百度飞桨架构师手把手带你零基础实践深度学习——第二周实战深度学习

【百度飞桨架构师手把手带你零基础实践深度学习——第二周实战】
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
第二周实战

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

文章图片

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
总目录第二周实战题目要求：

通过查阅API，使用衰减学习率，通过多次调参数，找到一个最佳的衰减步长，使得loss比原代码中下降的更快
请自行绘制修改学习率前后的loss衰减图

注意：

原代码中仅需要更改学习率部分
若loss下降效果不明显，可自行调大epoch_num至10

# 初次运行时将注释取消，以便解压文件 # 如果已经解压过了，则不需要运行此段代码，否则文件已经存在解压会报错 !unzip -o -q -d /home/aistudio/work/palm /home/aistudio/data/data23828//training.zip %cd /home/aistudio/work/palm/PALM-Training400/ !unzip -o -q PALM-Training400.zip !unzip -o -q -d /home/aistudio/work/palm /home/aistudio/data/data23828//validation.zip !unzip -o -q -d /home/aistudio/work/palm /home/aistudio/data/data23828//valid_gt.zip

/home/aistudio/work/palm/PALM-Training400

import cv2 import random import numpy as np# 对读入的图像数据进行预处理 def transform_img(img): # 将图片尺寸缩放道 224x224 img = cv2.resize(img, (224, 224)) # 读入的图像数据格式是[H, W, C] # 使用转置操作将其变成[C, H, W] img = np.transpose(img, (2,0,1)) img = img.astype('float32') # 将数据范围调整到[-1.0, 1.0]之间 img = img / 255. img = img * 2.0 - 1.0 return img# 定义训练集数据读取器 def data_loader(datadir, batch_size=10, mode = 'train'): # 将datadir目录下的文件列出来，每条文件都要读入 filenames = os.listdir(datadir) def reader(): if mode == 'train': # 训练时随机打乱数据顺序 random.shuffle(filenames) batch_imgs = [] batch_labels = [] for name in filenames: filepath = os.path.join(datadir, name) img = cv2.imread(filepath) img = transform_img(img) if name[0] == 'H' or name[0] == 'N': # H开头的文件名表示高度近似，N开头的文件名表示正常视力 # 高度近视和正常视力的样本，都不是病理性的，属于负样本，标签为0 label = 0 elif name[0] == 'P': # P开头的是病理性近视，属于正样本，标签为1 label = 1 else: raise('Not excepted file name') # 每读取一个样本的数据，就将其放入数据列表中 batch_imgs.append(img) batch_labels.append(label) if len(batch_imgs) == batch_size: # 当数据列表的长度等于batch_size的时候， # 把这些数据当作一个mini-batch，并作为数据生成器的一个输出 imgs_array = np.array(batch_imgs).astype('float32') labels_array = np.array(batch_labels).astype('float32').reshape(-1, 1) yield imgs_array, labels_array batch_imgs = [] batch_labels = []if len(batch_imgs) > 0: # 剩余样本数目不足一个batch_size的数据，一起打包成一个mini-batch imgs_array = np.array(batch_imgs).astype('float32') labels_array = np.array(batch_labels).astype('float32').reshape(-1, 1) yield imgs_array, labels_arrayreturn reader# 定义验证集数据读取器 def valid_data_loader(datadir, csvfile, batch_size=10, mode='valid'): # 训练集读取时通过文件名来确定样本标签，验证集则通过csvfile来读取每个图片对应的标签 # 请查看解压后的验证集标签数据，观察csvfile文件里面所包含的内容 # csvfile文件所包含的内容格式如下，每一行代表一个样本， # 其中第一列是图片id，第二列是文件名，第三列是图片标签， # 第四列和第五列是Fovea的坐标，与分类任务无关 # ID,imgName,Label,Fovea_X,Fovea_Y # 1,V0001.jpg,0,1157.74,1019.87 # 2,V0002.jpg,1,1285.82,1080.47 # 打开包含验证集标签的csvfile，并读入其中的内容 filelists = open(csvfile).readlines() def reader(): batch_imgs = [] batch_labels = [] for line in filelists[1:]: line = line.strip().split(',') name = line[1] label = int(line[2]) # 根据图片文件名加载图片，并对图像数据作预处理 filepath = os.path.join(datadir, name) img = cv2.imread(filepath) img = transform_img(img) # 每读取一个样本的数据，就将其放入数据列表中 batch_imgs.append(img) batch_labels.append(label) if len(batch_imgs) == batch_size: # 当数据列表的长度等于batch_size的时候， # 把这些数据当作一个mini-batch，并作为数据生成器的一个输出 imgs_array = np.array(batch_imgs).astype('float32') labels_array = np.array(batch_labels).astype('float32').reshape(-1, 1) yield imgs_array, labels_array batch_imgs = [] batch_labels = []if len(batch_imgs) > 0: # 剩余样本数目不足一个batch_size的数据，一起打包成一个mini-batch imgs_array = np.array(batch_imgs).astype('float32') labels_array = np.array(batch_labels).astype('float32').reshape(-1, 1) yield imgs_array, labels_arrayreturn reader

# -*- coding: utf-8 -*-# LeNet 识别眼疾图片import os import random import paddle import paddle.fluid as fluid import numpy as np#引入VisualDL库，并设定保存作图数据的文件位置 from visualdl import LogWriter log_writer = LogWriter(logdir="./log")DATADIR = '/home/aistudio/work/palm/PALM-Training400/PALM-Training400' DATADIR2 = '/home/aistudio/work/palm/PALM-Validation400' CSVFILE = '/home/aistudio/labels.csv'# 定义训练过程 def train(model): import paddle.fluid as fluid with fluid.dygraph.guard(fluid.CUDAPlace(0)): itert=0 import os import random import paddle import paddle.fluid as fluid import numpy as np import matplotlib.pyplot as plt %matplotlib inline #引入VisualDL库，并设定保存作图数据的文件位置 from visualdl import LogWriter log_writer = LogWriter(logdir="./log") loss_list=[] DATADIR = '/home/aistudio/work/palm/PALM-Training400/PALM-Training400' DATADIR2 = '/home/aistudio/work/palm/PALM-Validation400' CSVFILE = '/home/aistudio/labels.csv' print('start training ... ') model.train() epoch_num = 5 # 定义优化器 opt = fluid.optimizer.Momentum(learning_rate=fluid.dygraph.CosineDecay(0.001, 500,5), momentum=0.9, parameter_list=model.parameters()) # 定义数据读取器，训练数据读取器和验证数据读取器 train_loader = data_loader(DATADIR, batch_size=10, mode='train') valid_loader = valid_data_loader(DATADIR2, CSVFILE) for epoch in range(epoch_num): for batch_id, data in enumerate(train_loader()): x_data, y_data = https://www.it610.com/article/data img = fluid.dygraph.to_variable(x_data) label = fluid.dygraph.to_variable(y_data) # 运行模型前向计算，得到预测值 logits = model(img) # 进行loss计算 loss = fluid.layers.sigmoid_cross_entropy_with_logits(logits, label) avg_loss = fluid.layers.mean(loss) loss_list.append(avg_loss.numpy()) if batch_id % 10 == 0: print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, avg_loss.numpy())) log_writer.add_scalar(tag = 'loss', step = itert, value = https://www.it610.com/article/avg_loss.numpy()) itert = itert + 10 plt.plot(loss_list) # 反向传播，更新权重，清除梯度 avg_loss.backward() opt.minimize(avg_loss) model.clear_gradients()model.eval() accuracies = [] losses = [] for batch_id, data in enumerate(valid_loader()): x_data, y_data = data img = fluid.dygraph.to_variable(x_data) label = fluid.dygraph.to_variable(y_data) # 运行模型前向计算，得到预测值 logits = model(img) # 二分类，sigmoid计算后的结果以0.5为阈值分两个类别 # 计算sigmoid后的预测概率，进行loss计算 pred = fluid.layers.sigmoid(logits) loss = fluid.layers.sigmoid_cross_entropy_with_logits(logits, label) # 计算预测概率小于0.5的类别 pred2 = pred * (-1.0) + 1.0 # 得到两个类别的预测概率，并沿第一个维度级联 pred = fluid.layers.concat([pred2, pred], axis=1) acc = fluid.layers.accuracy(pred, fluid.layers.cast(label, dtype='int64')) accuracies.append(acc.numpy()) losses.append(loss.numpy()) print("[validation] accuracy/loss: {}/{}".format(np.mean(accuracies), np.mean(losses))) model.train()# save params of model fluid.save_dygraph(model.state_dict(), 'palm') # save optimizer state fluid.save_dygraph(opt.state_dict(), 'palm')# 定义评估过程 def evaluation(model, params_file_path): with fluid.dygraph.guard(fluid.CUDAPlace(0)): print('start evaluation .......') #加载模型参数 model_state_dict, _ = fluid.load_dygraph(params_file_path) model.load_dict(model_state_dict)model.eval() eval_loader = data_loader(DATADIR, batch_size=10, mode='eval')acc_set = [] avg_loss_set = [] for batch_id, data in enumerate(eval_loader()): x_data, y_data = https://www.it610.com/article/data img = fluid.dygraph.to_variable(x_data) label = fluid.dygraph.to_variable(y_data) y_data = y_data.astype(np.int64) label_64 = fluid.dygraph.to_variable(y_data) # 计算预测和精度 prediction, acc = model(img, label_64) # 计算损失函数值 loss = fluid.layers.sigmoid_cross_entropy_with_logits(prediction, label) avg_loss = fluid.layers.mean(loss) acc_set.append(float(acc.numpy())) avg_loss_set.append(float(avg_loss.numpy())) # 求平均精度 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))

# -*- coding:utf-8 -*-# ResNet模型代码 import numpy as np import paddle import paddle.fluid as fluid from paddle.fluid.layer_helper import LayerHelper from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear from paddle.fluid.dygraph.base import to_variable# ResNet中使用了BatchNorm层，在卷积层的后面加上BatchNorm以提升数值稳定性 # 定义卷积批归一化块 class ConvBNLayer(fluid.dygraph.Layer): def __init__(self, num_channels, num_filters, filter_size, stride=1, groups=1, act=None): """num_channels, 卷积层的输入通道数 num_filters, 卷积层的输出通道数 stride, 卷积层的步幅 groups, 分组卷积的组数，默认groups=1不使用分组卷积 act, 激活函数类型，默认act=None不使用激活函数 """ super(ConvBNLayer, self).__init__()# 创建卷积层 self._conv = Conv2D( num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, groups=groups, act=None, bias_attr=False)# 创建BatchNorm层 self._batch_norm = BatchNorm(num_filters, act=act)def forward(self, inputs): y = self._conv(inputs) y = self._batch_norm(y) return y# 定义残差块 # 每个残差块会对输入图片做三次卷积，然后跟输入图片进行短接 # 如果残差块中第三次卷积输出特征图的形状与输入不一致，则对输入图片做1x1卷积，将其输出形状调整成一致 class BottleneckBlock(fluid.dygraph.Layer): def __init__(self, num_channels, num_filters, stride, shortcut=True): super(BottleneckBlock, self).__init__() # 创建第一个卷积层 1x1 self.conv0 = ConvBNLayer( num_channels=num_channels, num_filters=num_filters, filter_size=1, act='relu') # 创建第二个卷积层 3x3 self.conv1 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, act='relu') # 创建第三个卷积 1x1，但输出通道数乘以4 self.conv2 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters * 4, filter_size=1, act=None)# 如果conv2的输出跟此残差块的输入数据形状一致，则shortcut=True # 否则shortcut = False，添加1个1x1的卷积作用在输入数据上，使其形状变成跟conv2一致 if not shortcut: self.short = ConvBNLayer( num_channels=num_channels, num_filters=num_filters * 4, filter_size=1, stride=stride)self.shortcut = shortcutself._num_channels_out = num_filters * 4def forward(self, inputs): y = self.conv0(inputs) conv1 = self.conv1(y) conv2 = self.conv2(conv1)# 如果shortcut=True，直接将inputs跟conv2的输出相加 # 否则需要对inputs进行一次卷积，将形状调整成跟conv2输出一致 if self.shortcut: short = inputs else: short = self.short(inputs)y = fluid.layers.elementwise_add(x=short, y=conv2) layer_helper = LayerHelper(self.full_name(), act='relu') return layer_helper.append_activation(y)# 定义ResNet模型 class ResNet(fluid.dygraph.Layer): def __init__(self, layers=50, class_dim=1): """layers, 网络层数，可以是50, 101或者152 class_dim，分类标签的类别数 """ super(ResNet, self).__init__() self.layers = layers supported_layers = [50, 101, 152] assert layers in supported_layers, \ "supported layers are {} but input layer is {}".format(supported_layers, layers)if layers == 50: #ResNet50包含多个模块，其中第2到第5个模块分别包含3、4、6、3个残差块 depth = [3, 4, 6, 3] elif layers == 101: #ResNet101包含多个模块，其中第2到第5个模块分别包含3、4、23、3个残差块 depth = [3, 4, 23, 3] elif layers == 152: #ResNet50包含多个模块，其中第2到第5个模块分别包含3、8、36、3个残差块 depth = [3, 8, 36, 3]# 残差块中使用到的卷积的输出通道数 num_filters = [64, 128, 256, 512]# ResNet的第一个模块，包含1个7x7卷积，后面跟着1个最大池化层 self.conv = ConvBNLayer( num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu') self.pool2d_max = Pool2D( pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')# ResNet的第二到第五个模块c2、c3、c4、c5 self.bottleneck_block_list = [] num_channels = 64 for block in range(len(depth)): shortcut = False for i in range(depth[block]): bottleneck_block = self.add_sublayer( 'bb_%d_%d' % (block, i), BottleneckBlock( num_channels=num_channels, num_filters=num_filters[block], stride=2 if i == 0 and block != 0 else 1, # c3、c4、c5将会在第一个残差块使用stride=2；其余所有残差块stride=1 shortcut=shortcut)) num_channels = bottleneck_block._num_channels_out self.bottleneck_block_list.append(bottleneck_block) shortcut = True# 在c5的输出特征图上使用全局池化 self.pool2d_avg = Pool2D(pool_size=7, pool_type='avg', global_pooling=True)# stdv用来作为全连接层随机初始化参数的方差 import math stdv = 1.0 / math.sqrt(2048 * 1.0)# 创建全连接层，输出大小为类别数目 self.out = Linear(input_dim=2048, output_dim=class_dim, param_attr=fluid.param_attr.ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv)))def forward(self, inputs): y = self.conv(inputs) y = self.pool2d_max(y) for bottleneck_block in self.bottleneck_block_list: y = bottleneck_block(y) y = self.pool2d_avg(y) y = fluid.layers.reshape(y, [y.shape[0], -1]) y = self.out(y) return y