Python实现Keras搭建神经网络训练分类模型教程

Python实现Keras搭建神经网络训练分类模型教程

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
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46560/60000 [>…] - ETA: 1s - loss: 0.3786 - accuracy: 0.8953
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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
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55776/60000 [>…] - ETA: 0s - loss: 0.1959 - accuracy: 0.9437
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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

# 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

Activation表示每层的激活函数，可以传入预定义的激活函数，也可以传入符合接口规则的其他高级激活函数

model.add(Dense(64, input_shape=(X.shape[1],)))
model.add(Activation(‘sigmoid’))

model.add(Dense(256))
model.add(Activation(‘relu’))

model.add(Dense(256))
model.add(Activation(‘tanh’))

model.add(Dense(32))
model.add(Activation(‘tanh’))

输出层，输出的维度大小由具体任务而定

这里是一维输出的回归问题

model.add(Dense(1))
model.add(Activation(‘linear’))

‘’‘模型编译’’’

optimizer表示优化器（可自由选择），loss表示使用哪一种

model.compile(optimizer=‘rmsprop’, loss=‘mean_squared_error’)

自定义学习率，也可以使用原始的基础学习率

reduce_lr = ReduceLROnPlateau(monitor=‘loss’, factor=0.1, patience=10,
verbose=0, mode=‘auto’, min_delta=0.001,
cooldown=0, min_lr=0)

‘’‘模型训练’’’

这里的模型也可以先从函数返回后，再进行训练

epochs表示训练的轮数，batch_size表示每次训练的样本数量（小批量学习），validation_split表示用作验证集的训练数据的比例

callbacks表示回调函数的集合，用于模型训练时查看模型的内在状态和统计数据，相应的回调函数方法会在各自的阶段被调用

verbose表示输出的详细程度，值越大输出越详细

model.fit(X, y, epochs=100,
batch_size=50, validation_split=0.0,
callbacks=[reduce_lr],
verbose=0)

打印模型结构

print(model.summary())

return model

下图是此模型的结构图，其中下划线后面的数字是根据调用次数而定

以上这篇Python实现Keras搭建神经网络训练分类模型教程就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多