LR實現，訓練集和測試集都是書上給的西瓜資料集3.0α，用keras實現程式碼如下：

# coding: utf-8

"""
使用keras實現logistic分類器
"""
import os
import gzip
import urllib

import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.optimizers import SGD
import keras
from keras.utils.np_utils import
 
 to_categorical
from keras.callbacks import ModelCheckpoint,Callback
current_dir = os.path.abspath(os.path.curdir)
np.random.seed(1337) # 每一次執行結果都一樣
def load_data():
#讀入csv資料,每一行的格式都是1,0.697,0.46,是\n
file = open('data/西瓜資料集3.0.csv'.decode('utf-8'))
data = [raw.strip('\n').split(',') for raw in file]
#X是密度和含糖率兩項資料，Y是西瓜是否好瓜的label,1是0否
 

X = [[float(raw[1]), float(raw[2])] for raw in data[1:]]
Y = [1 if raw[-1]=='是' else 0 for raw in data[1:]]
return X,Y

def create_model(input_dim, output_dim):
"""
建立logistic模型
:param input_dim: (int) 輸入維度
:param output_dim: (int) 輸出維度
:return: Sequential
"""
model = Sequential()
model.add(Dense(output_dim, input_dim=input_dim))
model.add(Activation('softmax'
 
))

sgd = SGD(lr=0.05)
model.compile(loss='categorical_crossentropy',
optimizer=sgd, metrics=["accuracy"])
return model

if __name__ == '__main__':

train_set_x, train_set_y = load_data()

train_set_x = np.asarray(train_set_x)
train_set_y = np.asarray(train_set_y)
input_dim = train_set_x.shape[1]
output_dim = train_set_y.max() - train_set_y.min() + 1
train_set_y = to_categorical(train_set_y)


model = create_model(input_dim, output_dim)
checkpointer = ModelCheckpoint(filepath="best_model.hdf5", monitor='val_acc', verbose=1, save_best_only=True)
# history = LossHistory()
earlyStopping = keras.callbacks.EarlyStopping(monitor='val_acc', patience=30, verbose=1, mode='auto')
history = model.fit(train_set_x,train_set_y, batch_size=3, nb_epoch=200, callbacks=[checkpointer, earlyStopping],
shuffle=True, verbose=0,
validation_data=(train_set_x,train_set_y))
result = model.predict(train_set_x,batch_size=20,verbose=1)
result = [np.argmax(i) for i in result]
print result
score = model.evaluate(train_set_x, train_set_y, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])