tebsorflow2.0 深度学习基础和tf.keras（上）

2022-10-18 169

版权

本文内容由阿里云实名注册用户自发贡献，版权归原作者所有，阿里云开发者社区不拥有其著作权，亦不承担相应法律责任。具体规则请查看《阿里云开发者社区用户服务协议》和《阿里云开发者社区知识产权保护指引》。如果您发现本社区中有涉嫌抄袭的内容，填写侵权投诉表单进行举报，一经查实，本社区将立刻删除涉嫌侵权内容。

简介： 1. tf.keras实现线性回归(1)1.1 Income数据导入可视化

1. tf.keras实现线性回归(1)

1.1 Income数据导入可视化

import tensorflow as tf
import pandas as pd
import matplotlib.pylab as plt
print('Tensorflow Version: {}'.format(tf.__version__))
data = pd.read_csv(r'C:\Users\lihuanyu\Desktop\数据集\Income1.csv')
plt.scatter(data.Education, data.Income)
plt.show()
data.head()

	Unnamed: 0	Education	Income
0	1	10.000000	26.658839
1	2	10.401338	27.306435
2	3	10.842809	22.132410
3	4	11.244147	21.169841
4	5	11.645485	15.192634

1.2 构建模型

data1 =  data.to_numpy()
x = data1[:,1]
y = data1[:,2]
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1,input_shape=(1,)))
#查看模型
model.summary()
model.compile(optimizer='adam',loss="mse")

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 1)                 2         
=================================================================
Total params: 2
Trainable params: 2
Non-trainable params: 0
_________________________________________________________________

1.3 训练

history = model.fit(x,y,epochs=10000)
plt.scatter(x,model.predict(x))
plt.scatter(data.Education, data.Income)
plt.show()

2. 多层感知机

2.1 概述

单层神经元的缺陷是神经元要求数据必须是线性可分的，而异或问题无法找到一条直线分割两个类。

为了继续使用神经网络解决这种不具备线性可分性的问题，采取在神经网络的输入端和输出端之间插入更多的神经元。

2.2 数据准备

import tensorflow as tf
print('Tensorflow Version: {}'.format(tf.__version__))
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
data = pd.read_csv(r'C:\Users\lihuanyu\Desktop\数据集\Advertising.csv')   
data1 = data.to_numpy()
data.head()

Tensorflow Version: 2.0.0

	Unnamed: 0	TV	radio	newspaper	sales
0	1	230.1	37.8	69.2	22.1
1	2	44.5	39.3	45.1	10.4
2	3	17.2	45.9	69.3	9.3
3	4	151.5	41.3	58.5	18.5
4	5	180.8	10.8	58.4	12.9

2.3 观察数据分布

plt.scatter(data.TV, data.sales)
plt.show()
• 1
• 2

plt.scatter(data.radio, data.sales)
plt.show()
• 1
• 2

plt.scatter(data.newspaper, data.sales)
plt.show()
• 1
• 2

2.4 建立模型及结果分析

x = data.iloc[:,1:-1].to_numpy()
y = data.iloc[:,-1].to_numpy()
model = tf.keras.Sequential([tf.keras.layers.Dense(10,input_shape=(3,),activation="relu"),
                             tf.keras.layers.Dense(1)]
                           )
model.compile(optimizer="adam",loss="mse")
model.summary()
model.fit(x,y,epochs=100)

Model: "sequential_5"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_10 (Dense)             (None, 10)                40        
_________________________________________________________________
dense_11 (Dense)             (None, 1)                 11        
=================================================================
Total params: 51
Trainable params: 51
Non-trainable params: 0
_________________________________________________________________
Train on 200 samples
Epoch 1/100
200/200 [==============================] - 0s 2ms/sample - loss: 759.2462
.......省略
200/200 [==============================] - 0s 122us/sample - loss: 2.1529
Epoch 97/100
200/200 [==============================] - 0s 130us/sample - loss: 2.1491
Epoch 98/100
200/200 [==============================] - 0s 100us/sample - loss: 2.1935
Epoch 99/100
200/200 [==============================] - 0s 170us/sample - loss: 2.1596
Epoch 100/100
200/200 [==============================] - 0s 145us/sample - loss: 2.1212

print(model.predict(x[0:5,:]),"\n",y[0:5].reshape(-1,1))

[[21.708485 ]
 [11.457169 ]
 [ 9.0395775]
 [18.80669  ]
 [12.999142 ]] 
 [[22.1]
 [10.4]
 [ 9.3]
 [18.5]
 [12.9]]
# 3 . softmax多分类任务

3.1 任务概述

采用全连接神经网络，实现对Fashion MNIST的分类。Fashion MNIST 数据集包含 70000 张灰度图像，涵盖 10个类别。我们将使用 60000 张图像训练网络，并使用 10000 张图像评估经过学习的网络分类图像的准确率。可以从 TensorFlow 直接访问 Fashion MNIST，只需导入和加载数据即可。