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Multi_classification_DNd41.py
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Multi_classification_DNd41.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Apr 6 10:04:32 2021
@author: ASUS
"""
##多分类模型
import numpy as np
import tensorflow as tf
import tensorflow.keras.layers as layers
import matplotlib.pyplot as plt
######################数据准备#########################
## 1.获取数据
seqs_500_750=np.load("D:/workspace of spyder/毕业设计/my project data/datafile/DND41/DNd41_500_750.npy")
seqs_750_1000=np.load("D:/workspace of spyder/毕业设计/my project data/datafile/DND41/DNd41_750_1000.npy")
seqs_250_500=np.load("D:/workspace of spyder/毕业设计/my project data/datafile/DND41/DNd41_250_500.npy")
seqs_background=np.load("D:/workspace of spyder/毕业设计/my project data/datafile/DND41/DNd41_background.npy")
seqs_0_250=np.load("D:/workspace of spyder/毕业设计/my project data/datafile/DND41/DNd41_0_250.npy")
## 2.对各类样本进行one-hot编码
y_0_250=np.zeros((len(seqs_0_250),5))
y_0_250[:,0]=1
y_250_500=np.zeros((len(seqs_250_500),5))
y_250_500[:,1]=1
y_500_750=np.zeros((len(seqs_500_750),5))
y_500_750[:,2]=1
y_750_1000=np.zeros((len(seqs_750_1000),5))
y_750_1000[:,3]=1
y_background=np.zeros((len(seqs_background),5))
y_background[:,4]=1
x=np.append(seqs_0_250,seqs_250_500,axis=0)
x=np.append(x,seqs_500_750,axis=0)
x=np.append(x,seqs_750_1000,axis=0)
x=np.append(x,seqs_background[0:2000],axis=0)
y=np.append(y_0_250,y_250_500,axis=0)
y=np.append(y,y_500_750,axis=0)
y=np.append(y,y_750_1000,axis=0)
y=np.append(y,y_background[0:2000],axis=0)
index=[]
for i in range(len(x)):
index.append(i)
np.random.shuffle(index)
x=x[index]
y=y[index]
##数据集划分
from sklearn.model_selection import train_test_split
train_x,test_x,train_y,test_y=train_test_split(x,y)
############################建立模型#################
##1.设置网络结构参数
detctor_length=16
num_detector=64
num_hidden_unit=64
from tensorflow.keras import regularizers
weight_decay = 0.005
# kernel_regularizer=regularizers.l2(weight_decay)
##2.建立模型
model=tf.keras.Sequential()
model.add(layers.Conv1D(num_detector,detctor_length,input_shape=(train_x.shape[1:]),activation='relu',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.GlobalMaxPool1D())
model.add(layers.Dense(num_hidden_unit,activation='relu',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(num_hidden_unit,activation='relu',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(5,activation='softmax'))
##3. 模型编译
model.compile(optimizer=tf.keras.optimizers.RMSprop(),
loss='categorical_crossentropy',
metrics=['acc'])
##4. 模型训练
history = model.fit(train_x,train_y,
epochs=100,
batch_size=256,
validation_data=(test_x,test_y))
plt.plot(history.epoch,history.history.get('acc'),label='acc')
plt.plot(history.epoch,history.history.get('val_acc'),label='val_acc')
plt.legend()
##5. 保存模型
model.save('D:/workspace of spyder/毕业设计/my project data/model_file/multi_classification_DNd41.h5')
# 加载模型，同时加载了模型的结构、权重等信息
loaded_model = tf.keras.models.load_model('D:/workspace of spyder/毕业设计/my project data/model_file/multi_classification_DNd41.h5')