AI-CNN-验证码识别
1 需求
GitHub - xhh890921/cnn-captcha-pytorch: 小黑黑讲AI，AI实战项目《验证码识别》
2 接口
3 示例
config.json
{"train_data_path": "./data/train-digit/","test_data_path": "./data/test-digit/","train_num": 2000,"test_num": 1000,"characters": "0123456789","digit_num": 1,"img_width": 200,"img_height": 100,"resize_width": 128,"resize_height": 128,"batch_size": 128,"epoch_num": 200,"learning_rate": 0.0001,"model_save_path": "./model/","model_name": "captcha.1digit.2k","test_model_path": "./model/captcha.1digit.2k"
}generate.py
# 导入验证码模块ImageCaptcha和随机数模块random
from captcha.image import ImageCaptcha
import random# 定义函数generate_data，用于生成验证码图片
# num是需要生成的验证码图片数量
# count是验证码图中包含的字符数量
# chars保存验证码中包含的字符
# path是图片结果的保存路径
# width是height是图片的宽和高
def generate_data(num, count, chars, path, width, height):# 使用变量i，循环生成num个验证码图片for i in range(num):# 打印当前的验证码编号print("generate %d"%(i))# 使用ImageCaptcha，创建验证码生成器generatorgenerator = ImageCaptcha(width=width, height=height)random_str = "" #保存验证码图片上的字符# 向random_str中，循环添加count个字符for j in range(count):# 每个字符，使用random.choice，随机的从chars中选择choose = random.choice(chars)random_str += choose# 调用generate_image，生成验证码图片imgimg = generator.generate_image(random_str)# 在验证码上加干扰点generator.create_noise_dots(img, '#000000', 4, 40)# 在验证码上加干扰线generator.create_noise_curve(img, '#000000')# 设置文件名，命名规则为，验证码字符串random_str，加下划线，加数据编号file_name = path + random_str + '_' + str(i) + '.jpg'img.save(file_name) # 保存文件import json
import osif __name__ == '__main__':# 使用open函数，打开config.json配置文件with open("config.json", "r") as f:# 使用json.load读取解析json，结果保存在configconfig = json.load(f)# 接着从配置中获取各项参数# 具体使用config加中括号中括号中为参数名，这样的方式读取配置内容train_data_path = config["train_data_path"] # 训练数据路径test_data_path = config["test_data_path"] # 测试数据路径train_num = config["train_num"] # 训练样本个数test_num = config["test_num"] # 测试样本个数characters = config["characters"] # 验证码使用的字符集digit_num = config["digit_num"] # 图片上的字符数量img_width = config["img_width"] # 图片的宽度img_height = config["img_height"] # 图片的高度# 检查数据路径上的文件夹是否存在# 如果不存在，则创建保存数据的文件夹if not os.path.exists(train_data_path):os.makedirs(train_data_path)if not os.path.exists(test_data_path):os.makedirs(test_data_path)# 调用generate_data，生成训练数据generate_data(train_num, digit_num, characters,train_data_path, img_width, img_height)# 调用generate_data，生成测试数据generate_data(test_num, digit_num, characters,test_data_path, img_width, img_height)dataset.py
from torch.utils.data import Dataset
from PIL import Image
import torch
import os# 设置CaptchaDataset继承Dataset，用于读取验证码数据
class CaptchaDataset(Dataset):# init函数用于初始化# 函数传入数据的路径data_dir和数据转换对象transform# 将验证码使用的字符集characters，通过参数传入def __init__(self, data_dir, transform, characters):self.file_list = list() #保存每个训练数据的路径# 使用os.listdir，获取data_dir中的全部文件files = os.listdir(data_dir)for file in files: #遍历files# 将目录路径与文件名组合为文件路径path = os.path.join(data_dir, file)# 将path添加到file_list列表self.file_list.append(path)# 将数据转换对象transform保存到类中self.transform = transform# 创建一个字符到数字的字典self.char2int = {}# 在创建字符到数字的字典时，使用外界传入的字符集charactersfor i, char in enumerate(characters):self.char2int[char] = idef __len__(self):# 直接返回数据集中的样本数量# 重写该方法后可以使用len(dataset)语法，来获取数据集的大小return len(self.file_list)# 函数传入索引index，函数应当返回与该索引对应的数据和标签# 通过dataset[i]，就可以获取到第i个样本了def __getitem__(self, index):file_path = self.file_list[index] #获取数据的路径# 打开文件，并使用convert('L')，将图片转换为灰色# 不需要通过颜色来判断验证码中的字符，转为灰色后，可以提升模型的鲁棒性image = Image.open(file_path).convert('L')# 使用transform转换数据，将图片数据转为张量数据image = self.transform(image)# 获取该数据图片中的字符标签label_char = os.path.basename(file_path).split('_')[0]# 在获取到该数据图片中的字符标签label_char后label = list()for char in label_char: # 遍历字符串label_char# 将其中的字符转为数字，添加到列表label中label.append(self.char2int[char])# 将label转为张量，作为训练数据的标签label = torch.tensor(label, dtype=torch.long)return image, label #返回image和labelfrom torch.utils.data import DataLoader
from torchvision import transforms
import jsonif __name__ == '__main__':with open("config.json", "r") as f:config = json.load(f)height = config["resize_height"] # 图片的高度width = config["resize_width"] # 图片的宽度# 定义数据转换对象transform# 使用transforms.Compose定义数据预处理流水线# 在transform添加Resize和ToTensor两个数据处理操作transform = transforms.Compose([transforms.Resize((height, width)), # 将图片缩放到指定的大小transforms.ToTensor()]) # 将图片数据转换为张量data_path = config["train_data_path"] # 训练数据储存路径characters = config["characters"] # 验证码使用的字符集batch_size = config["batch_size"]epoch_num = config["epoch_num"]# 定义CaptchaDataset对象datasetdataset = CaptchaDataset(data_path, transform, characters)# 定义数据加载器data_load# 其中参数dataset是数据集# batch_size=8代表每个小批量数据的大小是8# shuffle = True表示每个epoch，都会随机打乱数据的顺序data_load = DataLoader(dataset,batch_size = batch_size,shuffle = True)# 编写一个循环，模拟小批量梯度下降迭代时的数据读取# 外层循环，代表了整个训练数据集的迭代轮数，3个epoch就是3轮循环# 对于每个epoch，都会遍历全部的训练数据for epoch in range(epoch_num):print("epoch = %d"%(epoch))# 内层循环代表了，在一个迭代轮次中，以小批量的方式# 使用dataloader对数据进行遍历# batch_idx表示当前遍历的批次# data和label表示这个批次的训练数据和标记for batch_idx, (data, label) in enumerate(data_load):print("batch_idx = %d label = %s"%(batch_idx, label))model.py
import torch.nn as nn# 设置类CNNModel，它继承了torch.nn中的Module模块
class CNNModel(nn.Module):# 定义卷积神经网络# 修改初始化函数init的参数列表# 需要将训练图片的高height、宽width、# 图片中的字符数量digit_num，类别数量class_num传入def __init__(self, height, width, digit_num, class_num):super(CNNModel, self).__init__()self.digit_num = digit_num # 将digit_num保存在类中# 定义第1个卷积层组conv1# 其中包括了1个卷积层# 1个ReLU激活函数和1个2乘2的最大池化self.conv1 = nn.Sequential(# 卷积层使用Conv2d定义# 包括了1个输入通道，8个输出通道# 卷积核的大小是3乘3的# 使用padding='same'进行填充# 这样可以保证输入和输出的特征图大小相同nn.Conv2d(1, 32, kernel_size=3, padding='same'),nn.ReLU(),nn.MaxPool2d(2),nn.Dropout(0.25))# 第2个卷积层组，和conv1具有相同的结self.conv2 = nn.Sequential(# 包括8个输入通道和16个输出通道nn.Conv2d(32, 64, kernel_size=3, padding='same'),nn.ReLU(),nn.MaxPool2d(2),nn.Dropout(0.25))# 第3个卷积层组，和conv1具有相同的结self.conv3 = nn.Sequential(# 包括16个输入通道和16个输出通道nn.Conv2d(64, 64, kernel_size=3, padding='same'),nn.ReLU(),nn.MaxPool2d(2),nn.Dropout(0.25))# 完成三个卷积层的计算后，计算全连接层的输入数据数量input_num# 它等于图片的高和宽，分别除以8，再乘以输出特征图的个数16# 除以8的原因是，由于经过了3个2*2的最大池化# 因此图片的高和宽，都被缩小到原来的1/8input_num = (height//8) * (width//8) * 64self.fc1 = nn.Sequential(nn.Linear(input_num, 1024),nn.ReLU(),nn.Dropout(0.25))# 将输出层的神经元个数设置为class_numself.fc2 = nn.Sequential(nn.Linear(1024, class_num),)# 后面训练会使用交叉熵损失函数CrossEntropyLoss# softmax函数会定义在损失函数中，所以这里就不显示的定义了# 前向传播函数# 函数输入一个四维张量x# 这四个维度分别是样本数量、输入通道、图片的高度和宽度def forward(self, x): # [n, 1, 128, 128]# 将输入张量x按照顺序，输入至每一层中进行计算# 每层都会使张量x的维度发生变化out = self.conv1(x) # [n, 8, 64, 64]out = self.conv2(out) # [n, 16, 32, 32]out = self.conv3(out) # [n, 16, 16, 16]# 使用view函数，将张量的维度从n*16*16*16转为n*4096out = out.view(out.size(0), -1) # [n, 4096]out = self.fc1(out) # [n, 128]# 经过3个卷积层与2个全连接层后，会计算得到n*40的张量out = self.fc2(out) # [n, 40]# 使用初始化时传入的digit_num# 也就是将模型的最终输出，修改为n*digit_num*字符种类out = out.view(out.size(0), self.digit_num, -1)return outimport json
if __name__ == '__main__':with open("config.json", "r") as f:config = json.load(f)height = config["resize_height"] # 图片的高度width = config["resize_width"] # 图片的宽度characters = config["characters"] # 验证码使用的字符集digit_num = config["digit_num"]class_num = len(characters) * digit_num# 定义一个CNNModelUp1实例model = CNNModel(height, width, digit_num, class_num)print(model) #将其打印，观察打印结果可以了解模型的结构print("")train.py
# 直接导入dataset.py中的CaptchaDataset类
from dataset import CaptchaDataset
# 直接导入model.py中的CNNModel类
from model import CNNModelimport torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import transforms
from torch import optim
import json
import osif __name__ == '__main__':# 打开配置文件with open("config.json", "r") as f:config = json.load(f)# 读取resize_height和resize_width两个参数# 它们代表图片数据最终缩放的高和宽，用于创建transformheight = config["resize_height"] # 图片的高度width = config["resize_width"] # 图片的宽度# 定义数据转换对象transform# 使用transforms.Compose定义数据预处理流水线# 在transform添加Resize和ToTensor两个数据处理操作transform = transforms.Compose([transforms.RandomRotation(10), # 添加旋转方案transforms.Resize((height, width)), # 将图片缩放到指定的大小transforms.ToTensor()]) # 将图片数据转换为张量train_data_path = config["train_data_path"] # 获取训练数据路径characters = config["characters"] # 验证码字符集batch_size = config["batch_size"] # 批量大小epoch_num = config["epoch_num"] # 迭代轮数digit_num = config["digit_num"] # 字符个数learning_rate = config["learning_rate"] #迭代速率# 计算类别个数class_num，等于使用的字符数量*字符个数class_num = len(characters) * digit_nummodel_save_path = config["model_save_path"] #获取模型的保存路径model_name = config["model_name"] #模型名称model_save_name = model_save_path + "/" + model_name# 创建模型文件夹if not os.path.exists(model_save_path):os.makedirs(model_save_path)print("resize_height = %d"%(height))print("resize_width = %d" %(width))print("train_data_path = %s"%(train_data_path))print("characters = %s" % (characters))print("batch_size = %d" % (batch_size))print("epoch_num = %d" % (epoch_num))print("digit_num = %d" % (digit_num))print("class_num = %d" % (class_num))print("learning_rate = %lf" % (learning_rate))print("model_save_name = %s" % (model_save_name))print("")# 定义CaptchaDataset对象train_datatrain_data = CaptchaDataset(train_data_path, transform, characters)# 使用DataLoader，定义数据加载器train_load# 其中参数train_data是训练集# batch_size=64代表每个小批量数据的大小是64# shuffle = True表示每一轮训练，都会随机打乱数据的顺序train_load = DataLoader(train_data,batch_size = batch_size,shuffle = True)# 训练集有3000个数据，由于每个小批量大小是64，# 3000个数据就会分成47个小批量，前46个小批量包括64个数据，# 最后一个小批量包括56个数据。46*64+56=3000# 定义设备对象device，这里如果cuda可用则使用GPU，否则使用CPUdevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")# 创建一个CNNModel模型对象，并转移到GPU上model = CNNModel(height, width, digit_num, class_num).to(device)model.train() # 需要指定迭代速率。默认情况下是0.001，我们将迭代速率修改0.0001# 因为面对更复杂的数据，较小的迭代速率可以使迭代更稳定optimizer = optim.Adam(model.parameters(), lr=learning_rate)criterion = nn.CrossEntropyLoss() # 创建一个交叉熵损失函数print("Begin training:")# 提升迭代轮数，从50轮训练提升至200轮训练for epoch in range(epoch_num): # 外层循环，代表了整个训练数据集的遍历次数# 内层循环代表了，在一个epoch中，以批量的方式，使用train_load对于数据进行遍历# batch_idx 表示当前遍历的批次# (data, label) 表示这个批次的训练数据和标记。for batch_idx, (data, label) in enumerate(train_load):# 将数据data和标签label转移到GPU上data, label = data.to(device), label.to(device)# 使用当前的模型，预测训练数据data，结果保存在output中output = model(data)# 修改损失值loss的计算方法# 将4位验证码的每一位的损失，都累加到一起loss = torch.tensor(0.0).to(device)for i in range(digit_num): #使用i，循环4位验证码# 每一位验证码的模型计算输出为output[:, i, :]# 标记为label[:, i]# 交叉熵损失函数criterion，计算一位验证码的损失# 将4位验证码的损失，累加到lossloss += criterion(output[:, i, :], label[:, i])loss.backward() # 计算损失函数关于模型参数的梯度optimizer.step() # 更新模型参数optimizer.zero_grad() # 将梯度清零，以便于下一次迭代# 计算训练时每个batch的正确率accpredicted = torch.argmax(output, dim=2)correct = (predicted == label).all(dim=1).sum().item()acc = correct / data.size(0)# 对于每个epoch，每训练10个batch，打印一次当前的损失if batch_idx % 10 == 0:print(f"Epoch {epoch + 1}/{epoch_num} "f"| Batch {batch_idx}/{len(train_load)} "f"| Loss: {loss.item():.4f} "f"| accuracy {correct}/{data.size(0)}={acc:.3f}")# 每10轮训练，就保存一次checkpoint模型，用来调试使用if (epoch + 1) % 10 == 0:checkpoint = model_save_path + "/check.epoch" + str(epoch+1)torch.save(model.state_dict(), checkpoint)print("checkpoint saved : %s" % (checkpoint))# 程序的最后，使用配置中的路径，保存训练结果torch.save(model.state_dict(), model_save_name)print("model saved : %s" % (model_save_name))test.py
from dataset import CaptchaDataset
from model import CNNModelimport torch
from torch.utils.data import DataLoader
import torchvision.transforms as transformsimport jsonif __name__ == '__main__':with open("config.json", "r") as f:config = json.load(f)height = config["resize_height"] # 图片的高度width = config["resize_width"] # 图片的宽度# 定义数据转换对象transform# 将图片缩放到指定的大小，并将图片数据转换为张量transform = transforms.Compose([transforms.Resize((height, width)),transforms.ToTensor()])test_data_path = config["test_data_path"] # 训练数据储存路径characters = config["characters"] # 验证码使用的字符集digit_num = config["digit_num"]class_num = len(characters) * digit_numtest_model_path = config["test_model_path"]print("resize_height = %d" % (height))print("resize_width = %d" % (width))print("test_data_path = %s" % (test_data_path))print("characters = %s" % (characters))print("digit_num = %d" % (digit_num))print("class_num = %d" % (class_num))print("test_model_path = %s" % (test_model_path))print("")# 使用CaptchaDataset构造测试数据集test_data = CaptchaDataset(test_data_path, transform, characters)# 使用DataLoader读取test_data# 不需要设置任何参数，这样会一个一个数据的读取test_loader = DataLoader(test_data)# 定义设备对象device，这里如果cuda可用则使用GPU，否则使用CPUdevice = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")# 创建一个CNNModel模型对象，并转移到GPU上model = CNNModel(height, width, digit_num, class_num).to(device)model.eval()# 调用load_state_dict，读取已经训练好的模型文件captcha.digitmodel.load_state_dict(torch.load(test_model_path))right = 0 # 设置right变量，保存预测正确的样本数量all = 0 # all保存全部的样本数量# 遍历test_loader中的数据# x表示样本的特征张量，y表示样本的标签for (x, y) in test_loader:x, y = x.to(device), y.to(device) # 转移数据至GPUpred = model(x) # 使用模型预测x的结果，保存在pred中# 使用pred.argmax(dim=2).squeeze(0)，获取4位验证码数据的预测结果# y.squeeze(0)是4验证码的标记结果if torch.equal(pred.argmax(dim=2).squeeze(0),y.squeeze(0)):right += 1 # 如果相同，那么right加1all += 1 # 每次循环，all变量加1# 循环结束后，计算模型的正确率acc = right * 1.0 / allprint("test accuracy = %d / %d = %.3lf" % (right, all, acc))D:\Python310\python.exe D:/project/PycharmProjects/CNN/train.py
resize_height = 128
resize_width = 128
train_data_path = ./data/train-digit/
characters = 0123456789
batch_size = 128
epoch_num = 200
digit_num = 1
class_num = 10
learning_rate = 0.000100
model_save_name = ./model//captcha.1digit.2kBegin training:
Epoch 1/200 | Batch 0/16 | Loss: 2.3091 | accuracy 15/128=0.117
Epoch 1/200 | Batch 10/16 | Loss: 2.3238 | accuracy 10/128=0.078
Epoch 2/200 | Batch 0/16 | Loss: 2.3016 | accuracy 14/128=0.109
Epoch 2/200 | Batch 10/16 | Loss: 2.3000 | accuracy 15/128=0.117
Epoch 3/200 | Batch 0/16 | Loss: 2.3062 | accuracy 13/128=0.102
Epoch 3/200 | Batch 10/16 | Loss: 2.3053 | accuracy 12/128=0.094
Epoch 4/200 | Batch 0/16 | Loss: 2.3071 | accuracy 15/128=0.117
Epoch 4/200 | Batch 10/16 | Loss: 2.3018 | accuracy 18/128=0.141
Epoch 5/200 | Batch 0/16 | Loss: 2.2999 | accuracy 14/128=0.109
Epoch 5/200 | Batch 10/16 | Loss: 2.3003 | accuracy 17/128=0.133
Epoch 6/200 | Batch 0/16 | Loss: 2.3056 | accuracy 10/128=0.078
Epoch 6/200 | Batch 10/16 | Loss: 2.3008 | accuracy 17/128=0.133
Epoch 7/200 | Batch 0/16 | Loss: 2.3007 | accuracy 10/128=0.078
Epoch 7/200 | Batch 10/16 | Loss: 2.3061 | accuracy 10/128=0.078
Epoch 8/200 | Batch 0/16 | Loss: 2.3027 | accuracy 16/128=0.125
Epoch 8/200 | Batch 10/16 | Loss: 2.3041 | accuracy 11/128=0.086
Epoch 9/200 | Batch 0/16 | Loss: 2.3063 | accuracy 14/128=0.109
Epoch 9/200 | Batch 10/16 | Loss: 2.3000 | accuracy 12/128=0.094
Epoch 10/200 | Batch 0/16 | Loss: 2.2981 | accuracy 17/128=0.133
Epoch 10/200 | Batch 10/16 | Loss: 2.3018 | accuracy 17/128=0.133
checkpoint saved : ./model//check.epoch10
Epoch 11/200 | Batch 0/16 | Loss: 2.3048 | accuracy 13/128=0.102
Epoch 11/200 | Batch 10/16 | Loss: 2.3009 | accuracy 18/128=0.141
Epoch 12/200 | Batch 0/16 | Loss: 2.3007 | accuracy 5/128=0.039
Epoch 12/200 | Batch 10/16 | Loss: 2.3052 | accuracy 13/128=0.102
Epoch 13/200 | Batch 0/16 | Loss: 2.3016 | accuracy 15/128=0.117
Epoch 13/200 | Batch 10/16 | Loss: 2.2970 | accuracy 16/128=0.125
Epoch 14/200 | Batch 0/16 | Loss: 2.2986 | accuracy 19/128=0.148
Epoch 14/200 | Batch 10/16 | Loss: 2.3021 | accuracy 14/128=0.109
Epoch 15/200 | Batch 0/16 | Loss: 2.2987 | accuracy 17/128=0.133
Epoch 15/200 | Batch 10/16 | Loss: 2.3041 | accuracy 14/128=0.109
Epoch 16/200 | Batch 0/16 | Loss: 2.2994 | accuracy 16/128=0.125
Epoch 16/200 | Batch 10/16 | Loss: 2.3019 | accuracy 16/128=0.125
Epoch 17/200 | Batch 0/16 | Loss: 2.2933 | accuracy 14/128=0.109
Epoch 17/200 | Batch 10/16 | Loss: 2.2991 | accuracy 12/128=0.094
Epoch 18/200 | Batch 0/16 | Loss: 2.3012 | accuracy 16/128=0.125
Epoch 18/200 | Batch 10/16 | Loss: 2.3045 | accuracy 13/128=0.102
Epoch 19/200 | Batch 0/16 | Loss: 2.2907 | accuracy 25/128=0.195
Epoch 19/200 | Batch 10/16 | Loss: 2.3016 | accuracy 10/128=0.078
Epoch 20/200 | Batch 0/16 | Loss: 2.3050 | accuracy 13/128=0.102
Epoch 20/200 | Batch 10/16 | Loss: 2.2988 | accuracy 14/128=0.109
checkpoint saved : ./model//check.epoch20
Epoch 21/200 | Batch 0/16 | Loss: 2.2999 | accuracy 17/128=0.133
Epoch 21/200 | Batch 10/16 | Loss: 2.2937 | accuracy 15/128=0.117
Epoch 22/200 | Batch 0/16 | Loss: 2.3047 | accuracy 16/128=0.125
Epoch 22/200 | Batch 10/16 | Loss: 2.2853 | accuracy 18/128=0.141
Epoch 23/200 | Batch 0/16 | Loss: 2.2850 | accuracy 19/128=0.148
Epoch 23/200 | Batch 10/16 | Loss: 2.2959 | accuracy 13/128=0.102
Epoch 24/200 | Batch 0/16 | Loss: 2.2884 | accuracy 18/128=0.141
Epoch 24/200 | Batch 10/16 | Loss: 2.2940 | accuracy 18/128=0.141
Epoch 25/200 | Batch 0/16 | Loss: 2.2775 | accuracy 18/128=0.141
Epoch 25/200 | Batch 10/16 | Loss: 2.2858 | accuracy 15/128=0.117
Epoch 26/200 | Batch 0/16 | Loss: 2.2522 | accuracy 27/128=0.211
Epoch 26/200 | Batch 10/16 | Loss: 2.3032 | accuracy 16/128=0.125
Epoch 27/200 | Batch 0/16 | Loss: 2.2583 | accuracy 24/128=0.188
Epoch 27/200 | Batch 10/16 | Loss: 2.2422 | accuracy 28/128=0.219
Epoch 28/200 | Batch 0/16 | Loss: 2.2255 | accuracy 29/128=0.227
Epoch 28/200 | Batch 10/16 | Loss: 2.2325 | accuracy 16/128=0.125
Epoch 29/200 | Batch 0/16 | Loss: 2.1752 | accuracy 28/128=0.219
Epoch 29/200 | Batch 10/16 | Loss: 2.2192 | accuracy 23/128=0.180
Epoch 30/200 | Batch 0/16 | Loss: 2.2291 | accuracy 18/128=0.141
Epoch 30/200 | Batch 10/16 | Loss: 2.1861 | accuracy 25/128=0.195
checkpoint saved : ./model//check.epoch30
Epoch 31/200 | Batch 0/16 | Loss: 2.1700 | accuracy 35/128=0.273
Epoch 31/200 | Batch 10/16 | Loss: 2.0598 | accuracy 33/128=0.258
Epoch 32/200 | Batch 0/16 | Loss: 2.1042 | accuracy 29/128=0.227
Epoch 32/200 | Batch 10/16 | Loss: 2.0796 | accuracy 27/128=0.211
Epoch 33/200 | Batch 0/16 | Loss: 2.1144 | accuracy 23/128=0.180
Epoch 33/200 | Batch 10/16 | Loss: 2.1632 | accuracy 26/128=0.203
Epoch 34/200 | Batch 0/16 | Loss: 2.0593 | accuracy 38/128=0.297
Epoch 34/200 | Batch 10/16 | Loss: 2.0564 | accuracy 37/128=0.289
Epoch 35/200 | Batch 0/16 | Loss: 1.9282 | accuracy 42/128=0.328
Epoch 35/200 | Batch 10/16 | Loss: 2.0059 | accuracy 36/128=0.281
Epoch 36/200 | Batch 0/16 | Loss: 2.0065 | accuracy 35/128=0.273
Epoch 36/200 | Batch 10/16 | Loss: 1.9090 | accuracy 42/128=0.328
Epoch 37/200 | Batch 0/16 | Loss: 1.9358 | accuracy 39/128=0.305
Epoch 37/200 | Batch 10/16 | Loss: 1.9197 | accuracy 45/128=0.352
Epoch 38/200 | Batch 0/16 | Loss: 1.9248 | accuracy 42/128=0.328
Epoch 38/200 | Batch 10/16 | Loss: 1.9072 | accuracy 40/128=0.312
Epoch 39/200 | Batch 0/16 | Loss: 1.9429 | accuracy 41/128=0.320
Epoch 39/200 | Batch 10/16 | Loss: 1.9401 | accuracy 39/128=0.305
Epoch 40/200 | Batch 0/16 | Loss: 1.8600 | accuracy 44/128=0.344
Epoch 40/200 | Batch 10/16 | Loss: 1.8164 | accuracy 46/128=0.359
checkpoint saved : ./model//check.epoch40
Epoch 41/200 | Batch 0/16 | Loss: 1.8458 | accuracy 48/128=0.375
Epoch 41/200 | Batch 10/16 | Loss: 1.7130 | accuracy 54/128=0.422
Epoch 42/200 | Batch 0/16 | Loss: 1.6807 | accuracy 53/128=0.414
Epoch 42/200 | Batch 10/16 | Loss: 1.8174 | accuracy 41/128=0.320
Epoch 43/200 | Batch 0/16 | Loss: 1.8646 | accuracy 40/128=0.312
Epoch 43/200 | Batch 10/16 | Loss: 1.6046 | accuracy 54/128=0.422
Epoch 44/200 | Batch 0/16 | Loss: 1.7627 | accuracy 43/128=0.336
Epoch 44/200 | Batch 10/16 | Loss: 1.7279 | accuracy 48/128=0.375
Epoch 45/200 | Batch 0/16 | Loss: 1.6728 | accuracy 50/128=0.391
Epoch 45/200 | Batch 10/16 | Loss: 1.6171 | accuracy 53/128=0.414
Epoch 46/200 | Batch 0/16 | Loss: 1.6969 | accuracy 51/128=0.398
Epoch 46/200 | Batch 10/16 | Loss: 1.6196 | accuracy 48/128=0.375
Epoch 47/200 | Batch 0/16 | Loss: 1.6617 | accuracy 56/128=0.438
Epoch 47/200 | Batch 10/16 | Loss: 1.5410 | accuracy 67/128=0.523
Epoch 48/200 | Batch 0/16 | Loss: 1.6146 | accuracy 55/128=0.430
Epoch 48/200 | Batch 10/16 | Loss: 1.7213 | accuracy 44/128=0.344
Epoch 49/200 | Batch 0/16 | Loss: 1.5919 | accuracy 61/128=0.477
Epoch 49/200 | Batch 10/16 | Loss: 1.5982 | accuracy 51/128=0.398
Epoch 50/200 | Batch 0/16 | Loss: 1.6092 | accuracy 59/128=0.461
Epoch 50/200 | Batch 10/16 | Loss: 1.4322 | accuracy 65/128=0.508
checkpoint saved : ./model//check.epoch50
Epoch 51/200 | Batch 0/16 | Loss: 1.5115 | accuracy 65/128=0.508
Epoch 51/200 | Batch 10/16 | Loss: 1.5191 | accuracy 58/128=0.453
Epoch 52/200 | Batch 0/16 | Loss: 1.5553 | accuracy 64/128=0.500
Epoch 52/200 | Batch 10/16 | Loss: 1.5587 | accuracy 60/128=0.469
Epoch 53/200 | Batch 0/16 | Loss: 1.5137 | accuracy 61/128=0.477
Epoch 53/200 | Batch 10/16 | Loss: 1.3685 | accuracy 67/128=0.523
Epoch 54/200 | Batch 0/16 | Loss: 1.6554 | accuracy 50/128=0.391
Epoch 54/200 | Batch 10/16 | Loss: 1.4803 | accuracy 59/128=0.461
Epoch 55/200 | Batch 0/16 | Loss: 1.3825 | accuracy 66/128=0.516
Epoch 55/200 | Batch 10/16 | Loss: 1.4612 | accuracy 62/128=0.484
Epoch 56/200 | Batch 0/16 | Loss: 1.3605 | accuracy 73/128=0.570
Epoch 56/200 | Batch 10/16 | Loss: 1.4856 | accuracy 66/128=0.516
Epoch 57/200 | Batch 0/16 | Loss: 1.5354 | accuracy 51/128=0.398
Epoch 57/200 | Batch 10/16 | Loss: 1.4573 | accuracy 59/128=0.461
Epoch 58/200 | Batch 0/16 | Loss: 1.3566 | accuracy 61/128=0.477
Epoch 58/200 | Batch 10/16 | Loss: 1.3901 | accuracy 63/128=0.492
Epoch 59/200 | Batch 0/16 | Loss: 1.3130 | accuracy 70/128=0.547
Epoch 59/200 | Batch 10/16 | Loss: 1.1667 | accuracy 76/128=0.594
Epoch 60/200 | Batch 0/16 | Loss: 1.3881 | accuracy 70/128=0.547
Epoch 60/200 | Batch 10/16 | Loss: 1.2703 | accuracy 68/128=0.531
checkpoint saved : ./model//check.epoch60
Epoch 61/200 | Batch 0/16 | Loss: 1.4010 | accuracy 62/128=0.484
Epoch 61/200 | Batch 10/16 | Loss: 1.3181 | accuracy 72/128=0.562
Epoch 62/200 | Batch 0/16 | Loss: 1.2716 | accuracy 69/128=0.539
Epoch 62/200 | Batch 10/16 | Loss: 1.3523 | accuracy 62/128=0.484
Epoch 63/200 | Batch 0/16 | Loss: 1.2137 | accuracy 78/128=0.609
Epoch 63/200 | Batch 10/16 | Loss: 1.2490 | accuracy 75/128=0.586
Epoch 64/200 | Batch 0/16 | Loss: 1.2601 | accuracy 77/128=0.602
Epoch 64/200 | Batch 10/16 | Loss: 1.2207 | accuracy 72/128=0.562
Epoch 65/200 | Batch 0/16 | Loss: 1.1812 | accuracy 73/128=0.570
Epoch 65/200 | Batch 10/16 | Loss: 1.2019 | accuracy 74/128=0.578
Epoch 66/200 | Batch 0/16 | Loss: 1.0996 | accuracy 77/128=0.602
Epoch 66/200 | Batch 10/16 | Loss: 1.1076 | accuracy 72/128=0.562
Epoch 67/200 | Batch 0/16 | Loss: 1.2806 | accuracy 71/128=0.555
Epoch 67/200 | Batch 10/16 | Loss: 1.2237 | accuracy 74/128=0.578
Epoch 68/200 | Batch 0/16 | Loss: 1.1196 | accuracy 81/128=0.633
Epoch 68/200 | Batch 10/16 | Loss: 1.1982 | accuracy 78/128=0.609
Epoch 69/200 | Batch 0/16 | Loss: 1.0038 | accuracy 93/128=0.727
Epoch 69/200 | Batch 10/16 | Loss: 1.2466 | accuracy 72/128=0.562
Epoch 70/200 | Batch 0/16 | Loss: 1.0274 | accuracy 79/128=0.617
Epoch 70/200 | Batch 10/16 | Loss: 1.0536 | accuracy 82/128=0.641
checkpoint saved : ./model//check.epoch70
Epoch 71/200 | Batch 0/16 | Loss: 1.1594 | accuracy 79/128=0.617
Epoch 71/200 | Batch 10/16 | Loss: 1.0447 | accuracy 80/128=0.625
Epoch 72/200 | Batch 0/16 | Loss: 1.2550 | accuracy 68/128=0.531
Epoch 72/200 | Batch 10/16 | Loss: 1.1217 | accuracy 79/128=0.617
Epoch 73/200 | Batch 0/16 | Loss: 1.0504 | accuracy 78/128=0.609
Epoch 73/200 | Batch 10/16 | Loss: 1.2043 | accuracy 77/128=0.602
Epoch 74/200 | Batch 0/16 | Loss: 1.0929 | accuracy 74/128=0.578
Epoch 74/200 | Batch 10/16 | Loss: 1.0416 | accuracy 82/128=0.641
Epoch 75/200 | Batch 0/16 | Loss: 0.9702 | accuracy 89/128=0.695
Epoch 75/200 | Batch 10/16 | Loss: 0.9303 | accuracy 95/128=0.742
Epoch 76/200 | Batch 0/16 | Loss: 0.8531 | accuracy 93/128=0.727
Epoch 76/200 | Batch 10/16 | Loss: 1.0092 | accuracy 87/128=0.680
Epoch 77/200 | Batch 0/16 | Loss: 1.0739 | accuracy 78/128=0.609
Epoch 77/200 | Batch 10/16 | Loss: 1.0276 | accuracy 81/128=0.633
Epoch 78/200 | Batch 0/16 | Loss: 0.9078 | accuracy 91/128=0.711
Epoch 78/200 | Batch 10/16 | Loss: 0.9602 | accuracy 80/128=0.625
Epoch 79/200 | Batch 0/16 | Loss: 0.9347 | accuracy 85/128=0.664
Epoch 79/200 | Batch 10/16 | Loss: 0.9257 | accuracy 87/128=0.680
Epoch 80/200 | Batch 0/16 | Loss: 1.0276 | accuracy 84/128=0.656
Epoch 80/200 | Batch 10/16 | Loss: 0.8795 | accuracy 88/128=0.688
checkpoint saved : ./model//check.epoch80
Epoch 81/200 | Batch 0/16 | Loss: 0.7719 | accuracy 96/128=0.750
Epoch 81/200 | Batch 10/16 | Loss: 0.9031 | accuracy 90/128=0.703
Epoch 82/200 | Batch 0/16 | Loss: 0.8802 | accuracy 91/128=0.711
Epoch 82/200 | Batch 10/16 | Loss: 0.8708 | accuracy 88/128=0.688
Epoch 83/200 | Batch 0/16 | Loss: 0.8398 | accuracy 91/128=0.711
Epoch 83/200 | Batch 10/16 | Loss: 0.7149 | accuracy 99/128=0.773
Epoch 84/200 | Batch 0/16 | Loss: 0.7306 | accuracy 101/128=0.789
Epoch 84/200 | Batch 10/16 | Loss: 0.8610 | accuracy 92/128=0.719
Epoch 85/200 | Batch 0/16 | Loss: 0.8118 | accuracy 92/128=0.719
Epoch 85/200 | Batch 10/16 | Loss: 0.8698 | accuracy 94/128=0.734
Epoch 86/200 | Batch 0/16 | Loss: 0.7987 | accuracy 93/128=0.727
Epoch 86/200 | Batch 10/16 | Loss: 0.7173 | accuracy 101/128=0.789
Epoch 87/200 | Batch 0/16 | Loss: 0.7868 | accuracy 93/128=0.727
Epoch 87/200 | Batch 10/16 | Loss: 0.9372 | accuracy 80/128=0.625
Epoch 88/200 | Batch 0/16 | Loss: 0.8355 | accuracy 91/128=0.711
Epoch 88/200 | Batch 10/16 | Loss: 0.7740 | accuracy 93/128=0.727
Epoch 89/200 | Batch 0/16 | Loss: 0.8853 | accuracy 86/128=0.672
Epoch 89/200 | Batch 10/16 | Loss: 0.7612 | accuracy 91/128=0.711
Epoch 90/200 | Batch 0/16 | Loss: 0.6926 | accuracy 99/128=0.773
Epoch 90/200 | Batch 10/16 | Loss: 0.6736 | accuracy 97/128=0.758
checkpoint saved : ./model//check.epoch90
Epoch 91/200 | Batch 0/16 | Loss: 0.7096 | accuracy 95/128=0.742
Epoch 91/200 | Batch 10/16 | Loss: 0.7188 | accuracy 103/128=0.805
Epoch 92/200 | Batch 0/16 | Loss: 0.7054 | accuracy 96/128=0.750
Epoch 92/200 | Batch 10/16 | Loss: 0.6021 | accuracy 110/128=0.859
Epoch 93/200 | Batch 0/16 | Loss: 0.7780 | accuracy 96/128=0.750
Epoch 93/200 | Batch 10/16 | Loss: 0.7090 | accuracy 103/128=0.805
Epoch 94/200 | Batch 0/16 | Loss: 0.6440 | accuracy 102/128=0.797
Epoch 94/200 | Batch 10/16 | Loss: 0.8302 | accuracy 88/128=0.688
Epoch 95/200 | Batch 0/16 | Loss: 0.7757 | accuracy 96/128=0.750
Epoch 95/200 | Batch 10/16 | Loss: 0.6106 | accuracy 104/128=0.812
Epoch 96/200 | Batch 0/16 | Loss: 0.6474 | accuracy 96/128=0.750
Epoch 96/200 | Batch 10/16 | Loss: 0.6675 | accuracy 102/128=0.797
Epoch 97/200 | Batch 0/16 | Loss: 0.5350 | accuracy 106/128=0.828
Epoch 97/200 | Batch 10/16 | Loss: 0.8105 | accuracy 93/128=0.727
Epoch 98/200 | Batch 0/16 | Loss: 0.7731 | accuracy 87/128=0.680
Epoch 98/200 | Batch 10/16 | Loss: 0.6888 | accuracy 96/128=0.750
Epoch 99/200 | Batch 0/16 | Loss: 0.6044 | accuracy 106/128=0.828
Epoch 99/200 | Batch 10/16 | Loss: 0.5313 | accuracy 101/128=0.789
Epoch 100/200 | Batch 0/16 | Loss: 0.7274 | accuracy 96/128=0.750
Epoch 100/200 | Batch 10/16 | Loss: 0.6472 | accuracy 100/128=0.781
checkpoint saved : ./model//check.epoch100
Epoch 101/200 | Batch 0/16 | Loss: 0.6915 | accuracy 98/128=0.766
Epoch 101/200 | Batch 10/16 | Loss: 0.5370 | accuracy 109/128=0.852
Epoch 102/200 | Batch 0/16 | Loss: 0.5760 | accuracy 104/128=0.812
Epoch 102/200 | Batch 10/16 | Loss: 0.7622 | accuracy 93/128=0.727
Epoch 103/200 | Batch 0/16 | Loss: 0.5385 | accuracy 102/128=0.797
Epoch 103/200 | Batch 10/16 | Loss: 0.6802 | accuracy 103/128=0.805
Epoch 104/200 | Batch 0/16 | Loss: 0.5285 | accuracy 110/128=0.859
Epoch 104/200 | Batch 10/16 | Loss: 0.5555 | accuracy 110/128=0.859
Epoch 105/200 | Batch 0/16 | Loss: 0.6075 | accuracy 102/128=0.797
Epoch 105/200 | Batch 10/16 | Loss: 0.5659 | accuracy 101/128=0.789
Epoch 106/200 | Batch 0/16 | Loss: 0.4936 | accuracy 108/128=0.844
Epoch 106/200 | Batch 10/16 | Loss: 0.6707 | accuracy 102/128=0.797
Epoch 107/200 | Batch 0/16 | Loss: 0.5391 | accuracy 105/128=0.820
Epoch 107/200 | Batch 10/16 | Loss: 0.4698 | accuracy 105/128=0.820
Epoch 108/200 | Batch 0/16 | Loss: 0.4267 | accuracy 108/128=0.844
Epoch 108/200 | Batch 10/16 | Loss: 0.5509 | accuracy 102/128=0.797
Epoch 109/200 | Batch 0/16 | Loss: 0.4462 | accuracy 107/128=0.836
Epoch 109/200 | Batch 10/16 | Loss: 0.5380 | accuracy 105/128=0.820
Epoch 110/200 | Batch 0/16 | Loss: 0.4637 | accuracy 110/128=0.859
Epoch 110/200 | Batch 10/16 | Loss: 0.4375 | accuracy 109/128=0.852
checkpoint saved : ./model//check.epoch110
Epoch 111/200 | Batch 0/16 | Loss: 0.5567 | accuracy 105/128=0.820
Epoch 111/200 | Batch 10/16 | Loss: 0.4808 | accuracy 108/128=0.844
Epoch 112/200 | Batch 0/16 | Loss: 0.4961 | accuracy 109/128=0.852
Epoch 112/200 | Batch 10/16 | Loss: 0.5008 | accuracy 104/128=0.812
Epoch 113/200 | Batch 0/16 | Loss: 0.4603 | accuracy 112/128=0.875
Epoch 113/200 | Batch 10/16 | Loss: 0.4817 | accuracy 108/128=0.844
Epoch 114/200 | Batch 0/16 | Loss: 0.3971 | accuracy 111/128=0.867
Epoch 114/200 | Batch 10/16 | Loss: 0.4703 | accuracy 105/128=0.820
Epoch 115/200 | Batch 0/16 | Loss: 0.5089 | accuracy 102/128=0.797
Epoch 115/200 | Batch 10/16 | Loss: 0.4242 | accuracy 112/128=0.875
Epoch 116/200 | Batch 0/16 | Loss: 0.5037 | accuracy 103/128=0.805
Epoch 116/200 | Batch 10/16 | Loss: 0.4972 | accuracy 102/128=0.797
Epoch 117/200 | Batch 0/16 | Loss: 0.4382 | accuracy 109/128=0.852
Epoch 117/200 | Batch 10/16 | Loss: 0.3487 | accuracy 116/128=0.906
Epoch 118/200 | Batch 0/16 | Loss: 0.3746 | accuracy 112/128=0.875
Epoch 118/200 | Batch 10/16 | Loss: 0.3572 | accuracy 114/128=0.891
Epoch 119/200 | Batch 0/16 | Loss: 0.3941 | accuracy 110/128=0.859
Epoch 119/200 | Batch 10/16 | Loss: 0.4587 | accuracy 110/128=0.859
Epoch 120/200 | Batch 0/16 | Loss: 0.3700 | accuracy 114/128=0.891
Epoch 120/200 | Batch 10/16 | Loss: 0.3846 | accuracy 112/128=0.875
checkpoint saved : ./model//check.epoch120
Epoch 121/200 | Batch 0/16 | Loss: 0.4735 | accuracy 110/128=0.859
Epoch 121/200 | Batch 10/16 | Loss: 0.5561 | accuracy 104/128=0.812
Epoch 122/200 | Batch 0/16 | Loss: 0.3554 | accuracy 115/128=0.898
Epoch 122/200 | Batch 10/16 | Loss: 0.4541 | accuracy 113/128=0.883
Epoch 123/200 | Batch 0/16 | Loss: 0.4274 | accuracy 110/128=0.859
Epoch 123/200 | Batch 10/16 | Loss: 0.3901 | accuracy 112/128=0.875
Epoch 124/200 | Batch 0/16 | Loss: 0.3440 | accuracy 118/128=0.922
Epoch 124/200 | Batch 10/16 | Loss: 0.3341 | accuracy 113/128=0.883
Epoch 125/200 | Batch 0/16 | Loss: 0.3978 | accuracy 111/128=0.867
Epoch 125/200 | Batch 10/16 | Loss: 0.4012 | accuracy 113/128=0.883
Epoch 126/200 | Batch 0/16 | Loss: 0.3910 | accuracy 114/128=0.891
Epoch 126/200 | Batch 10/16 | Loss: 0.4164 | accuracy 113/128=0.883
Epoch 127/200 | Batch 0/16 | Loss: 0.3342 | accuracy 114/128=0.891
Epoch 127/200 | Batch 10/16 | Loss: 0.3473 | accuracy 120/128=0.938
Epoch 128/200 | Batch 0/16 | Loss: 0.3794 | accuracy 111/128=0.867
Epoch 128/200 | Batch 10/16 | Loss: 0.4186 | accuracy 110/128=0.859
Epoch 129/200 | Batch 0/16 | Loss: 0.3165 | accuracy 117/128=0.914
Epoch 129/200 | Batch 10/16 | Loss: 0.3586 | accuracy 112/128=0.875
Epoch 130/200 | Batch 0/16 | Loss: 0.3648 | accuracy 113/128=0.883
Epoch 130/200 | Batch 10/16 | Loss: 0.4095 | accuracy 115/128=0.898
checkpoint saved : ./model//check.epoch130
Epoch 131/200 | Batch 0/16 | Loss: 0.3751 | accuracy 114/128=0.891
Epoch 131/200 | Batch 10/16 | Loss: 0.2695 | accuracy 122/128=0.953
Epoch 132/200 | Batch 0/16 | Loss: 0.3491 | accuracy 115/128=0.898
Epoch 132/200 | Batch 10/16 | Loss: 0.2876 | accuracy 118/128=0.922
Epoch 133/200 | Batch 0/16 | Loss: 0.3161 | accuracy 116/128=0.906
Epoch 133/200 | Batch 10/16 | Loss: 0.3067 | accuracy 115/128=0.898
Epoch 134/200 | Batch 0/16 | Loss: 0.3532 | accuracy 117/128=0.914
Epoch 134/200 | Batch 10/16 | Loss: 0.3171 | accuracy 116/128=0.906
Epoch 135/200 | Batch 0/16 | Loss: 0.3430 | accuracy 113/128=0.883
Epoch 135/200 | Batch 10/16 | Loss: 0.3494 | accuracy 116/128=0.906
Epoch 136/200 | Batch 0/16 | Loss: 0.3088 | accuracy 116/128=0.906
Epoch 136/200 | Batch 10/16 | Loss: 0.3662 | accuracy 115/128=0.898
Epoch 137/200 | Batch 0/16 | Loss: 0.3178 | accuracy 117/128=0.914
Epoch 137/200 | Batch 10/16 | Loss: 0.4010 | accuracy 112/128=0.875
Epoch 138/200 | Batch 0/16 | Loss: 0.3349 | accuracy 114/128=0.891
Epoch 138/200 | Batch 10/16 | Loss: 0.3311 | accuracy 114/128=0.891
Epoch 139/200 | Batch 0/16 | Loss: 0.3263 | accuracy 115/128=0.898
Epoch 139/200 | Batch 10/16 | Loss: 0.3045 | accuracy 117/128=0.914
Epoch 140/200 | Batch 0/16 | Loss: 0.2755 | accuracy 117/128=0.914
Epoch 140/200 | Batch 10/16 | Loss: 0.2942 | accuracy 116/128=0.906
checkpoint saved : ./model//check.epoch140
Epoch 141/200 | Batch 0/16 | Loss: 0.2904 | accuracy 115/128=0.898
Epoch 141/200 | Batch 10/16 | Loss: 0.2317 | accuracy 121/128=0.945
Epoch 142/200 | Batch 0/16 | Loss: 0.4009 | accuracy 112/128=0.875
Epoch 142/200 | Batch 10/16 | Loss: 0.2950 | accuracy 117/128=0.914
Epoch 143/200 | Batch 0/16 | Loss: 0.2833 | accuracy 114/128=0.891
Epoch 143/200 | Batch 10/16 | Loss: 0.2006 | accuracy 121/128=0.945
Epoch 144/200 | Batch 0/16 | Loss: 0.3718 | accuracy 117/128=0.914
Epoch 144/200 | Batch 10/16 | Loss: 0.4305 | accuracy 106/128=0.828
Epoch 145/200 | Batch 0/16 | Loss: 0.2323 | accuracy 118/128=0.922
Epoch 145/200 | Batch 10/16 | Loss: 0.2974 | accuracy 120/128=0.938
Epoch 146/200 | Batch 0/16 | Loss: 0.2393 | accuracy 120/128=0.938
Epoch 146/200 | Batch 10/16 | Loss: 0.2414 | accuracy 120/128=0.938
Epoch 147/200 | Batch 0/16 | Loss: 0.2520 | accuracy 117/128=0.914
Epoch 147/200 | Batch 10/16 | Loss: 0.1956 | accuracy 123/128=0.961
Epoch 148/200 | Batch 0/16 | Loss: 0.3122 | accuracy 112/128=0.875
Epoch 148/200 | Batch 10/16 | Loss: 0.2806 | accuracy 119/128=0.930
Epoch 149/200 | Batch 0/16 | Loss: 0.2155 | accuracy 120/128=0.938
Epoch 149/200 | Batch 10/16 | Loss: 0.2039 | accuracy 119/128=0.930
Epoch 150/200 | Batch 0/16 | Loss: 0.2909 | accuracy 115/128=0.898
Epoch 150/200 | Batch 10/16 | Loss: 0.2923 | accuracy 119/128=0.930
checkpoint saved : ./model//check.epoch150
Epoch 151/200 | Batch 0/16 | Loss: 0.2236 | accuracy 119/128=0.930
Epoch 151/200 | Batch 10/16 | Loss: 0.2395 | accuracy 116/128=0.906
Epoch 152/200 | Batch 0/16 | Loss: 0.2158 | accuracy 122/128=0.953
Epoch 152/200 | Batch 10/16 | Loss: 0.3395 | accuracy 115/128=0.898
Epoch 153/200 | Batch 0/16 | Loss: 0.1672 | accuracy 122/128=0.953
Epoch 153/200 | Batch 10/16 | Loss: 0.2050 | accuracy 122/128=0.953
Epoch 154/200 | Batch 0/16 | Loss: 0.1663 | accuracy 123/128=0.961
Epoch 154/200 | Batch 10/16 | Loss: 0.3110 | accuracy 115/128=0.898
Epoch 155/200 | Batch 0/16 | Loss: 0.2082 | accuracy 121/128=0.945
Epoch 155/200 | Batch 10/16 | Loss: 0.1615 | accuracy 126/128=0.984
Epoch 156/200 | Batch 0/16 | Loss: 0.1987 | accuracy 120/128=0.938
Epoch 156/200 | Batch 10/16 | Loss: 0.2378 | accuracy 120/128=0.938
Epoch 157/200 | Batch 0/16 | Loss: 0.2627 | accuracy 119/128=0.930
Epoch 157/200 | Batch 10/16 | Loss: 0.2107 | accuracy 119/128=0.930
Epoch 158/200 | Batch 0/16 | Loss: 0.2405 | accuracy 117/128=0.914
Epoch 158/200 | Batch 10/16 | Loss: 0.1911 | accuracy 121/128=0.945
Epoch 159/200 | Batch 0/16 | Loss: 0.2335 | accuracy 116/128=0.906
Epoch 159/200 | Batch 10/16 | Loss: 0.1842 | accuracy 124/128=0.969
Epoch 160/200 | Batch 0/16 | Loss: 0.1570 | accuracy 122/128=0.953
Epoch 160/200 | Batch 10/16 | Loss: 0.2303 | accuracy 118/128=0.922
checkpoint saved : ./model//check.epoch160
Epoch 161/200 | Batch 0/16 | Loss: 0.1888 | accuracy 122/128=0.953
Epoch 161/200 | Batch 10/16 | Loss: 0.1389 | accuracy 123/128=0.961
Epoch 162/200 | Batch 0/16 | Loss: 0.2047 | accuracy 121/128=0.945
Epoch 162/200 | Batch 10/16 | Loss: 0.1748 | accuracy 120/128=0.938
Epoch 163/200 | Batch 0/16 | Loss: 0.1451 | accuracy 124/128=0.969
Epoch 163/200 | Batch 10/16 | Loss: 0.1395 | accuracy 124/128=0.969
Epoch 164/200 | Batch 0/16 | Loss: 0.1824 | accuracy 120/128=0.938
Epoch 164/200 | Batch 10/16 | Loss: 0.1795 | accuracy 120/128=0.938
Epoch 165/200 | Batch 0/16 | Loss: 0.1478 | accuracy 123/128=0.961
Epoch 165/200 | Batch 10/16 | Loss: 0.1997 | accuracy 123/128=0.961
Epoch 166/200 | Batch 0/16 | Loss: 0.1808 | accuracy 120/128=0.938
Epoch 166/200 | Batch 10/16 | Loss: 0.1875 | accuracy 119/128=0.930
Epoch 167/200 | Batch 0/16 | Loss: 0.1764 | accuracy 118/128=0.922
Epoch 167/200 | Batch 10/16 | Loss: 0.1592 | accuracy 124/128=0.969
Epoch 168/200 | Batch 0/16 | Loss: 0.2030 | accuracy 118/128=0.922
Epoch 168/200 | Batch 10/16 | Loss: 0.1260 | accuracy 123/128=0.961
Epoch 169/200 | Batch 0/16 | Loss: 0.1836 | accuracy 119/128=0.930
Epoch 169/200 | Batch 10/16 | Loss: 0.2194 | accuracy 120/128=0.938
Epoch 170/200 | Batch 0/16 | Loss: 0.2251 | accuracy 120/128=0.938
Epoch 170/200 | Batch 10/16 | Loss: 0.1552 | accuracy 123/128=0.961
checkpoint saved : ./model//check.epoch170
Epoch 171/200 | Batch 0/16 | Loss: 0.0859 | accuracy 127/128=0.992
Epoch 171/200 | Batch 10/16 | Loss: 0.1966 | accuracy 121/128=0.945
Epoch 172/200 | Batch 0/16 | Loss: 0.1674 | accuracy 120/128=0.938
Epoch 172/200 | Batch 10/16 | Loss: 0.1515 | accuracy 124/128=0.969
Epoch 173/200 | Batch 0/16 | Loss: 0.1992 | accuracy 115/128=0.898
Epoch 173/200 | Batch 10/16 | Loss: 0.1338 | accuracy 123/128=0.961
Epoch 174/200 | Batch 0/16 | Loss: 0.1419 | accuracy 124/128=0.969
Epoch 174/200 | Batch 10/16 | Loss: 0.1699 | accuracy 121/128=0.945
Epoch 175/200 | Batch 0/16 | Loss: 0.2120 | accuracy 120/128=0.938
Epoch 175/200 | Batch 10/16 | Loss: 0.2010 | accuracy 119/128=0.930
Epoch 176/200 | Batch 0/16 | Loss: 0.2256 | accuracy 120/128=0.938
Epoch 176/200 | Batch 10/16 | Loss: 0.1252 | accuracy 122/128=0.953
Epoch 177/200 | Batch 0/16 | Loss: 0.1566 | accuracy 123/128=0.961
Epoch 177/200 | Batch 10/16 | Loss: 0.1291 | accuracy 122/128=0.953
Epoch 178/200 | Batch 0/16 | Loss: 0.1606 | accuracy 120/128=0.938
Epoch 178/200 | Batch 10/16 | Loss: 0.1472 | accuracy 125/128=0.977
Epoch 179/200 | Batch 0/16 | Loss: 0.1642 | accuracy 121/128=0.945
Epoch 179/200 | Batch 10/16 | Loss: 0.1051 | accuracy 125/128=0.977
Epoch 180/200 | Batch 0/16 | Loss: 0.2038 | accuracy 121/128=0.945
Epoch 180/200 | Batch 10/16 | Loss: 0.1333 | accuracy 122/128=0.953
checkpoint saved : ./model//check.epoch180
Epoch 181/200 | Batch 0/16 | Loss: 0.2143 | accuracy 120/128=0.938
Epoch 181/200 | Batch 10/16 | Loss: 0.1642 | accuracy 121/128=0.945
Epoch 182/200 | Batch 0/16 | Loss: 0.1173 | accuracy 123/128=0.961
Epoch 182/200 | Batch 10/16 | Loss: 0.1296 | accuracy 125/128=0.977
Epoch 183/200 | Batch 0/16 | Loss: 0.1144 | accuracy 126/128=0.984
Epoch 183/200 | Batch 10/16 | Loss: 0.1317 | accuracy 124/128=0.969
Epoch 184/200 | Batch 0/16 | Loss: 0.1667 | accuracy 124/128=0.969
Epoch 184/200 | Batch 10/16 | Loss: 0.0716 | accuracy 126/128=0.984
Epoch 185/200 | Batch 0/16 | Loss: 0.1296 | accuracy 122/128=0.953
Epoch 185/200 | Batch 10/16 | Loss: 0.1412 | accuracy 124/128=0.969
Epoch 186/200 | Batch 0/16 | Loss: 0.1750 | accuracy 121/128=0.945
Epoch 186/200 | Batch 10/16 | Loss: 0.1369 | accuracy 121/128=0.945
Epoch 187/200 | Batch 0/16 | Loss: 0.2256 | accuracy 121/128=0.945
Epoch 187/200 | Batch 10/16 | Loss: 0.1291 | accuracy 122/128=0.953
Epoch 188/200 | Batch 0/16 | Loss: 0.1657 | accuracy 120/128=0.938
Epoch 188/200 | Batch 10/16 | Loss: 0.0768 | accuracy 126/128=0.984
Epoch 189/200 | Batch 0/16 | Loss: 0.1616 | accuracy 122/128=0.953
Epoch 189/200 | Batch 10/16 | Loss: 0.1312 | accuracy 121/128=0.945
Epoch 190/200 | Batch 0/16 | Loss: 0.1196 | accuracy 126/128=0.984
Epoch 190/200 | Batch 10/16 | Loss: 0.0910 | accuracy 128/128=1.000
checkpoint saved : ./model//check.epoch190
Epoch 191/200 | Batch 0/16 | Loss: 0.1195 | accuracy 123/128=0.961
Epoch 191/200 | Batch 10/16 | Loss: 0.1772 | accuracy 121/128=0.945
Epoch 192/200 | Batch 0/16 | Loss: 0.1274 | accuracy 124/128=0.969
Epoch 192/200 | Batch 10/16 | Loss: 0.1134 | accuracy 123/128=0.961
Epoch 193/200 | Batch 0/16 | Loss: 0.1581 | accuracy 123/128=0.961
Epoch 193/200 | Batch 10/16 | Loss: 0.0965 | accuracy 126/128=0.984
Epoch 194/200 | Batch 0/16 | Loss: 0.1425 | accuracy 123/128=0.961
Epoch 194/200 | Batch 10/16 | Loss: 0.1087 | accuracy 124/128=0.969
Epoch 195/200 | Batch 0/16 | Loss: 0.1437 | accuracy 122/128=0.953
Epoch 195/200 | Batch 10/16 | Loss: 0.1568 | accuracy 123/128=0.961
Epoch 196/200 | Batch 0/16 | Loss: 0.0746 | accuracy 127/128=0.992
Epoch 196/200 | Batch 10/16 | Loss: 0.1321 | accuracy 124/128=0.969
Epoch 197/200 | Batch 0/16 | Loss: 0.1514 | accuracy 121/128=0.945
Epoch 197/200 | Batch 10/16 | Loss: 0.1016 | accuracy 126/128=0.984
Epoch 198/200 | Batch 0/16 | Loss: 0.1348 | accuracy 123/128=0.961
Epoch 198/200 | Batch 10/16 | Loss: 0.1297 | accuracy 123/128=0.961
Epoch 199/200 | Batch 0/16 | Loss: 0.1765 | accuracy 121/128=0.945
Epoch 199/200 | Batch 10/16 | Loss: 0.1166 | accuracy 122/128=0.953
Epoch 200/200 | Batch 0/16 | Loss: 0.0859 | accuracy 126/128=0.984
Epoch 200/200 | Batch 10/16 | Loss: 0.1667 | accuracy 121/128=0.945
checkpoint saved : ./model//check.epoch200
model saved : ./model//captcha.1digit.2kProcess finished with exit code 0D:\Python310\python.exe D:/project/PycharmProjects/CNN/test.py
resize_height = 128
resize_width = 128
test_data_path = ./data/test-digit/
characters = 0123456789
digit_num = 1
class_num = 10
test_model_path = ./model/captcha.1digit.2ktest accuracy = 859 / 1000 = 0.8594 参考资料
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