Keras搭建第一个CNN神经网络（mnist手写体数字分类）

关于CNN神经网络的介绍可以看这篇博客：

https://blog.csdn.net/zgcr654321/article/details/82959488

本文中keras使用的后台框架还是tensorflow，其神经网络的实现与tensorflow并没有区别，只是用了keras自己的一套语法来实现而已。

代码如下：

[code]# 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, Convolution2D, MaxPooling2D, Flatten
from keras.optimizers import Adam
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(-1, 1, 28, 28) / 255.
x_test = x_test.reshape(-1, 1, 28, 28) / 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

model = Sequential()
# 第一层卷积和池化
model.add(Convolution2D(batch_input_shape=(None, 1, 28, 28), filters=32, kernel_size=5, strides=1, padding='same',
data_format='channels_first', ))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=2, strides=2, padding='same', data_format='channels_first', ))
# 第二层卷积和池化
model.add(Convolution2D(filters=64, kernel_size=5, strides=1, padding='same', data_format='channels_first'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=2, strides=2, padding='same', data_format='channels_first'))
# 全连接层，输入数据先变成一维
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
# 输出层
model.add(Dense(10))
model.add(Activation('softmax'))
# 设置adam优化方法，loss函数, accuracy
adam = Adam(lr=1e-4)
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])

# 训练网络
model.fit(x_train, y_train, epochs=1, 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：

[code]Using TensorFlow backend.
Epoch 1/1

32/60000 [..............................] - ETA: 1:06:43 - loss: 2.2865 - acc: 0.2500
128/60000 [..............................] - ETA: 17:12 - loss: 2.2846 - acc: 0.1953
224/60000 [..............................] - ETA: 10:07 - loss: 2.2632 - acc: 0.2188
288/60000 [..............................] - ETA: 8:02 - loss: 2.2541 - acc: 0.2326
384/60000 [..............................] - ETA: 6:12 - loss: 2.2249 - acc: 0.2865
480/60000 [..............................] - ETA: 5:06 - loss: 2.2004 - acc: 0.3104
576/60000 [..............................] - ETA: 4:21 - loss: 2.1851 - acc: 0.3212
672/60000 [..............................] - ETA: 3:49 - loss: 2.1589 - acc: 0.3542
768/60000 [..............................] - ETA: 3:25 - loss: 2.1299 - acc: 0.3880
864/60000 [..............................] - ETA: 3:07 - loss: 2.1043 - acc: 0.4097
960/60000 [..............................] - ETA: 2:52 - loss: 2.0701 - acc: 0.4344
1056/60000 [..............................] - ETA: 2:40 - loss: 2.0337 - acc: 0.4621
1120/60000 [..............................] - ETA: 2:33 - loss: 2.0083 - acc: 0.4732
1216/60000 [..............................] - ETA: 2:24 - loss: 1.9656 - acc: 0.4967
1312/60000 [..............................] - ETA: 2:17 - loss: 1.9218 - acc: 0.5114
1408/60000 [..............................] - ETA: 2:10 - loss: 1.8785 - acc: 0.5220
1472/60000 [..............................] - ETA: 2:07 - loss: 1.8518 - acc: 0.5279
1568/60000 [..............................] - ETA: 2:02 - loss: 1.8101 - acc: 0.5376
1664/60000 [..............................] - ETA: 1:57 - loss: 1.7690 - acc: 0.5511
1760/60000 [..............................] - ETA: 1:53 - loss: 1.7297 - acc: 0.5625
1856/60000 [..............................] - ETA: 1:49 - loss: 1.6849 - acc: 0.5749
1952/60000 [..............................] - ETA: 1:45 - loss: 1.6418 - acc: 0.5856
2048/60000 [>.............................] - ETA: 1:42 - loss: 1.5998 - acc: 0.5967
2144/60000 [>.............................] - ETA: 1:39 - loss: 1.5640 - acc: 0.6035
2240/60000 [>.............................] - ETA: 1:36 - loss: 1.5284 - acc: 0.6121
2336/60000 [>.............................] - ETA: 1:34 - loss: 1.4906 - acc: 0.6199
2432/60000 [>.............................] - ETA: 1:32 - loss: 1.4588 - acc: 0.6262
2528/60000 [>.............................] - ETA: 1:29 - loss: 1.4268 - acc: 0.6345
2624/60000 [>.............................] - ETA: 1:27 - loss: 1.3932 - acc: 0.6418
2720/60000 [>.............................] - ETA: 1:25 - loss: 1.3615 - acc: 0.6504
2816/60000 [>.............................] - ETA: 1:24 - loss: 1.3395 - acc: 0.6552
2912/60000 [>.............................] - ETA: 1:22 - loss: 1.3137 - acc: 0.6617
3008/60000 [>.............................] - ETA: 1:21 - loss: 1.2916 - acc: 0.6676
3104/60000 [>.............................] - ETA: 1:19 - loss: 1.2695 - acc: 0.6714
3200/60000 [>.............................] - ETA: 1:18 - loss: 1.2477 - acc: 0.6775
3296/60000 [>.............................] - ETA: 1:16 - loss: 1.2243 - acc: 0.6829
3392/60000 [>.............................] - ETA: 1:15 - loss: 1.2040 - acc: 0.6884
3488/60000 [>.............................] - ETA: 1:14 - loss: 1.1862 - acc: 0.6921
3552/60000 [>.............................] - ETA: 1:14 - loss: 1.1752 - acc: 0.6945
3648/60000 [>.............................] - ETA: 1:13 - loss: 1.1543 - acc: 0.7001
3744/60000 [>.............................] - ETA: 1:12 - loss: 1.1357 - acc: 0.7054
3808/60000 [>.............................] - ETA: 1:11 - loss: 1.1239 - acc: 0.7088
3872/60000 [>.............................] - ETA: 1:11 - loss: 1.1133 - acc: 0.7107
3936/60000 [>.............................] - ETA: 1:11 - loss: 1.1031 - acc: 0.7129
4032/60000 [=>............................] - ETA: 1:10 - loss: 1.0904 - acc: 0.7160
4128/60000 [=>............................] - ETA: 1:09 - loss: 1.0754 - acc: 0.7200
4224/60000 [=>............................] - ETA: 1:08 - loss: 1.0626 - acc: 0.7232
4320/60000 [=>............................] - ETA: 1:07 - loss: 1.0491 - acc: 0.7262
4416/60000 [=>............................] - ETA: 1:06 - loss: 1.0331 - acc: 0.7305
4512/60000 [=>............................] - ETA: 1:06 - loss: 1.0230 - acc: 0.7340
4608/60000 [=>............................] - ETA: 1:05 - loss: 1.0081 - acc: 0.7378
4704/60000 [=>............................] - ETA: 1:04 - loss: 0.9945 - acc: 0.7413
4800/60000 [=>............................] - ETA: 1:04 - loss: 0.9813 - acc: 0.7446
4896/60000 [=>............................] - ETA: 1:03 - loss: 0.9697 - acc: 0.7471
4992/60000 [=>............................] - ETA: 1:02 - loss: 0.9599 - acc: 0.7496
5088/60000 [=>............................] - ETA: 1:02 - loss: 0.9485 - acc: 0.7522
5184/60000 [=>............................] - ETA: 1:01 - loss: 0.9386 - acc: 0.7554
5280/60000 [=>............................] - ETA: 1:01 - loss: 0.9277 - acc: 0.7581
5376/60000 [=>............................] - ETA: 1:00 - loss: 0.9187 - acc: 0.7600
5472/60000 [=>............................] - ETA: 1:00 - loss: 0.9092 - acc: 0.7621
5568/60000 [=>............................] - ETA: 59s - loss: 0.9006 - acc: 0.7642
5664/60000 [=>............................] - ETA: 59s - loss: 0.8913 - acc: 0.7668
5760/60000 [=>............................] - ETA: 58s - loss: 0.8831 - acc: 0.7693
5856/60000 [=>............................] - ETA: 58s - loss: 0.8736 - acc: 0.7720
5952/60000 [=>............................] - ETA: 57s - loss: 0.8637 - acc: 0.7745
6048/60000 [==>...........................] - ETA: 57s - loss: 0.8544 - acc: 0.7765
6144/60000 [==>...........................] - ETA: 56s - loss: 0.8490 - acc: 0.7780
6240/60000 [==>...........................] - ETA: 56s - loss: 0.8409 - acc: 0.7803
6304/60000 [==>...........................] - ETA: 56s - loss: 0.8354 - acc: 0.7817
6400/60000 [==>...........................] - ETA: 55s - loss: 0.8287 - acc: 0.7833
6496/60000 [==>...........................] - ETA: 55s - loss: 0.8215 - acc: 0.7853
6592/60000 [==>...........................] - ETA: 55s - loss: 0.8132 - acc: 0.7872
6688/60000 [==>...........................] - ETA: 54s - loss: 0.8055 - acc: 0.7890
6784/60000 [==>...........................] - ETA: 54s - loss: 0.8026 - acc: 0.7889
6880/60000 [==>...........................] - ETA: 54s - loss: 0.7958 - acc: 0.7904
6944/60000 [==>...........................] - ETA: 54s - loss: 0.7915 - acc: 0.7915
7040/60000 [==>...........................] - ETA: 53s - loss: 0.7847 - acc: 0.7932
7136/60000 [==>...........................] - ETA: 53s - loss: 0.7777 - acc: 0.7948
7200/60000 [==>...........................] - ETA: 53s - loss: 0.7735 - acc: 0.7960
7296/60000 [==>...........................] - ETA: 52s - loss: 0.7666 - acc: 0.7973
7392/60000 [==>...........................] - ETA: 52s - loss: 0.7601 - acc: 0.7988
7488/60000 [==>...........................] - ETA: 52s - loss: 0.7531 - acc: 0.8006
7584/60000 [==>...........................] - ETA: 52s - loss: 0.7488 - acc: 0.8009
7680/60000 [==>...........................] - ETA: 51s - loss: 0.7432 - acc: 0.8022
7776/60000 [==>...........................] - ETA: 51s - loss: 0.7380 - acc: 0.8036
7872/60000 [==>...........................] - ETA: 51s - loss: 0.7334 - acc: 0.8044
7968/60000 [==>...........................] - ETA: 51s - loss: 0.7279 - acc: 0.8055
8064/60000 [===>..........................] - ETA: 50s - loss: 0.7236 - acc: 0.8067
8160/60000 [===>..........................] - ETA: 50s - loss: 0.7169 - acc: 0.8087
8256/60000 [===>..........................] - ETA: 50s - loss: 0.7137 - acc: 0.8096
8352/60000 [===>..........................] - ETA: 49s - loss: 0.7082 - acc: 0.8111
8448/60000 [===>..........................] - ETA: 49s - loss: 0.7037 - acc: 0.8120
8544/60000 [===>..........................] - ETA: 49s - loss: 0.6994 - acc: 0.8126
8640/60000 [===>..........................] - ETA: 49s - loss: 0.6942 - acc: 0.8139
8736/60000 [===>..........................] - ETA: 48s - loss: 0.6886 - acc: 0.8154
8832/60000 [===>..........................] - ETA: 48s - loss: 0.6835 - acc: 0.8166
8928/60000 [===>..........................] - ETA: 48s - loss: 0.6788 - acc: 0.8179
9024/60000 [===>..........................] - ETA: 48s - loss: 0.6743 - acc: 0.8190
9120/60000 [===>..........................] - ETA: 48s - loss: 0.6694 - acc: 0.8205
9216/60000 [===>..........................] - ETA: 47s - loss: 0.6667 - acc: 0.8215
9312/60000 [===>..........................] - ETA: 47s - loss: 0.6629 - acc: 0.8225
9408/60000 [===>..........................] - ETA: 47s - loss: 0.6581 - acc: 0.8237
9504/60000 [===>..........................] - ETA: 47s - loss: 0.6531 - acc: 0.8250
9568/60000 [===>..........................] - ETA: 47s - loss: 0.6505 - acc: 0.8257
9632/60000 [===>..........................] - ETA: 47s - loss: 0.6471 - acc: 0.8266
9728/60000 [===>..........................] - ETA: 46s - loss: 0.6432 - acc: 0.8278
9824/60000 [===>..........................] - ETA: 46s - loss: 0.6403 - acc: 0.8288
9920/60000 [===>..........................] - ETA: 46s - loss: 0.6383 - acc: 0.8294
10016/60000 [====>.........................] - ETA: 46s - loss: 0.6340 - acc: 0.8305
10112/60000 [====>.........................] - ETA: 46s - loss: 0.6310 - acc: 0.8310
10208/60000 [====>.........................] - ETA: 45s - loss: 0.6280 - acc: 0.8318
10304/60000 [====>.........................] - ETA: 45s - loss: 0.6239 - acc: 0.8327
10400/60000 [====>.........................] - ETA: 45s - loss: 0.6196 - acc: 0.8338
10464/60000 [====>.........................] - ETA: 45s - loss: 0.6174 - acc: 0.8343
10560/60000 [====>.........................] - ETA: 45s - loss: 0.6144 - acc: 0.8350
10656/60000 [====>.........................] - ETA: 45s - loss: 0.6105 - acc: 0.8359
10752/60000 [====>.........................] - ETA: 44s - loss: 0.6061 - acc: 0.8372
10848/60000 [====>.........................] - ETA: 44s - loss: 0.6033 - acc: 0.8378
10944/60000 [====>.........................] - ETA: 44s - loss: 0.6001 - acc: 0.8384
11008/60000 [====>.........................] - ETA: 44s - loss: 0.5979 - acc: 0.8386
11104/60000 [====>.........................] - ETA: 44s - loss: 0.5956 - acc: 0.8390
11200/60000 [====>.........................] - ETA: 44s - loss: 0.5919 - acc: 0.8400
11296/60000 [====>.........................] - ETA: 43s - loss: 0.5881 - acc: 0.8411
11392/60000 [====>.........................] - ETA: 43s - loss: 0.5842 - acc: 0.8423
11488/60000 [====>.........................] - ETA: 43s - loss: 0.5820 - acc: 0.8428
11584/60000 [====>.........................] - ETA: 43s - loss: 0.5786 - acc: 0.8436
11680/60000 [====>.........................] - ETA: 43s - loss: 0.5756 - acc: 0.8443
11776/60000 [====>.........................] - ETA: 42s - loss: 0.5730 - acc: 0.8452
11872/60000 [====>.........................] - ETA: 42s - loss: 0.5705 - acc: 0.8458
11968/60000 [====>.........................] - ETA: 42s - loss: 0.5670 - acc: 0.8468
12064/60000 [=====>........................] - ETA: 42s - loss: 0.5642 - acc: 0.8476
12160/60000 [=====>........................] - ETA: 42s - loss: 0.5622 - acc: 0.8482
12256/60000 [=====>........................] - ETA: 42s - loss: 0.5595 - acc: 0.8489
12352/60000 [=====>........................] - ETA: 41s - loss: 0.5575 - acc: 0.8494
12448/60000 [=====>........................] - ETA: 41s - loss: 0.5547 - acc: 0.8503
12544/60000 [=====>........................] - ETA: 41s - loss: 0.5516 - acc: 0.8511
12640/60000 [=====>........................] - ETA: 41s - loss: 0.5496 - acc: 0.8514
12736/60000 [=====>........................] - ETA: 41s - loss: 0.5469 - acc: 0.8519
12832/60000 [=====>........................] - ETA: 41s - loss: 0.5440 - acc: 0.8524
12928/60000 [=====>........................] - ETA: 41s - loss: 0.5408 - acc: 0.8533
13024/60000 [=====>........................] - ETA: 40s - loss: 0.5385 - acc: 0.8539
13120/60000 [=====>........................] - ETA: 40s - loss: 0.5362 - acc: 0.8542
13216/60000 [=====>........................] - ETA: 40s - loss: 0.5333 - acc: 0.8549
13312/60000 [=====>........................] - ETA: 40s - loss: 0.5312 - acc: 0.8554
13408/60000 [=====>........................] - ETA: 40s - loss: 0.5284 - acc: 0.8562
13504/60000 [=====>........................] - ETA: 40s - loss: 0.5258 - acc: 0.8569
13600/60000 [=====>........................] - ETA: 40s - loss: 0.5235 - acc: 0.8572
13696/60000 [=====>........................] - ETA: 39s - loss: 0.5213 - acc: 0.8578
13792/60000 [=====>........................] - ETA: 39s - loss: 0.5201 - acc: 0.8580
13888/60000 [=====>........................] - ETA: 39s - loss: 0.5179 - acc: 0.8584
13984/60000 [=====>........................] - ETA: 39s - loss: 0.5156 - acc: 0.8588
14080/60000 [======>.......................] - ETA: 39s - loss: 0.5131 - acc: 0.8596
14176/60000 [======>.......................] - ETA: 39s - loss: 0.5115 - acc: 0.8600
14272/60000 [======>.......................] - ETA: 39s - loss: 0.5095 - acc: 0.8606
14368/60000 [======>.......................] - ETA: 38s - loss: 0.5072 - acc: 0.8612
14464/60000 [======>.......................] - ETA: 38s - loss: 0.5047 - acc: 0.8619
14560/60000 [======>.......................] - ETA: 38s - loss: 0.5028 - acc: 0.8623
14656/60000 [======>.......................] - ETA: 38s - loss: 0.5010 - acc: 0.8627
14752/60000 [======>.......................] - ETA: 38s - loss: 0.4987 - acc: 0.8633
14816/60000 [======>.......................] - ETA: 38s - loss: 0.4970 - acc: 0.8638
14912/60000 [======>.......................] - ETA: 38s - loss: 0.4950 - acc: 0.8644
15008/60000 [======>.......................] - ETA: 38s - loss: 0.4926 - acc: 0.8651
15104/60000 [======>.......................] - ETA: 37s - loss: 0.4912 - acc: 0.8654
15200/60000 [======>.......................] - ETA: 37s - loss: 0.4892 - acc: 0.8660
15264/60000 [======>.......................] - ETA: 37s - loss: 0.4882 - acc: 0.8664
15360/60000 [======>.......................] - ETA: 37s - loss: 0.4870 - acc: 0.8665
15424/60000 [======>.......................] - ETA: 37s - loss: 0.4861 - acc: 0.8666
15520/60000 [======>.......................] - ETA: 37s - loss: 0.4845 - acc: 0.8671
15616/60000 [======>.......................] - ETA: 37s - loss: 0.4821 - acc: 0.8678
15712/60000 [======>.......................] - ETA: 37s - loss: 0.4803 - acc: 0.8682
15808/60000 [======>.......................] - ETA: 37s - loss: 0.4791 - acc: 0.8685
15904/60000 [======>.......................] - ETA: 37s - loss: 0.4778 - acc: 0.8689
16000/60000 [=======>......................] - ETA: 36s - loss: 0.4757 - acc: 0.8694
16096/60000 [=======>......................] - ETA: 36s - loss: 0.4740 - acc: 0.8699
16192/60000 [=======>......................] - ETA: 36s - loss: 0.4719 - acc: 0.8705
16288/60000 [=======>......................] - ETA: 36s - loss: 0.4699 - acc: 0.8710
16384/60000 [=======>......................] - ETA: 36s - loss: 0.4680 - acc: 0.8713
16480/60000 [=======>......................] - ETA: 36s - loss: 0.4673 - acc: 0.8714
16576/60000 [=======>......................] - ETA: 36s - loss: 0.4658 - acc: 0.8718
16672/60000 [=======>......................] - ETA: 36s - loss: 0.4646 - acc: 0.8722
16768/60000 [=======>......................] - ETA: 35s - loss: 0.4628 - acc: 0.8726
16864/60000 [=======>......................] - ETA: 35s - loss: 0.4610 - acc: 0.8732
16928/60000 [=======>......................] - ETA: 35s - loss: 0.4599 - acc: 0.8734
17024/60000 [=======>......................] - ETA: 35s - loss: 0.4581 - acc: 0.8739
17088/60000 [=======>......................] - ETA: 35s - loss: 0.4570 - acc: 0.8741
17152/60000 [=======>......................] - ETA: 35s - loss: 0.4562 - acc: 0.8743
17216/60000 [=======>......................] - ETA: 35s - loss: 0.4559 - acc: 0.8744
17312/60000 [=======>......................] - ETA: 35s - loss: 0.4538 - acc: 0.8751
17408/60000 [=======>......................] - ETA: 35s - loss: 0.4522 - acc: 0.8755
17504/60000 [=======>......................] - ETA: 35s - loss: 0.4503 - acc: 0.8760
17600/60000 [=======>......................] - ETA: 35s - loss: 0.4494 - acc: 0.8763
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60000/60000 [==============================] - 44s 729us/step - loss: 0.2102 - acc: 0.9405

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10000/10000 [==============================] - 2s 175us/step
test loss:0.061329067667014896 test accuracy:0.9817

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