版权声明：站在巨人的肩膀上学习。 https://blog.csdn.net/zgcr654321/article/details/83957923
我们使用mnist数据集，这个数据集有手写体数字0-9的图片，一共10类，我们对这个数据集中的手写体数字图片进行分类。
如果mnist数据集无法自动下载，可能是因为from keras.datasets import mnist自动下载数据集的网址被墙，请手动下载并按下面代码中注释进行相应修改。
这次我们不使用model.add一层一层添加神经层，而是直接在模型的里面定义所有的神经层。即：
model = Sequential([Dense(32, input_dim=784), Activation('relu'), Dense(10), Activation('softmax'), ])然后定义优化器：
# 优化器选择RMSprop，lr=0.001
rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)计算loss函数和准确率：
# 计算loss函数和准确率，metrics里面可以放入需要计算的cost，accuracy，score等。
model.compile(optimizer=rmsprop, loss='categorical_crossentropy', metrics=['accuracy'])完整代码如下：
# 如果是第一次在keras中用mnist数据集，则会下载这个数据集的包
from keras.datasets import mnist
import numpy as np
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import RMSprop
import os
os.environ['KERAS_BACKEND'] = 'tensorflow'
# 指定backend框架为tensorflow
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# 想要不出现红字提示，则需要上面这一句
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# 指定使用的GPU的编号，从0开始，如果使用tensorflow-gpu环境编译则需要上面这句
# mnist数据集的x数据为60,000*784的形式，我们需要手动除以255进行标准化，因为每个像素都是在 0 到 255 之间
# mnist数据集的y数据要用到Keras改造的 numpy 的一个函数np_utils.to_categorical，把y数据变成one-hot的形式
# 即之前y数据是一个数值，在0-9之间，现在是一个大小为10的向量，它属于哪个数字，就在哪个位置为 1，其他位置都是 0。
# 如果下载数据集失败，是因为网址被墙了。
# 手动从这里下载数据集：下载链接：https://pan.baidu.com/s/1jH6uFFC 密码: dw3d 把下载好的数据集放到.py所在目录下。
# 然后添加path='./mnist.npz'
# 再添加mnist_data = np.load(path)
# (X_train, y_train), (X_test, y_test) = mnist.load_data()改为
# X_train, y_train = mnist_data['x_train'], mnist_data['y_train']
# X_test, y_test = mnist_data['x_test'], mnist_data['y_test']
# 注意读取完后关闭文件mnist_data.close()
path = './mnist.npz'
mnist_data = np.load(path)
x_train, y_train = mnist_data['x_train'], mnist_data['y_train']
x_test, y_test = mnist_data['x_test'], mnist_data['y_test']
mnist_data.close()
# X_train的shape=(60000, 28, 28)，y_train的shape=(60000,)
# X_test的shape=(10000, 28, 28)，y_train的shape=(10000,)
# print(X_train.shape, y_train.shape)
# print(X_test.shape, y_test.shape)
# 对x_train和x_test的数据进行标准化
x_train = x_train.reshape(x_train.shape[0], -1) / 255.
x_test = x_test.reshape(x_test.shape[0], -1) / 255.
# 对y_train和y_test的数据进行标准化
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)
# 标准化之后
# X_train的shape=(60000, 784)，y_train的shape=(60000, 10)
# X_test的shape=(10000, 784)，y_train的shape=(10000,10)
# print(X_train.shape, y_train.shape)
# print(X_test.shape, y_test.shape)
# 这时x数据和y数据就和tensorflow中下载的mnist数据集中的数据形式一样了
# 直接在模型的里面添加多个神经层
# 加入Dense神经层，32是输出的维度，784是输入的维度。
# 第一层传出的数据有32个 feature，传给激励单元，激励函数用到的是relu函数。 经过激励函数之后，就变成了非线性的数据。
# 然后再把这个数据传给下一个Dense神经层，这个Dense层我们定义它有10个输出的feature。 接下来再将数据输入给softmax函数，用来分类。
model = Sequential([Dense(32, input_dim=784), Activation('relu'), Dense(10), Activation('softmax'), ])
# 优化器选择RMSprop，lr=0.001
rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
# 计算loss函数和准确率
# metrics里面可以放入需要计算的cost，accuracy，score等。
model.compile(optimizer=rmsprop, loss='categorical_crossentropy', metrics=['accuracy'])
# 训练网络
np.random.seed(1337)
# epochs表示把整个数据集的样本使用多少次，batch_size每批32个数据样本
model.fit(x_train, y_train, epochs=2, batch_size=32)
# 测试网络
loss, accuracy = model.evaluate(x_test, y_test)
print('test loss:{} test accuracy:{} '.format(loss, accuracy))运行结果如下：
Using TensorFlow backend.
Epoch 1/2
32/60000 [..............................] - ETA: 1:13:41 - loss: 2.5772 - acc: 0.0312
256/60000 [..............................] - ETA: 9:22 - loss: 2.2214 - acc: 0.2188
512/60000 [..............................] - ETA: 4:45 - loss: 2.0312 - acc: 0.3770
800/60000 [..............................] - ETA: 3:06 - loss: 1.8802 - acc: 0.4512
1056/60000 [..............................] - ETA: 2:23 - loss: 1.7750 - acc: 0.5000
1344/60000 [..............................] - ETA: 1:54 - loss: 1.6681 - acc: 0.5424
1600/60000 [..............................] - ETA: 1:37 - loss: 1.5777 - acc: 0.5756
1856/60000 [..............................] - ETA: 1:25 - loss: 1.5067 - acc: 0.6013
2112/60000 [>.............................] - ETA: 1:16 - loss: 1.4461 - acc: 0.6217
2368/60000 [>.............................] - ETA: 1:08 - loss: 1.3856 - acc: 0.6377
2656/60000 [>.............................] - ETA: 1:02 - loss: 1.3303 - acc: 0.6510
2912/60000 [>.............................] - ETA: 57s - loss: 1.2813 - acc: 0.6655
3168/60000 [>.............................] - ETA: 53s - loss: 1.2344 - acc: 0.6793
3392/60000 [>.............................] - ETA: 50s - loss: 1.1922 - acc: 0.6919
3648/60000 [>.............................] - ETA: 47s - loss: 1.1511 - acc: 0.7045
3968/60000 [>.............................] - ETA: 44s - loss: 1.1079 - acc: 0.7160
4256/60000 [=>............................] - ETA: 42s - loss: 1.0726 - acc: 0.7258
4544/60000 [=>............................] - ETA: 39s - loss: 1.0372 - acc: 0.7348
4864/60000 [=>............................] - ETA: 37s - loss: 1.0026 - acc: 0.7434
5184/60000 [=>............................] - ETA: 35s - loss: 0.9769 - acc: 0.7485
5472/60000 [=>............................] - ETA: 34s - loss: 0.9536 - acc: 0.7546
5728/60000 [=>............................] - ETA: 32s - loss: 0.9306 - acc: 0.7612
5984/60000 [=>............................] - ETA: 31s - loss: 0.9113 - acc: 0.7657
6240/60000 [==>...........................] - ETA: 30s - loss: 0.8948 - acc: 0.7702
6496/60000 [==>...........................] - ETA: 29s - loss: 0.8756 - acc: 0.7756
6720/60000 [==>...........................] - ETA: 29s - loss: 0.8597 - acc: 0.7790
6976/60000 [==>...........................] - ETA: 28s - loss: 0.8430 - acc: 0.7828
7232/60000 [==>...........................] - ETA: 27s - loss: 0.8307 - acc: 0.7853
7456/60000 [==>...........................] - ETA: 27s - loss: 0.8179 - acc: 0.7888
7712/60000 [==>...........................] - ETA: 26s - loss: 0.8049 - acc: 0.7928
7968/60000 [==>...........................] - ETA: 25s - loss: 0.7950 - acc: 0.7948
8256/60000 [===>..........................] - ETA: 25s - loss: 0.7816 - acc: 0.7984
8576/60000 [===>..........................] - ETA: 24s - loss: 0.7641 - acc: 0.8034
8864/60000 [===>..........................] - ETA: 23s - loss: 0.7523 - acc: 0.8064
9184/60000 [===>..........................] - ETA: 23s - loss: 0.7390 - acc: 0.8102
9408/60000 [===>..........................] - ETA: 22s - loss: 0.7292 - acc: 0.8128
9696/60000 [===>..........................] - ETA: 22s - loss: 0.7190 - acc: 0.8152
9984/60000 [===>..........................] - ETA: 21s - loss: 0.7092 - acc: 0.8176
10272/60000 [====>.........................] - ETA: 21s - loss: 0.7008 - acc: 0.8190
10592/60000 [====>.........................] - ETA: 20s - loss: 0.6923 - acc: 0.8207
10880/60000 [====>.........................] - ETA: 20s - loss: 0.6823 - acc: 0.8230
11136/60000 [====>.........................] - ETA: 19s - loss: 0.6761 - acc: 0.8244
11424/60000 [====>.........................] - ETA: 19s - loss: 0.6676 - acc: 0.8265
11712/60000 [====>.........................] - ETA: 19s - loss: 0.6587 - acc: 0.8291
12000/60000 [=====>........................] - ETA: 18s - loss: 0.6517 - acc: 0.8305
12288/60000 [=====>........................] - ETA: 18s - loss: 0.6438 - acc: 0.8325
12576/60000 [=====>........................] - ETA: 18s - loss: 0.6379 - acc: 0.8337
12896/60000 [=====>........................] - ETA: 17s - loss: 0.6317 - acc: 0.8355
13152/60000 [=====>........................] - ETA: 17s - loss: 0.6242 - acc: 0.8373
13376/60000 [=====>........................] - ETA: 17s - loss: 0.6182 - acc: 0.8387
13632/60000 [=====>........................] - ETA: 17s - loss: 0.6139 - acc: 0.8393
13888/60000 [=====>........................] - ETA: 16s - loss: 0.6083 - acc: 0.8407
14144/60000 [======>.......................] - ETA: 16s - loss: 0.6041 - acc: 0.8416
14432/60000 [======>.......................] - ETA: 16s - loss: 0.5992 - acc: 0.8430
14720/60000 [======>.......................] - ETA: 16s - loss: 0.5958 - acc: 0.8438
15008/60000 [======>.......................] - ETA: 15s - loss: 0.5912 - acc: 0.8450
15296/60000 [======>.......................] - ETA: 15s - loss: 0.5879 - acc: 0.8456
15584/60000 [======>.......................] - ETA: 15s - loss: 0.5841 - acc: 0.8468
15904/60000 [======>.......................] - ETA: 15s - loss: 0.5807 - acc: 0.8475
16192/60000 [=======>......................] - ETA: 14s - loss: 0.5759 - acc: 0.8491
16480/60000 [=======>......................] - ETA: 14s - loss: 0.5708 - acc: 0.8501
16704/60000 [=======>......................] - ETA: 14s - loss: 0.5669 - acc: 0.8510
16960/60000 [=======>......................] - ETA: 14s - loss: 0.5636 - acc: 0.8519
17216/60000 [=======>......................] - ETA: 14s - loss: 0.5609 - acc: 0.8526
17472/60000 [=======>......................] - ETA: 14s - loss: 0.5560 - acc: 0.8539
17728/60000 [=======>......................] - ETA: 13s - loss: 0.5536 - acc: 0.8543
17952/60000 [=======>......................] - ETA: 13s - loss: 0.5498 - acc: 0.8553
18176/60000 [========>.....................] - ETA: 13s - loss: 0.5472 - acc: 0.8559
18400/60000 [========>.....................] - ETA: 13s - loss: 0.5451 - acc: 0.8567
18656/60000 [========>.....................] - ETA: 13s - loss: 0.5409 - acc: 0.8578
18912/60000 [========>.....................] - ETA: 13s - loss: 0.5384 - acc: 0.8586
19200/60000 [========>.....................] - ETA: 13s - loss: 0.5352 - acc: 0.8593
19488/60000 [========>.....................] - ETA: 12s - loss: 0.5311 - acc: 0.8604
19776/60000 [========>.....................] - ETA: 12s - loss: 0.5289 - acc: 0.8607
20032/60000 [=========>....................] - ETA: 12s - loss: 0.5255 - acc: 0.8614
20352/60000 [=========>....................] - ETA: 12s - loss: 0.5220 - acc: 0.8623
20640/60000 [=========>....................] - ETA: 12s - loss: 0.5188 - acc: 0.8629
20928/60000 [=========>....................] - ETA: 12s - loss: 0.5153 - acc: 0.8638
21216/60000 [=========>....................] - ETA: 11s - loss: 0.5118 - acc: 0.8647
21504/60000 [=========>....................] - ETA: 11s - loss: 0.5088 - acc: 0.8656
21792/60000 [=========>....................] - ETA: 11s - loss: 0.5075 - acc: 0.8662
22080/60000 [==========>...................] - ETA: 11s - loss: 0.5054 - acc: 0.8668
22400/60000 [==========>...................] - ETA: 11s - loss: 0.5027 - acc: 0.8673
22688/60000 [==========>...................] - ETA: 11s - loss: 0.4993 - acc: 0.8681
22976/60000 [==========>...................] - ETA: 10s - loss: 0.4971 - acc: 0.8685
23264/60000 [==========>...................] - ETA: 10s - loss: 0.4943 - acc: 0.8692
23552/60000 [==========>...................] - ETA: 10s - loss: 0.4925 - acc: 0.8694
23808/60000 [==========>...................] - ETA: 10s - loss: 0.4909 - acc: 0.8698
24064/60000 [===========>..................] - ETA: 10s - loss: 0.4884 - acc: 0.8704
24320/60000 [===========>..................] - ETA: 10s - loss: 0.4859 - acc: 0.8711
24576/60000 [===========>..................] - ETA: 10s - loss: 0.4838 - acc: 0.8715
24864/60000 [===========>..................] - ETA: 10s - loss: 0.4809 - acc: 0.8721
25152/60000 [===========>..................] - ETA: 10s - loss: 0.4790 - acc: 0.8725
25440/60000 [===========>..................] - ETA: 9s - loss: 0.4775 - acc: 0.8730
25760/60000 [===========>..................] - ETA: 9s - loss: 0.4757 - acc: 0.8733
26048/60000 [============>.................] - ETA: 9s - loss: 0.4738 - acc: 0.8736
26368/60000 [============>.................] - ETA: 9s - loss: 0.4721 - acc: 0.8737
26688/60000 [============>.................] - ETA: 9s - loss: 0.4706 - acc: 0.8742
27008/60000 [============>.................] - ETA: 9s - loss: 0.4683 - acc: 0.8747
27296/60000 [============>.................] - ETA: 9s - loss: 0.4658 - acc: 0.8755
27616/60000 [============>.................] - ETA: 8s - loss: 0.4633 - acc: 0.8759
27936/60000 [============>.................] - ETA: 8s - loss: 0.4613 - acc: 0.8763
28256/60000 [=============>................] - ETA: 8s - loss: 0.4589 - acc: 0.8769
28576/60000 [=============>................] - ETA: 8s - loss: 0.4577 - acc: 0.8769
28864/60000 [=============>................] - ETA: 8s - loss: 0.4566 - acc: 0.8770
29184/60000 [=============>................] - ETA: 8s - loss: 0.4556 - acc: 0.8772
29504/60000 [=============>................] - ETA: 8s - loss: 0.4543 - acc: 0.8775
29824/60000 [=============>................] - ETA: 8s - loss: 0.4527 - acc: 0.8779
30144/60000 [==============>...............] - ETA: 7s - loss: 0.4501 - acc: 0.8785
30464/60000 [==============>...............] - ETA: 7s - loss: 0.4491 - acc: 0.8787
30752/60000 [==============>...............] - ETA: 7s - loss: 0.4471 - acc: 0.8793
31072/60000 [==============>...............] - ETA: 7s - loss: 0.4456 - acc: 0.8794
31360/60000 [==============>...............] - ETA: 7s - loss: 0.4446 - acc: 0.8797
31648/60000 [==============>...............] - ETA: 7s - loss: 0.4434 - acc: 0.8800
31936/60000 [==============>...............] - ETA: 7s - loss: 0.4421 - acc: 0.8804
32256/60000 [===============>..............] - ETA: 7s - loss: 0.4400 - acc: 0.8810
32576/60000 [===============>..............] - ETA: 7s - loss: 0.4390 - acc: 0.8813
32896/60000 [===============>..............] - ETA: 7s - loss: 0.4373 - acc: 0.8816
33216/60000 [===============>..............] - ETA: 6s - loss: 0.4353 - acc: 0.8823
33504/60000 [===============>..............] - ETA: 6s - loss: 0.4335 - acc: 0.8826
33728/60000 [===============>..............] - ETA: 6s - loss: 0.4325 - acc: 0.8825
34048/60000 [================>.............] - ETA: 6s - loss: 0.4307 - acc: 0.8830
34336/60000 [================>.............] - ETA: 6s - loss: 0.4295 - acc: 0.8831
34656/60000 [================>.............] - ETA: 6s - loss: 0.4274 - acc: 0.8835
34944/60000 [================>.............] - ETA: 6s - loss: 0.4268 - acc: 0.8835
35168/60000 [================>.............] - ETA: 6s - loss: 0.4261 - acc: 0.8836
35424/60000 [================>.............] - ETA: 6s - loss: 0.4247 - acc: 0.8839
35680/60000 [================>.............] - ETA: 6s - loss: 0.4232 - acc: 0.8842
35968/60000 [================>.............] - ETA: 6s - loss: 0.4220 - acc: 0.8845
36256/60000 [=================>............] - ETA: 6s - loss: 0.4206 - acc: 0.8848
36576/60000 [=================>............] - ETA: 5s - loss: 0.4197 - acc: 0.8850
36896/60000 [=================>............] - ETA: 5s - loss: 0.4186 - acc: 0.8853
37184/60000 [=================>............] - ETA: 5s - loss: 0.4171 - acc: 0.8855
37472/60000 [=================>............] - ETA: 5s - loss: 0.4157 - acc: 0.8858
37760/60000 [=================>............] - ETA: 5s - loss: 0.4152 - acc: 0.8859
38080/60000 [==================>...........] - ETA: 5s - loss: 0.4142 - acc: 0.8862
38400/60000 [==================>...........] - ETA: 5s - loss: 0.4123 - acc: 0.8867
38688/60000 [==================>...........] - ETA: 5s - loss: 0.4116 - acc: 0.8869
39008/60000 [==================>...........] - ETA: 5s - loss: 0.4104 - acc: 0.8872
39328/60000 [==================>...........] - ETA: 5s - loss: 0.4096 - acc: 0.8874
39616/60000 [==================>...........] - ETA: 5s - loss: 0.4086 - acc: 0.8877
39904/60000 [==================>...........] - ETA: 4s - loss: 0.4073 - acc: 0.8880
40192/60000 [===================>..........] - ETA: 4s - loss: 0.4066 - acc: 0.8882
40512/60000 [===================>..........] - ETA: 4s - loss: 0.4064 - acc: 0.8883
40800/60000 [===================>..........] - ETA: 4s - loss: 0.4053 - acc: 0.8886
41120/60000 [===================>..........] - ETA: 4s - loss: 0.4041 - acc: 0.8890
41440/60000 [===================>..........] - ETA: 4s - loss: 0.4026 - acc: 0.8894
41728/60000 [===================>..........] - ETA: 4s - loss: 0.4018 - acc: 0.8896
42016/60000 [====================>.........] - ETA: 4s - loss: 0.4010 - acc: 0.8898
42304/60000 [====================>.........] - ETA: 4s - loss: 0.4000 - acc: 0.8901
42624/60000 [====================>.........] - ETA: 4s - loss: 0.3991 - acc: 0.8904
42912/60000 [====================>.........] - ETA: 4s - loss: 0.3981 - acc: 0.8907
43200/60000 [====================>.........] - ETA: 4s - loss: 0.3970 - acc: 0.8910
43488/60000 [====================>.........] - ETA: 3s - loss: 0.3959 - acc: 0.8913
43808/60000 [====================>.........] - ETA: 3s - loss: 0.3948 - acc: 0.8916
44128/60000 [=====================>........] - ETA: 3s - loss: 0.3947 - acc: 0.8917
44448/60000 [=====================>........] - ETA: 3s - loss: 0.3937 - acc: 0.8920
44736/60000 [=====================>........] - ETA: 3s - loss: 0.3924 - acc: 0.8923
45056/60000 [=====================>........] - ETA: 3s - loss: 0.3923 - acc: 0.8924
45376/60000 [=====================>........] - ETA: 3s - loss: 0.3910 - acc: 0.8928
45696/60000 [=====================>........] - ETA: 3s - loss: 0.3900 - acc: 0.8930
45984/60000 [=====================>........] - ETA: 3s - loss: 0.3890 - acc: 0.8933
46272/60000 [======================>.......] - ETA: 3s - loss: 0.3879 - acc: 0.8936
46528/60000 [======================>.......] - ETA: 3s - loss: 0.3876 - acc: 0.8937
46752/60000 [======================>.......] - ETA: 3s - loss: 0.3866 - acc: 0.8940
46976/60000 [======================>.......] - ETA: 3s - loss: 0.3859 - acc: 0.8941
47168/60000 [======================>.......] - ETA: 3s - loss: 0.3853 - acc: 0.8943
47424/60000 [======================>.......] - ETA: 2s - loss: 0.3844 - acc: 0.8945
47680/60000 [======================>.......] - ETA: 2s - loss: 0.3835 - acc: 0.8948
47936/60000 [======================>.......] - ETA: 2s - loss: 0.3826 - acc: 0.8949
48192/60000 [=======================>......] - ETA: 2s - loss: 0.3824 - acc: 0.8949
48448/60000 [=======================>......] - ETA: 2s - loss: 0.3815 - acc: 0.8952
48704/60000 [=======================>......] - ETA: 2s - loss: 0.3808 - acc: 0.8953
48960/60000 [=======================>......] - ETA: 2s - loss: 0.3800 - acc: 0.8956
49216/60000 [=======================>......] - ETA: 2s - loss: 0.3794 - acc: 0.8957
49504/60000 [=======================>......] - ETA: 2s - loss: 0.3783 - acc: 0.8959
49792/60000 [=======================>......] - ETA: 2s - loss: 0.3770 - acc: 0.8963
50112/60000 [========================>.....] - ETA: 2s - loss: 0.3761 - acc: 0.8966
50432/60000 [========================>.....] - ETA: 2s - loss: 0.3756 - acc: 0.8968
50720/60000 [========================>.....] - ETA: 2s - loss: 0.3754 - acc: 0.8968
51008/60000 [========================>.....] - ETA: 2s - loss: 0.3749 - acc: 0.8969
51296/60000 [========================>.....] - ETA: 2s - loss: 0.3739 - acc: 0.8972
51616/60000 [========================>.....] - ETA: 1s - loss: 0.3735 - acc: 0.8973
51904/60000 [========================>.....] - ETA: 1s - loss: 0.3722 - acc: 0.8977
52192/60000 [=========================>....] - ETA: 1s - loss: 0.3715 - acc: 0.8979
52480/60000 [=========================>....] - ETA: 1s - loss: 0.3705 - acc: 0.8982
52800/60000 [=========================>....] - ETA: 1s - loss: 0.3697 - acc: 0.8984
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54016/60000 [==========================>...] - ETA: 1s - loss: 0.3667 - acc: 0.8990
54336/60000 [==========================>...] - ETA: 1s - loss: 0.3661 - acc: 0.8992
54656/60000 [==========================>...] - ETA: 1s - loss: 0.3649 - acc: 0.8995
54976/60000 [==========================>...] - ETA: 1s - loss: 0.3639 - acc: 0.8998
55296/60000 [==========================>...] - ETA: 1s - loss: 0.3632 - acc: 0.9001
55584/60000 [==========================>...] - ETA: 1s - loss: 0.3624 - acc: 0.9003
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56224/60000 [===========================>..] - ETA: 0s - loss: 0.3607 - acc: 0.9007
56544/60000 [===========================>..] - ETA: 0s - loss: 0.3606 - acc: 0.9008
56832/60000 [===========================>..] - ETA: 0s - loss: 0.3596 - acc: 0.9010
57120/60000 [===========================>..] - ETA: 0s - loss: 0.3585 - acc: 0.9012
57344/60000 [===========================>..] - ETA: 0s - loss: 0.3582 - acc: 0.9013
57600/60000 [===========================>..] - ETA: 0s - loss: 0.3580 - acc: 0.9014
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58144/60000 [============================>.] - ETA: 0s - loss: 0.3571 - acc: 0.9016
58432/60000 [============================>.] - ETA: 0s - loss: 0.3561 - acc: 0.9018
58720/60000 [============================>.] - ETA: 0s - loss: 0.3554 - acc: 0.9020
59040/60000 [============================>.] - ETA: 0s - loss: 0.3545 - acc: 0.9022
59360/60000 [============================>.] - ETA: 0s - loss: 0.3538 - acc: 0.9024
59648/60000 [============================>.] - ETA: 0s - loss: 0.3530 - acc: 0.9026
59936/60000 [============================>.] - ETA: 0s - loss: 0.3525 - acc: 0.9027
60000/60000 [==============================] - 13s 225us/step - loss: 0.3524 - acc: 0.9028
Epoch 2/2
32/60000 [..............................] - ETA: 13s - loss: 0.1603 - acc: 0.9062
352/60000 [..............................] - ETA: 10s - loss: 0.1865 - acc: 0.9403
640/60000 [..............................] - ETA: 10s - loss: 0.1994 - acc: 0.9359
928/60000 [..............................] - ETA: 10s - loss: 0.1899 - acc: 0.9386
1216/60000 [..............................] - ETA: 10s - loss: 0.1985 - acc: 0.9416
1536/60000 [..............................] - ETA: 10s - loss: 0.1987 - acc: 0.9434
1824/60000 [..............................] - ETA: 10s - loss: 0.2013 - acc: 0.9435
2144/60000 [>.............................] - ETA: 10s - loss: 0.2011 - acc: 0.9426
2464/60000 [>.............................] - ETA: 9s - loss: 0.2064 - acc: 0.9420
2752/60000 [>.............................] - ETA: 9s - loss: 0.2140 - acc: 0.9404
3040/60000 [>.............................] - ETA: 9s - loss: 0.2190 - acc: 0.9391
3328/60000 [>.............................] - ETA: 9s - loss: 0.2223 - acc: 0.9399
3648/60000 [>.............................] - ETA: 9s - loss: 0.2159 - acc: 0.9408
3968/60000 [>.............................] - ETA: 9s - loss: 0.2198 - acc: 0.9400
4288/60000 [=>............................] - ETA: 9s - loss: 0.2271 - acc: 0.9373
4576/60000 [=>............................] - ETA: 9s - loss: 0.2250 - acc: 0.9384
4896/60000 [=>............................] - ETA: 9s - loss: 0.2266 - acc: 0.9387
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5504/60000 [=>............................] - ETA: 9s - loss: 0.2297 - acc: 0.9384
5792/60000 [=>............................] - ETA: 9s - loss: 0.2304 - acc: 0.9375
6112/60000 [==>...........................] - ETA: 9s - loss: 0.2306 - acc: 0.9373
6400/60000 [==>...........................] - ETA: 9s - loss: 0.2306 - acc: 0.9375
6720/60000 [==>...........................] - ETA: 9s - loss: 0.2310 - acc: 0.9365
7008/60000 [==>...........................] - ETA: 9s - loss: 0.2294 - acc: 0.9365
7296/60000 [==>...........................] - ETA: 9s - loss: 0.2296 - acc: 0.9360
7616/60000 [==>...........................] - ETA: 9s - loss: 0.2309 - acc: 0.9351
7936/60000 [==>...........................] - ETA: 8s - loss: 0.2340 - acc: 0.9346
8224/60000 [===>..........................] - ETA: 8s - loss: 0.2321 - acc: 0.9351
8480/60000 [===>..........................] - ETA: 8s - loss: 0.2284 - acc: 0.9360
8768/60000 [===>..........................] - ETA: 8s - loss: 0.2301 - acc: 0.9352
9024/60000 [===>..........................] - ETA: 8s - loss: 0.2292 - acc: 0.9354
9312/60000 [===>..........................] - ETA: 8s - loss: 0.2289 - acc: 0.9351
9632/60000 [===>..........................] - ETA: 8s - loss: 0.2275 - acc: 0.9355
9920/60000 [===>..........................] - ETA: 8s - loss: 0.2272 - acc: 0.9357
10208/60000 [====>.........................] - ETA: 8s - loss: 0.2269 - acc: 0.9355
10496/60000 [====>.........................] - ETA: 8s - loss: 0.2263 - acc: 0.9355
10816/60000 [====>.........................] - ETA: 8s - loss: 0.2261 - acc: 0.9353
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11712/60000 [====>.........................] - ETA: 8s - loss: 0.2275 - acc: 0.9351
12000/60000 [=====>........................] - ETA: 8s - loss: 0.2271 - acc: 0.9354
12320/60000 [=====>........................] - ETA: 8s - loss: 0.2268 - acc: 0.9358
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12896/60000 [=====>........................] - ETA: 8s - loss: 0.2257 - acc: 0.9358
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21600/60000 [=========>....................] - ETA: 6s - loss: 0.2170 - acc: 0.9374
21920/60000 [=========>....................] - ETA: 6s - loss: 0.2173 - acc: 0.9373
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22496/60000 [==========>...................] - ETA: 6s - loss: 0.2167 - acc: 0.9374
22816/60000 [==========>...................] - ETA: 6s - loss: 0.2166 - acc: 0.9375
23072/60000 [==========>...................] - ETA: 6s - loss: 0.2160 - acc: 0.9377
23360/60000 [==========>...................] - ETA: 6s - loss: 0.2150 - acc: 0.9381
23680/60000 [==========>...................] - ETA: 6s - loss: 0.2151 - acc: 0.9380
24000/60000 [===========>..................] - ETA: 6s - loss: 0.2152 - acc: 0.9379
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24640/60000 [===========>..................] - ETA: 6s - loss: 0.2148 - acc: 0.9383
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60000/60000 [==============================] - 11s 179us/step - loss: 0.2075 - acc: 0.9407
32/10000 [..............................] - ETA: 9s
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10000/10000 [==============================] - 1s 53us/step
test loss:0.18184480152353644 test accuracy:0.9462
Process finished with exit code 0