tensorflow逻辑回归实例笔记

import tensorflow as tf 
import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
%matplotlib inline

data = pd.read_csv("C:\\Users\\94823\\Desktop\\tensorflow学习需要的数据集\\credit-a.csv",header=None)

data

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
0	0	30.83	0.000	0	0	9	0	1.25	0	0	1	1	0	202	0.0	-1
1	1	58.67	4.460	0	0	8	1	3.04	0	0	6	1	0	43	560.0	-1
2	1	24.50	0.500	0	0	8	1	1.50	0	1	0	1	0	280	824.0	-1
3	0	27.83	1.540	0	0	9	0	3.75	0	0	5	0	0	100	3.0	-1
4	0	20.17	5.625	0	0	9	0	1.71	0	1	0	1	2	120	0.0	-1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
648	0	21.08	10.085	1	1	11	1	1.25	1	1	0	1	0	260	0.0	1
649	1	22.67	0.750	0	0	0	0	2.00	1	0	2	0	0	200	394.0	1
650	1	25.25	13.500	1	1	13	7	2.00	1	0	1	0	0	200	1.0	1
651	0	17.92	0.205	0	0	12	0	0.04	1	1	0	1	0	280	750.0	1
652	0	35.00	3.375	0	0	0	1	8.29	1	1	0	0	0	0	0.0	1

653 rows × 16 columns

data.iloc[:,-1].value_counts()  # 最后一列值的统计个数

 1    357
-1    296
Name: 15, dtype: int64

x = data.iloc[:,:-1]
y = data.iloc[:,-1].replace(-1,0)  # 把最后一列的0替换成-1

model = tf.keras.Sequential()

model.add(tf.keras.layers.Dense(4,input_shape=(15,),activation='relu'))model.add(tf.keras.layers.Dense(4,activation='relu')) # input_shape他会自己推断model.add(tf.keras.layers.Dense(1,activation='sigmoid')) # 输出层

model.summary()

Model: "sequential"_________________________________________________________________Layer (type)                 Output Shape              Param #   =================================================================dense (Dense)                (None, 4)                 64        _________________________________________________________________dense_1 (Dense)              (None, 4)                 20        _________________________________________________________________dense_2 (Dense)              (None, 1)                 5         =================================================================Total params: 89Trainable params: 89Non-trainable params: 0_________________________________________________________________

model.compile(optimizer='adam',            loss='binary_crossentropy',  # 信息熵作为损失函数              metrics=['acc'])  # 正确率

• TP：正例预测正确的个数

  FP：负例预测错误的个数

  TN：负例预测正确的个数

  FN：正例预测错误的个数

  \(acc =\frac{TP+TN}{TP+TN+FP+FN}\)是一个评价函数

history = model.fit(x,y,epochs=100)

Epoch 95/10021/21 [==============================] - 0s 1ms/step - loss: 0.5220 - acc: 0.7458Epoch 96/10021/21 [==============================] - 0s 1ms/step - loss: 0.4917 - acc: 0.7565Epoch 97/10021/21 [==============================] - 0s 2ms/step - loss: 0.4887 - acc: 0.7580Epoch 98/10021/21 [==============================] - 0s 1ms/step - loss: 0.5376 - acc: 0.7534Epoch 99/10021/21 [==============================] - 0s 1ms/step - loss: 0.5540 - acc: 0.7427Epoch 100/10021/21 [==============================] - 0s 2ms/step - loss: 0.5571 - acc: 0.7519

history.history.keys()

dict_keys(['loss', 'acc'])

plt.plot(history.epoch,history.history.get('loss'))# 损失函数的变化

[<matplotlib.lines.Line2D at 0x2a2e4891250>]

plt.plot(history.epoch,history.history.get('acc'))# 准确率的变化

[<matplotlib.lines.Line2D at 0x2a2e4979d60>]