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基于CNN的鸢尾花分类器

花匠小妙招
2024-11-06 23:38

基于CNN的鸢尾花分类器

鸢尾花为结构化数据，包括四个特征，三个标签

本次使用CNN来进行分类

废话不多说，请看代码

#导入所要用到的包

from sklearn import datasets
from keras.layers import *
import keras.backend as K
from keras import Model
from keras.utils import plot_model,to_categorical
import pandas as pd
import numpy as np
from keras.utils import plot_model
from sklearn.utils import shuffle
import matplotlib.pyplot as plt

#划分特征和标签

iris = datasets.load_iris()
#iris = np.loadtxt('./iris.csv', delimiter='t')
X = np.array(iris['data'])
Y = np.array(iris['target'])

fea_cnt = len(X[0]) #特征个数
numb = max(Y)+1 # 分类类别个数

Y = np.array([to_categorical(y,numb,dtype='int32') for y in Y])

#构建模型，二维卷积
def build_model(fea_cnt,numb):
K.clear_session() #清除之前的模型，省得压满内存
inputs = Input(shape=(fea_cnt,), dtype='float32')
embds = Dense(64 ,activation='selu')(inputs)
embds = Reshape((8,8,1))(embds)
embds = Conv2D(1,(4,4),strides=(1,1),padding="valid")(embds)
embds = MaxPooling2D(pool_size=(2,2), strides=None, padding='valid')(embds)
embds = Flatten()(embds)
embds = Dense(64,activation='selu')(embds)
embds = Dense(32,activation='selu')(embds)
outputs = Dense(numb,activation='softmax')(embds)

model = Model(inputs=inputs, outputs=outputs)
model.compile(loss='categorical_crossentropy', optimizer = 'adam', metrics=['accuracy'])
#model.summary()
return model

#数据集划分，20%的数据为测试集

model = build_model(fea_cnt,numb)
X,Y = shuffle(X,Y, random_state=1337) #打乱顺序进行分隔
validation_split=0.2

train_X = X[:int(len(X)*(1-validation_split))]
train_Y = Y[:int(len(Y)*(1-validation_split))]
test_X = X[int(len(X)*(1-validation_split)):]
test_Y = Y[int(len(Y)*(1-validation_split)):]

#训练模型
epochs = 12
batch_size = 4
model.fit(train_X ,train_Y
,batch_size = batch_size
,epochs = epochs
,validation_data= [test_X,test_Y]
)

plot_model(model, to_file='./model.png',show_shapes=True,dpi=300) #输出框架图
score = model.evaluate(test_X, test_Y)

#打印准确度与损失值

print('Test loss:', score[0])
print('Test accuracy:', score[1])