paddle深度学习基础之模型加载及恢复训练

深度学习基础之模型加载及恢复训练"/>

paddle深度学习基础之模型加载及恢复训练

前言

前面几节，我们从各个方面对模型进行了优化，也实现了将模型保存下来。但是在日常训练工作中我们会遇到一些突发情况，导致训练过程主动或被动的中断。如果训练一个模型需要花费几天的训练时间，中断后从初始状态重新训练是不可接受的。别着急，这一节咱们就是讨论这个事情。

文章目录

• • 前言
  • 保存模型
  • 加载模型
  • 完整代码
  • 总结

保存模型

如果前面几篇博客看过的同学，肯定已经知道如何保存模型。这里还需要强调一下，我们不仅可以保存模型的参数，还可以保存优化器的参数。比如我们这次测试代码使用的是动态学习率的优化器，训练的次数不同，学习率也不一样，所有，我们也需要把这个信息给存储下来。

model_save_path="model/mnist-model/dygraph-mnist"
fluid.save_dygraph(model.state_dict(),model_save_path)#保存模型参数
fluid.save_dygraph(optimaizer.state_dict(),model_save_path)#保存优化器参数

加载模型

params_dict, opt_dict = fluid.load_dygraph(model_save_path)
model = MNIST()
model.load_dict(params_dict)optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.001,parameter_list=model.parameters())
optimizer.set_dict(opt_dict)

• params_dict ：模型的参数

• opt_dict ：优化器的参数

完整代码

import paddle
import numpy as np
import matplotlib.pyplot as plt
import gzip
import json
import paddle.fluid as fluid
from paddle.fluid.dygraph.nn import Linear
from paddle.fluid.dygraph.nn import Conv2D,Pool2D
from tb_paddle import SummaryWriter
from PIL import Image
import os
'''
此项目主要是在网络结构层面上优化模型
1.经典的全链接神经网络
'''#解压缩
mnistdata = gzip.open('data/mnist.json.gz')
#通过json导入  因为此数据使用json形式保存的，所以需要json导入 也可以使用pandas 数据导入方式
data = json.load(mnistdata)#分别获取训练集、验证集和测试集数据
train_data,val_data,test_data = data
#设置数据大小
IMG_ROWS=28
IMG_COLS=28
##数据乱序，生成批次数据
def data_loader(dataname='train',batch_size=20):#乱序处理方法1if(dataname=='train'):img = train_data[0]label = train_data[1]elif(dataname=='test'):img = test_data[0]label = test_data[1]elif(dataname=='val'):img = val_data[0]label = val_data[1]else:raise Exception("data only can be one of ['train','test','val']")#验证数据有效性assert len(img)==len(label),'the lenth of img must be the  same as the length of label'list = []datasize = len(img)list = [i for i in range(datasize)]#打乱数据np.random.shuffle(list)#获取数据,定义一个数据生成器def data_genergator():listdata=[]listlabel=[]for i in list:#转化数据结构imgdata = np.reshape(img[i],[1,IMG_ROWS,IMG_COLS]).astype('float32')labeldata = np.reshape(label[i],[1]).astype('int64')listdata.append(imgdata)listlabel.append(labeldata)if(len(listdata)%batch_size==0):yield np.array(listdata),np.array(listlabel)listlabel=[]listdata=[]if(len(listdata)>0):yield np.array(listdata),np.array(listlabel)return data_genergator
#定义类
class MNIST(fluid.dygraph.Layer):def __init__(self):super(MNIST, self).__init__()# self.linear1 = Linear(input_dim=28*28,output_dim=10,act=None)# self.linear2 = Linear(input_dim=10,output_dim=10,act='sigmoid')# self.linear3 = Linear(input_dim=10,output_dim=1,act='sigmoid')self.conv1 = Conv2D(num_channels=1, num_filters=20, filter_size=5, stride=1, padding=2, act='relu')self.pool1 = Pool2D(pool_size=2, pool_stride=2, pool_type='max')self.conv2 = Conv2D(num_channels=20, num_filters=20, filter_size=5, stride=1, padding=2, act='relu')self.pool2 = Pool2D(pool_size=2, pool_stride=2, pool_type='max')self.linear = Linear(input_dim=980, output_dim=10, act='softmax')def forward(self, inputs,label,check_shape=False,check_content=False):conv1 = self.conv1(inputs)pool1 = self.pool1(conv1)conv2 = self.conv2(pool1)pool2 = self.pool2(conv2)pool21 = fluid.layers.reshape(pool2, [pool2.shape[0], -1])outputs = self.linear(pool21)# hidden1 = self.linear1(inputs)# hidden2 = self.linear2(hidden1)# outputs = self.linear3(hidden2)if(check_shape):print("\n------------打印各个层设置的网络超参数的尺寸 -------------")print("conv1-- kernel_size:{}, padding:{}, stride:{}".format(self.conv1.weight.shape, self.conv1._padding, self.conv1._stride))print("conv2-- kernel_size:{}, padding:{}, stride:{}".format(self.conv2.weight.shape, self.conv2._padding, self.conv2._stride))print("pool1-- pool_type:{}, pool_size:{}, pool_stride:{}".format(self.pool1._pool_type, self.pool1._pool_size, self.pool1._pool_stride))print("pool2-- pool_type:{}, poo2_size:{}, pool_stride:{}".format(self.pool2._pool_type, self.pool2._pool_size, self.pool2._pool_stride))print("liner-- weight_size:{}, bias_size_{}, activation:{}".format(self.linear.weight.shape, self.linear.bias.shape, self.linear._act))print("\n------------打印各个层的形状 -------------")print("inputs_shape: {}".format(inputs.shape))print("outputs1_shape: {}".format(conv1.shape))print("outputs2_shape: {}".format(pool1.shape))print("outputs3_shape: {}".format(conv2.shape))print("outputs4_shape: {}".format(pool2.shape))print("outputs5_shape: {}".format(outputs.shape))if check_content:# 打印卷积层的参数-卷积核权重，权重参数较多，此处只打印部分参数print("\n########## print convolution layer's kernel ###############")print("conv1 params -- kernel weights:", self.conv1.weight[0][0])print("conv2 params -- kernel weights:", self.conv2.weight[0][0])# 创建随机数，随机打印某一个通道的输出值idx1 = np.random.randint(0, conv1.shape[1])idx2 = np.random.randint(0, conv1.shape[1])# 打印卷积-池化后的结果，仅打印batch中第一个图像对应的特征print("\nThe {}th channel of conv1 layer: ".format(idx1), conv1[0][idx1])print("The {}th channel of conv2 layer: ".format(idx2), conv1[0][idx2])print("The output of last layer:", conv1[0], '\n')if label is not None:acc = fluid.layers.accuracy(input=outputs,label=label)return outputs,accelse:return outputs
#训练
with fluid.dygraph.guard():model = MNIST()model.train()train_loader = data_loader()optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.001,parameter_list=model.parameters())place = fluid.CPUPlace()traindata_loader = fluid.io.DataLoader.from_generator(capacity=5, return_list=True)traindata_loader.set_batch_generator(train_loader, places=place)EPOCH_NUM = 3#添加日志data_writer = SummaryWriter(logdir="log/data")model_save_path="model/mnist-model/dygraph-mnist"for epoch_id in range(EPOCH_NUM):for batch_id,data in enumerate(traindata_loader()):image_data, label_data = dataimage = fluid.dygraph.to_variable(image_data)label = fluid.dygraph.to_variable(label_data)if batch_id==1000:predict,acc = model(image,label,check_shape=False,check_content=False)else:predict,acc = model(image,label)# loss = fluid.layers.square_error_cost(predict,label)loss = fluid.layers.cross_entropy(predict,label)avg_loss = fluid.layers.mean(loss)if batch_id !=0 and batch_id %100 ==0:data_writer.add_scalar("train/loss",avg_loss.numpy(),batch_id)data_writer.add_scalar("train/accuracy",acc.numpy(),batch_id)print("epoch:{},batch:{},loss is:{},acc is :{}".format(epoch_id,batch_id,avg_loss.numpy(),acc.numpy()))avg_loss.backward()optimizer.minimize(avg_loss)model.clear_gradients()print("保存模型")fluid.save_dygraph(model.state_dict(), model_save_path+""+str(epoch_id))fluid.save_dygraph(optimizer.state_dict(),model_save_path+""+str(epoch_id))
#再训练
print("接着训练")
with fluid.dygraph.guard():model = MNIST()model_save_path="model/mnist-model/dygraph-mnist"params_dict, opt_dict = fluid.load_dygraph(model_save_path+"0")model.load_dict(params_dict)train_loader = data_loader()optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.001,parameter_list=model.parameters())optimizer.set_dict(opt_dict)place = fluid.CPUPlace()traindata_loader = fluid.io.DataLoader.from_generator(capacity=5, return_list=True)traindata_loader.set_batch_generator(train_loader, places=place)EPOCH_NUM = 3#添加日志for epoch_id in range(1,EPOCH_NUM):for batch_id,data in enumerate(traindata_loader()):image_data, label_data = dataimage = fluid.dygraph.to_variable(image_data)label = fluid.dygraph.to_variable(label_data)predict,acc = model(image,label)# loss = fluid.layers.square_error_cost(predict,label)loss = fluid.layers.cross_entropy(predict,label)avg_loss = fluid.layers.mean(loss)if batch_id !=0 and batch_id %100 ==0:print("epoch:{},batch:{},loss is:{},acc is :{}".format(epoch_id,batch_id,avg_loss.numpy(),acc.numpy()))avg_loss.backward()optimizer.minimize(avg_loss)model.clear_gradients()

总结

截至到这篇博客，整个基础系列就总结结束了。这些资源都是百度AI Studio提供的免费课程，全程听完，实践后，真的是收获很多。也很感谢制作这些课程的工作人员，也同样希望，这一系列基础课程能够给大家带来一些帮助。