百度飞桨架构师手把手带你零基础实践深度学习——【第一周实践作业】深度学习

百度飞桨架构师手把手带你零基础实践深度学习——打卡计划

总目录
【第一周实践作业】

下面给出课程链接，欢迎各位小伙来来报考！本帖将持续更新。我只是飞桨的搬运工

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话不多说，这么良心的课程赶快扫码上车！https://aistudio.baidu.com/aistudio/education/group/info/1297?activityId=5&directly=1&shared=1
总目录【第一周实践作业】项目地址

import os import random import paddle import paddle.fluid as fluid from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear import numpy as np from PIL import Imageimport gzip import json

train_data=https://www.it610.com/article/paddle.dataset.mnist.train() test_data=paddle.dataset.mnist.test()

train_data=https://www.it610.com/article/paddle.reader.shuffle(train_data,100) test_data=paddle.reader.shuffle(test_data,100)

train_data=https://www.it610.com/article/paddle.batch(train_data,100) test_data=paddle.batch(test_data,100)

class MNIST(fluid.dygraph.Layer): def __init__(self): super(MNIST, self).__init__() # 定义一个卷积层，使用relu激活函数 self.conv1 = Conv2D(num_channels=1, num_filters=20, filter_size=5, stride=1, padding=2, act='relu') # 定义一个池化层，池化核为2，步长为2，使用最大池化方式 self.pool1 = Pool2D(pool_size=2, pool_stride=2, pool_type='max') # 定义一个卷积层，使用relu激活函数 self.conv2 = Conv2D(num_channels=20, num_filters=20, filter_size=5, stride=1, padding=2, act='relu') # 定义一个池化层，池化核为2，步长为2，使用最大池化方式 self.pool2 = Pool2D(pool_size=2, pool_stride=2, pool_type='max') # 定义一个全连接层，输出节点数为10 self.fc = Linear(input_dim=980, output_dim=10, act='softmax') # 定义网络的前向计算过程 def forward(self, inputs, label): x = self.conv1(inputs) x = self.pool1(x) x = self.conv2(x) x = self.pool2(x) x = fluid.layers.reshape(x, [x.shape[0], 980]) x = self.fc(x) if label is not None: acc = fluid.layers.accuracy(input=x, label=label) return x, acc else: return x

use_gpu = True place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace() from visualdl import LogWriter log_writer = LogWriter(logdir="./log") with fluid.dygraph.guard(place): model = MNIST() model.train() iter = 0 EPOCH_NUM = 5 BATCH_SIZE = 100 # 定义学习率，并加载优化器参数到模型中 total_steps = (int(60000//BATCH_SIZE) + 1) * EPOCH_NUM lr = fluid.dygraph.PolynomialDecay(0.01, total_steps, 0.001)# 使用Adam优化器 optimizer = fluid.optimizer.AdamOptimizer(learning_rate=lr, parameter_list=model.parameters())for epoch_id in range(EPOCH_NUM): for batch_id, data in enumerate(train_data()): #准备数据，变得更加简洁 img_data = https://www.it610.com/article/np.array([x[0] for x in data]).astype('float32').reshape(-1,1,28,28) # 获得图像标签数据，并转为float32类型的数组 label_data = https://www.it610.com/article/np.array([x[1] for x in data]).astype('int64').reshape(-1, 1) image = fluid.dygraph.to_variable(img_data) label = fluid.dygraph.to_variable(label_data)#前向计算的过程，同时拿到模型输出值和分类准确率 predict, acc = model(image, label) avg_acc = fluid.layers.mean(acc)#计算损失，取一个批次样本损失的平均值 loss = fluid.layers.cross_entropy(predict, label) avg_loss = fluid.layers.mean(loss)#每训练了200批次的数据，打印下当前Loss的情况 if batch_id % 200 == 0: print("epoch: {}, batch: {}, loss is: {}, acc is {}".format(epoch_id, batch_id, avg_loss.numpy(),avg_acc.numpy())) log_writer.add_scalar(tag = 'acc', step = iter, value = https://www.it610.com/article/avg_acc.numpy()) log_writer.add_scalar(tag ='loss', step = iter, value = https://www.it610.com/article/avg_loss.numpy()) iter = iter + 200#后向传播，更新参数的过程 avg_loss.backward() optimizer.minimize(avg_loss) model.clear_gradients()# 保存模型参数和优化器的参数 fluid.save_dygraph(model.state_dict(),'./checkpoint/mnist_epoch{}'.format(epoch_id)) fluid.save_dygraph(optimizer.state_dict(), './checkpoint/mnist_epoch{}'.format(epoch_id))

开始快乐炼丹吧！
最终效果

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with fluid.dygraph.guard(): print('start evaluation .......') #加载模型参数 model = MNIST() model_state_dict, _ = fluid.load_dygraph('checkpoint/mnist_epoch4.pdopt') model.load_dict(model_state_dict)model.eval()acc_set = [] avg_loss_set = [] for batch_id, data in enumerate(test_data()): x_data = https://www.it610.com/article/np.array([x[0] for x in data]).astype('float32').reshape(-1,1,28,28) # 获得图像标签数据，并转为float32类型的数组 y_data = https://www.it610.com/article/np.array([x[1] for x in data]).astype('int64').reshape(-1, 1) img = fluid.dygraph.to_variable(x_data) label = fluid.dygraph.to_variable(y_data) prediction, acc = model(img, label) loss = fluid.layers.cross_entropy(input=prediction, label=label) avg_loss = fluid.layers.mean(loss) acc_set.append(float(acc.numpy())) avg_loss_set.append(float(avg_loss.numpy()))#计算多个batch的平均损失和准确率 acc_val_mean = np.array(acc_set).mean() avg_loss_val_mean = np.array(avg_loss_set).mean()print('loss={}, acc={}'.format(avg_loss_val_mean, acc_val_mean))

【百度飞桨架构师手把手带你零基础实践深度学习——【第一周实践作业】】