使用tf2做简单的2分类。

准备数据2fenlei.csv

创建 logtic_gress.py

#encoding=utf-8
# 多层感知器
# 3个特征值 推出一个2分类结果

import pandas as pd
import matplotlib.pyplot as plt
# from matplotlib.font_manager import FontProperties
import tensorflow as tf

# my_font = FontProperties(fname=r"c:windowsfontssimsun.ttc",size=16)
# data = pd.read_table("F:/AI/python_xx/2fenlei.csv",sep=,)

data = pd.read_csv("2fenlei.csv",sep=,)

# data.plot.scatter(x=TV,y=sort)
# plt.show()

print(data)

x = data.iloc[:,0:-1]  # 取不要最要最后一列的数
y = data.iloc[:,-1].replace(-1,0) # 取最后一列 并将-1替换为0  变成两类 0,1

print(y)

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(4,input_shape=(3,),activation=relu))
model.add(tf.keras.layers.Dense(4,activation=relu))
model.add(tf.keras.layers.Dense(1,activation=sigmoid)) # 映射到 0-1 之间
# model.summary()


model.compile(optimizer=adam,
                loss=binary_crossentropy,
                metrics=[acc])

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

# history.history.key()  # [loss, acc]

plt.plot(history.epoch, history.history.get(loss),bo,label=loss)
plt.plot(history.epoch, history.history.get(acc),g,label=acc)
plt.xlabel(Epochs)
plt.ylabel(loss+acc)
plt.legend(loc=best)  # 图例
plt.show()

print(model.predict(x[:1] ))  # 取第一个做预测[[0.9835632]]

结果：