [========>.....................] - ETA: 2s - loss: 0.3111 - acc: 0.9350
19460/60000 [========>.....................] - ETA: 2s - loss: 0.3125 - acc: 0.9352
20320/60000 [=========>....................] - ETA: 2s - loss: 0.3119 - acc: 0.9351
21200/60000 [=========>....................] - ETA: 2s - loss: 0.3120 - acc: 0.9350
21980/60000 [=========>....................] - ETA: 2s - loss: 0.3161 - acc: 0.9347
22820/60000 [==========>...................] - ETA: 2s - loss: 0.3150 - acc: 0.9350
23680/60000 [==========>...................] - ETA: 2s - loss: 0.3112 - acc: 0.9352
24560/60000 [===========>..................] - ETA: 2s - loss: 0.3115 - acc: 0.9352
25300/60000 [===========>..................] - ETA: 2s - loss: 0.3120 - acc: 0.9353
26100/60000 [============>.................] - ETA: 2s - loss: 0.3147 - acc: 0.9350
26940/60000 [============>.................] - ETA: 2s - loss: 0.3171 - acc: 0.9348
27760/60000 [============>.................] - ETA: 2s - loss: 0.3148 - acc: 0.9351
28600/60000 [=============>................] - ETA: 2s - loss: 0.3145 - acc: 0.9352
29360/60000 [=============>................] - ETA: 1s - loss: 0.3153 - acc: 0.9352
30200/60000 [==============>...............] - ETA: 1s - loss: 0.3134 - acc: 0.9354
31040/60000 [==============>...............] - ETA: 1s - loss: 0.3150 - acc: 0.9355
31880/60000 [==============>...............] - ETA: 1s - loss: 0.3152 - acc: 0.9356
32680/60000 [===============>..............] - ETA: 1s - loss: 0.3176 - acc: 0.9353
33520/60000 [===============>..............] - ETA: 1s - loss: 0.3156 - acc: 0.9357
34400/60000 [================>.............] - ETA: 1s - loss: 0.3133 - acc: 0.9358
35200/60000 [================>.............] - ETA: 1s - loss: 0.3155 - acc: 0.9356
35920/60000 [================>.............] - ETA: 1s - loss: 0.3152 - acc: 0.9357
36740/60000 [=================>............] - ETA: 1s - loss: 0.3147 - acc: 0.9358
37620/60000 [=================>............] - ETA: 1s - loss: 0.3134 - acc: 0.9360
38500/60000 [==================>...........] - ETA: 1s - loss: 0.3164 - acc: 0.9358
39320/60000 [==================>...........] - ETA: 1s - loss: 0.3164 - acc: 0.9357
40120/60000 [===================>..........] - ETA: 1s - loss: 0.3171 - acc: 0.9358
40940/60000 [===================>..........] - ETA: 1s - loss: 0.3193 - acc: 0.9356
41780/60000 [===================>..........] - ETA: 1s - loss: 0.3197 - acc: 0.9356
42660/60000 [====================>.........] - ETA: 1s - loss: 0.3196 - acc: 0.9358
43440/60000 [====================>.........] - ETA: 1s - loss: 0.3201 - acc: 0.9357
44200/60000 [=====================>........] - ETA: 0s - loss: 0.3180 - acc: 0.9362
45040/60000 [=====================>........] - ETA: 0s - loss: 0.3167 - acc: 0.9365
45900/60000 [=====================>........] - ETA: 0s - loss: 0.3178 - acc: 0.9365
46700/60000 [======================>.......] - ETA: 0s - loss: 0.3183 - acc: 0.9366
47480/60000 [======================>.......] - ETA: 0s - loss: 0.3194 - acc: 0.9366
48380/60000 [=======================>......] - ETA: 0s - loss: 0.3191 - acc: 0.9369
49240/60000 [=======================>......] - ETA: 0s - loss: 0.3203 - acc: 0.9367
50000/60000 [========================>.....] - ETA: 0s - loss: 0.3198 - acc: 0.9368
50820/60000 [========================>.....] - ETA: 0s - loss: 0.3203 - acc: 0.9369
51640/60000 [========================>.....] - ETA: 0s - loss: 0.3193 - acc: 0.9370
52480/60000 [=========================>....] - ETA: 0s - loss: 0.3198 - acc: 0.9369
53200/60000 [=========================>....] - ETA: 0s - loss: 0.3194 - acc: 0.9370
54000/60000 [==========================>...] - ETA: 0s - loss: 0.3207 - acc: 0.9368
54840/60000 [==========================>...] - ETA: 0s - loss: 0.3211 - acc: 0.9370
55720/60000 [==========================>...] - ETA: 0s - loss: 0.3240 - acc: 0.9368
56600/60000 [===========================>..] - ETA: 0s - loss: 0.3253 - acc: 0.9365
57380/60000 [===========================>..] - ETA: 0s - loss: 0.3260 - acc: 0.9365
58200/60000 [============================>.] - ETA: 0s - loss: 0.3252 - acc: 0.9365
59040/60000 [============================>.] - ETA: 0s - loss: 0.3242 - acc: 0.9366
59840/60000 [============================>.] - ETA: 0s - loss: 0.3244 - acc: 0.9366
60000/60000 [==============================] - 3s - loss: 0.3243 - acc: 0.9366
500/10000 [>.............................] - ETA: 0s
1/1 [==============================] - 0s
loss: 0.348073045909
accuracy: 0.941699999571
class: [4]
可以看到，我们的图片是4识别出来也是4，另外在测试集上的正确是0.94也是很不错了呢，毕竟我们只有两层神经元啊。
网友评论