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
           

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

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>]