Python实现Keras搭建神经网络训练分类模型教程

桃扇骨 2023-02-18 08:24 51阅读 0赞

更多python视频教程请到菜鸟教程https://www.piaodoo.com/

我就废话不多说了，大家还是直接看代码吧~

**注释讲解版：**

# Classifier example

import numpy as np

# for reproducibility #

np.random.seed(1337)

# from keras.datasets import mnist #

from keras.utils import np\_utils  
from keras.models import Sequential  
from keras.layers import Dense, Activation  
from keras.optimizers import RMSprop

# 程序中用到的数据是经典的手写体识别mnist数据集 #

# download the mnist to the path if it is the first time to be called #

# X shape (60,000 28x28), y #

# (X\_train, y\_train), (X\_test, y\_test) = mnist.load\_data() #

# 下载minst.npz： #

# 链接: https://pan.baidu.com/s/1b2ppKDOdzDJxivgmyOoQsA #

# 提取码: y5ir #

# 将下载好的minst.npz放到当前目录下 #

path=’./mnist.npz’  
f = np.load(path)  
X\_train, y\_train = f\[‘x\_train’\], f\[‘y\_train’\]  
X\_test, y\_test = f\[‘x\_test’\], f\[‘y\_test’\]  
f.close()

# data pre-processing #

# 数据预处理 #

# normalize #

# X shape (60,000 28x28)，表示输入数据 X 是个三维的数据 #

# 可以理解为 60000行数据，每一行是一张28 x 28 的灰度图片 #

# X\_train.reshape(X\_train.shape\[0\], -1)表示：只保留第一维，其余的纬度，不管多少纬度，重新排列为一维 #

# 参数-1就是不知道行数或者列数多少的情况下使用的参数 #

# 所以先确定除了参数-1之外的其他参数，然后通过(总参数的计算) / (确定除了参数-1之外的其他参数) = 该位置应该是多少的参数 #

# 这里用-1是偷懒的做法,等同于 28\*28 #

# reshape后的数据是：共60000行，每一行是784个数据点（feature） #

# 输入的 x 变成 60,000\*784 的数据，然后除以 255 进行标准化 #

# 因为每个像素都是在 0 到 255 之间的，标准化之后就变成了 0 到 1 之间 #

X\_train = X\_train.reshape(X\_train.shape\[0\], -1) / 255  
X\_test = X\_test.reshape(X\_test.shape\[0\], -1) / 255

# 分类标签编码 #

# 将y转化为one-hot vector #

y\_train = np\_utils.to\_categorical(y\_train, num\_classes = 10)  
y\_test = np\_utils.to\_categorical(y\_test, num\_classes = 10)

# Another way to build your neural net #

# 建立神经网络 #

# 应用了2层的神经网络，前一层的激活函数用的是relu，后一层的激活函数用的是softmax #

\#32是输出的维数  
model = Sequential(\[  
Dense(32, input\_dim=784),  
Activation(‘relu’),  
Dense(10),  
Activation(‘softmax’)  
\])

# Another way to define your optimizer #

# 优化函数 #

# 优化算法用的是RMSprop #

rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)

# We add metrics to get more results you want to see #

# 不自己定义，直接用内置的优化器也行，optimizer=‘rmsprop’ #

\#激活模型：接下来用 model.compile 激励神经网络  
model.compile(  
optimizer=rmsprop,  
loss=‘categorical\_crossentropy’,  
metrics=\[‘accuracy’\]  
)

print(‘Training------------’)

# Another way to train the model #

# 训练模型 #

# 上一个程序是用train\_on\_batch 一批一批的训练 X\_train, Y\_train #

# 默认的返回值是 cost，每100步输出一下结果 #

# 输出的样式与上一个程序的有所不同，感觉用model.fit()更清晰明了 #

# 上一个程序是Python实现Keras搭建神经网络训练回归模型： #

# https://blog.csdn.net/weixin\_45798684/article/details/106503685 #

model.fit(X\_train, y\_train, nb\_epoch=2, batch\_size=32)

print(’\\nTesting------------’)

# Evaluate the model with the metrics we defined earlier #

# 测试 #

loss, accuracy = model.evaluate(X\_test, y\_test)

print(‘test loss:’, loss)  
print(‘test accuracy:’, accuracy)

运行结果：

Using TensorFlow backend.

Training------------

Epoch 1/2

32/60000 \[…\] - ETA: 5:03 - loss: 2.4464 - accuracy: 0.0625  
864/60000 \[…\] - ETA: 14s - loss: 1.8023 - accuracy: 0.4850  
1696/60000 \[…\] - ETA: 9s - loss: 1.5119 - accuracy: 0.6002  
2432/60000 \[>…\] - ETA: 7s - loss: 1.3151 - accuracy: 0.6637  
3200/60000 \[>…\] - ETA: 6s - loss: 1.1663 - accuracy: 0.7056  
3968/60000 \[>…\] - ETA: 5s - loss: 1.0533 - accuracy: 0.7344  
4704/60000 \[=>…\] - ETA: 5s - loss: 0.9696 - accuracy: 0.7564  
5408/60000 \[=>…\] - ETA: 5s - loss: 0.9162 - accuracy: 0.7681  
6112/60000 \[>…\] - ETA: 5s - loss: 0.8692 - accuracy: 0.7804  
6784/60000 \[>…\] - ETA: 4s - loss: 0.8225 - accuracy: 0.7933  
7424/60000 \[>…\] - ETA: 4s - loss: 0.7871 - accuracy: 0.8021  
8128/60000 \[=>…\] - ETA: 4s - loss: 0.7546 - accuracy: 0.8099  
8960/60000 \[=>…\] - ETA: 4s - loss: 0.7196 - accuracy: 0.8183  
9568/60000 \[=>…\] - ETA: 4s - loss: 0.6987 - accuracy: 0.8230  
10144/60000 \[>…\] - ETA: 4s - loss: 0.6812 - accuracy: 0.8262  
10784/60000 \[>…\] - ETA: 4s - loss: 0.6640 - accuracy: 0.8297  
11456/60000 \[>…\] - ETA: 4s - loss: 0.6462 - accuracy: 0.8329  
12128/60000 \[=>…\] - ETA: 4s - loss: 0.6297 - accuracy: 0.8366  
12704/60000 \[=>…\] - ETA: 4s - loss: 0.6156 - accuracy: 0.8405  
13408/60000 \[=>…\] - ETA: 3s - loss: 0.6009 - accuracy: 0.8430  
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15488/60000 \[>…\] - ETA: 3s - loss: 0.5685 - accuracy: 0.8503  
16192/60000 \[=>…\] - ETA: 3s - loss: 0.5576 - accuracy: 0.8534  
16896/60000 \[=>…\] - ETA: 3s - loss: 0.5477 - accuracy: 0.8555  
17600/60000 \[=>…\] - ETA: 3s - loss: 0.5380 - accuracy: 0.8576  
18240/60000 \[>…\] - ETA: 3s - loss: 0.5279 - accuracy: 0.8600  
18976/60000 \[>…\] - ETA: 3s - loss: 0.5208 - accuracy: 0.8617  
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20416/60000 \[=>…\] - ETA: 3s - loss: 0.5046 - accuracy: 0.8654  
21088/60000 \[=>…\] - ETA: 3s - loss: 0.4992 - accuracy: 0.8669  
21792/60000 \[=>…\] - ETA: 3s - loss: 0.4932 - accuracy: 0.8684  
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23072/60000 \[>…\] - ETA: 2s - loss: 0.4845 - accuracy: 0.8703  
23648/60000 \[>…\] - ETA: 2s - loss: 0.4800 - accuracy: 0.8712  
24096/60000 \[=>…\] - ETA: 2s - loss: 0.4776 - accuracy: 0.8718  
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25568/60000 \[=>…\] - ETA: 2s - loss: 0.4658 - accuracy: 0.8745  
26080/60000 \[>…\] - ETA: 2s - loss: 0.4623 - accuracy: 0.8753  
26592/60000 \[>…\] - ETA: 2s - loss: 0.4600 - accuracy: 0.8756  
27072/60000 \[>…\] - ETA: 2s - loss: 0.4566 - accuracy: 0.8763  
27584/60000 \[>…\] - ETA: 2s - loss: 0.4532 - accuracy: 0.8771  
28032/60000 \[=>…\] - ETA: 2s - loss: 0.4513 - accuracy: 0.8775  
28512/60000 \[=>…\] - ETA: 2s - loss: 0.4477 - accuracy: 0.8784  
28992/60000 \[=>…\] - ETA: 2s - loss: 0.4464 - accuracy: 0.8786  
29472/60000 \[=>…\] - ETA: 2s - loss: 0.4439 - accuracy: 0.8791  
29952/60000 \[=>…\] - ETA: 2s - loss: 0.4404 - accuracy: 0.8800  
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30784/60000 \[>…\] - ETA: 2s - loss: 0.4349 - accuracy: 0.8813  
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31808/60000 \[>…\] - ETA: 2s - loss: 0.4301 - accuracy: 0.8827  
32256/60000 \[=>…\] - ETA: 2s - loss: 0.4279 - accuracy: 0.8832  
32736/60000 \[=>…\] - ETA: 2s - loss: 0.4258 - accuracy: 0.8838  
33280/60000 \[=>…\] - ETA: 2s - loss: 0.4228 - accuracy: 0.8844  
33920/60000 \[=>…\] - ETA: 2s - loss: 0.4195 - accuracy: 0.8849  
34560/60000 \[>…\] - ETA: 2s - loss: 0.4179 - accuracy: 0.8852  
35104/60000 \[>…\] - ETA: 2s - loss: 0.4165 - accuracy: 0.8854  
35680/60000 \[>…\] - ETA: 2s - loss: 0.4139 - accuracy: 0.8860  
36288/60000 \[=>…\] - ETA: 2s - loss: 0.4111 - accuracy: 0.8870  
36928/60000 \[=>…\] - ETA: 2s - loss: 0.4088 - accuracy: 0.8874  
37504/60000 \[=>…\] - ETA: 2s - loss: 0.4070 - accuracy: 0.8878  
38048/60000 \[>…\] - ETA: 1s - loss: 0.4052 - accuracy: 0.8882  
38656/60000 \[>…\] - ETA: 1s - loss: 0.4031 - accuracy: 0.8888  
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39840/60000 \[>…\] - ETA: 1s - loss: 0.3997 - accuracy: 0.8896  
40416/60000 \[=>…\] - ETA: 1s - loss: 0.3978 - accuracy: 0.8901  
40960/60000 \[=>…\] - ETA: 1s - loss: 0.3958 - accuracy: 0.8906  
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44832/60000 \[=>…\] - ETA: 1s - loss: 0.3842 - accuracy: 0.8938  
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45984/60000 \[=>…\] - ETA: 1s - loss: 0.3804 - accuracy: 0.8949  
46560/60000 \[>…\] - ETA: 1s - loss: 0.3786 - accuracy: 0.8953  
47168/60000 \[>…\] - ETA: 1s - loss: 0.3767 - accuracy: 0.8958  
47808/60000 \[>…\] - ETA: 1s - loss: 0.3744 - accuracy: 0.8963  
48416/60000 \[=>…\] - ETA: 1s - loss: 0.3732 - accuracy: 0.8966  
48928/60000 \[=>…\] - ETA: 0s - loss: 0.3714 - accuracy: 0.8971  
49440/60000 \[=>…\] - ETA: 0s - loss: 0.3701 - accuracy: 0.8974  
50048/60000 \[>…\] - ETA: 0s - loss: 0.3678 - accuracy: 0.8979  
50688/60000 \[>…\] - ETA: 0s - loss: 0.3669 - accuracy: 0.8983  
51264/60000 \[>…\] - ETA: 0s - loss: 0.3654 - accuracy: 0.8988  
51872/60000 \[>…\] - ETA: 0s - loss: 0.3636 - accuracy: 0.8992  
52608/60000 \[=>…\] - ETA: 0s - loss: 0.3618 - accuracy: 0.8997  
53376/60000 \[=>…\] - ETA: 0s - loss: 0.3599 - accuracy: 0.9003  
54048/60000 \[>…\] - ETA: 0s - loss: 0.3583 - accuracy: 0.9006  
54560/60000 \[>…\] - ETA: 0s - loss: 0.3568 - accuracy: 0.9010  
55296/60000 \[>…\] - ETA: 0s - loss: 0.3548 - accuracy: 0.9016  
56064/60000 \[=>…\] - ETA: 0s - loss: 0.3526 - accuracy: 0.9021  
56736/60000 \[=>…\] - ETA: 0s - loss: 0.3514 - accuracy: 0.9026  
57376/60000 \[=>…\] - ETA: 0s - loss: 0.3499 - accuracy: 0.9029  
58112/60000 \[>.\] - ETA: 0s - loss: 0.3482 - accuracy: 0.9033  
58880/60000 \[>.\] - ETA: 0s - loss: 0.3459 - accuracy: 0.9039  
59584/60000 \[>.\] - ETA: 0s - loss: 0.3444 - accuracy: 0.9043  
60000/60000 \[==================\] - 5s 87us/step - loss: 0.3435 - accuracy: 0.9046

Epoch 2/2

32/60000 \[…\] - ETA: 11s - loss: 0.0655 - accuracy: 1.0000  
736/60000 \[…\] - ETA: 4s - loss: 0.2135 - accuracy: 0.9389  
1408/60000 \[…\] - ETA: 4s - loss: 0.2217 - accuracy: 0.9361  
1984/60000 \[…\] - ETA: 4s - loss: 0.2316 - accuracy: 0.9390  
2432/60000 \[>…\] - ETA: 4s - loss: 0.2280 - accuracy: 0.9379  
3040/60000 \[>…\] - ETA: 4s - loss: 0.2374 - accuracy: 0.9368  
3808/60000 \[>…\] - ETA: 4s - loss: 0.2251 - accuracy: 0.9386  
4576/60000 \[=>…\] - ETA: 4s - loss: 0.2225 - accuracy: 0.9379  
5216/60000 \[=>…\] - ETA: 4s - loss: 0.2208 - accuracy: 0.9377  
5920/60000 \[=>…\] - ETA: 4s - loss: 0.2173 - accuracy: 0.9383  
6656/60000 \[>…\] - ETA: 4s - loss: 0.2217 - accuracy: 0.9370  
7392/60000 \[>…\] - ETA: 4s - loss: 0.2224 - accuracy: 0.9360  
8096/60000 \[=>…\] - ETA: 4s - loss: 0.2234 - accuracy: 0.9363  
8800/60000 \[=>…\] - ETA: 3s - loss: 0.2235 - accuracy: 0.9358  
9408/60000 \[=>…\] - ETA: 3s - loss: 0.2196 - accuracy: 0.9365  
10016/60000 \[>…\] - ETA: 3s - loss: 0.2207 - accuracy: 0.9363  
10592/60000 \[>…\] - ETA: 3s - loss: 0.2183 - accuracy: 0.9369  
11168/60000 \[>…\] - ETA: 3s - loss: 0.2177 - accuracy: 0.9377  
11776/60000 \[>…\] - ETA: 3s - loss: 0.2154 - accuracy: 0.9385  
12544/60000 \[=>…\] - ETA: 3s - loss: 0.2152 - accuracy: 0.9393  
13216/60000 \[=>…\] - ETA: 3s - loss: 0.2163 - accuracy: 0.9390  
13920/60000 \[=>…\] - ETA: 3s - loss: 0.2155 - accuracy: 0.9391  
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15424/60000 \[>…\] - ETA: 3s - loss: 0.2143 - accuracy: 0.9398  
16032/60000 \[=>…\] - ETA: 3s - loss: 0.2122 - accuracy: 0.9405  
16672/60000 \[=>…\] - ETA: 3s - loss: 0.2096 - accuracy: 0.9409  
17344/60000 \[=>…\] - ETA: 3s - loss: 0.2091 - accuracy: 0.9411  
18112/60000 \[>…\] - ETA: 3s - loss: 0.2086 - accuracy: 0.9416  
18784/60000 \[>…\] - ETA: 3s - loss: 0.2084 - accuracy: 0.9418  
19392/60000 \[>…\] - ETA: 3s - loss: 0.2076 - accuracy: 0.9418  
20000/60000 \[=>…\] - ETA: 3s - loss: 0.2067 - accuracy: 0.9421  
20608/60000 \[=>…\] - ETA: 3s - loss: 0.2071 - accuracy: 0.9419  
21184/60000 \[=>…\] - ETA: 3s - loss: 0.2056 - accuracy: 0.9423  
21856/60000 \[=>…\] - ETA: 3s - loss: 0.2063 - accuracy: 0.9419  
22624/60000 \[>…\] - ETA: 2s - loss: 0.2059 - accuracy: 0.9421  
23328/60000 \[>…\] - ETA: 2s - loss: 0.2056 - accuracy: 0.9422  
23936/60000 \[>…\] - ETA: 2s - loss: 0.2051 - accuracy: 0.9423  
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25248/60000 \[=>…\] - ETA: 2s - loss: 0.2036 - accuracy: 0.9426  
26016/60000 \[>…\] - ETA: 2s - loss: 0.2031 - accuracy: 0.9424  
26656/60000 \[>…\] - ETA: 2s - loss: 0.2035 - accuracy: 0.9422  
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28128/60000 \[=>…\] - ETA: 2s - loss: 0.2045 - accuracy: 0.9418  
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40000/60000 \[=>…\] - ETA: 1s - loss: 0.1990 - accuracy: 0.9428  
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44992/60000 \[=>…\] - ETA: 1s - loss: 0.1972 - accuracy: 0.9437  
45664/60000 \[=>…\] - ETA: 1s - loss: 0.1966 - accuracy: 0.9438  
46176/60000 \[>…\] - ETA: 1s - loss: 0.1968 - accuracy: 0.9437  
46752/60000 \[>…\] - ETA: 1s - loss: 0.1969 - accuracy: 0.9438  
47488/60000 \[>…\] - ETA: 0s - loss: 0.1965 - accuracy: 0.9439  
48256/60000 \[=>…\] - ETA: 0s - loss: 0.1965 - accuracy: 0.9438  
48896/60000 \[=>…\] - ETA: 0s - loss: 0.1963 - accuracy: 0.9436  
49568/60000 \[=>…\] - ETA: 0s - loss: 0.1962 - accuracy: 0.9438  
50304/60000 \[>…\] - ETA: 0s - loss: 0.1965 - accuracy: 0.9437  
51072/60000 \[>…\] - ETA: 0s - loss: 0.1967 - accuracy: 0.9437  
51744/60000 \[>…\] - ETA: 0s - loss: 0.1961 - accuracy: 0.9439  
52480/60000 \[=>…\] - ETA: 0s - loss: 0.1957 - accuracy: 0.9439  
53248/60000 \[=>…\] - ETA: 0s - loss: 0.1959 - accuracy: 0.9438  
54016/60000 \[>…\] - ETA: 0s - loss: 0.1963 - accuracy: 0.9437  
54592/60000 \[>…\] - ETA: 0s - loss: 0.1965 - accuracy: 0.9436  
55168/60000 \[>…\] - ETA: 0s - loss: 0.1962 - accuracy: 0.9436  
55776/60000 \[>…\] - ETA: 0s - loss: 0.1959 - accuracy: 0.9437  
56448/60000 \[=>…\] - ETA: 0s - loss: 0.1965 - accuracy: 0.9437  
57152/60000 \[=>…\] - ETA: 0s - loss: 0.1958 - accuracy: 0.9439  
57824/60000 \[=>…\] - ETA: 0s - loss: 0.1956 - accuracy: 0.9438  
58560/60000 \[>.\] - ETA: 0s - loss: 0.1951 - accuracy: 0.9440  
59360/60000 \[>.\] - ETA: 0s - loss: 0.1947 - accuracy: 0.9440  
60000/60000 \[================\] - 5s 76us/step - loss: 0.1946 - accuracy: 0.9440

Testing------------

32/10000 \[…\] - ETA: 15s  
1248/10000 \[>…\] - ETA: 0s  
2656/10000 \[>…\] - ETA: 0s  
4064/10000 \[=>…\] - ETA: 0s  
5216/10000 \[>…\] - ETA: 0s  
6464/10000 \[>…\] - ETA: 0s  
7744/10000 \[>…\] - ETA: 0s  
9056/10000 \[>…\] - ETA: 0s  
9984/10000 \[>.\] - ETA: 0s  
10000/10000 \[================\] - 0s 47us/step  
test loss: 0.17407772153392434  
test accuracy: 0.9513000249862671

**补充知识：****Keras 搭建简单神经网络：顺序模型+回归问题**

多层全连接神经网络

每层神经元个数、神经网络层数、激活函数等可自由修改

使用不同的损失函数可适用于其他任务，比如：分类问题

这是Keras搭建神经网络模型最基础的方法之一，Keras还有其他进阶的方法，官网给出了一些基本使用方法：[Keras官网][Keras]

# 这里搭建了一个4层全连接神经网络（不算输入层），传入函数以及函数内部的参数均可自由修改
    def ann(X, y):
      '''
      X: 输入的训练集数据
      y: 训练集对应的标签
      '''

‘’‘初始化模型’’’

# 首先定义了一个顺序模型作为框架，然后往这个框架里面添加网络层 #

# 这是最基础搭建神经网络的方法之一 #

model = Sequential()

‘’‘开始添加网络层’’’

# Dense表示全连接层，第一层需要我们提供输入的维度 input\_shape #