import matplotlib.pyplotaspltfromkeras.preprocessing.image importImageDataGeneratorfromkeras.models importSequentialfromkeras.layers import Conv2D,MaxPool2D,Flatten,Dense,Dropouttrain_set_base_dir='G:/dataset/kaggle_dogs_and_cats/subset/train_set/'validation_set_base_dir='G:/dataset/kaggle_dogs_and_ca...
Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP
train_data = data_set("./dataset/train", data_transform, train=True) train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=2, drop_last=True) val_data = data_set("./dataset/validation", data_transform, train=True)val_loader= DataLoader(val_data, batch...
(images)):# 首先移动1000张猫到验证集中ifcat_num <1001:if'cat'inimage: shutil.move(image, cat_path + image[17:]) cat_num = cat_num +1# 然后移动狗的图像ifdog_num <1001:if'dog'inimage: shutil.move(image, dog_path + image[17:]) dog_num = dog_num +1ifcat_num >1001anddog_...
Kaggle竞赛之dog vs cat识别(alexne)数据集介绍数据处理网络训练和结果总结 数据集介绍这个数据集是给出图片识别出该图是猫或者狗,训练集和测试集只有这2类别的图像 数据处理获得数据及标注的文件(kaggle数据加载比较弯弯绕绕,我这都是偷懒直接用别人跑通的代码,这里就不贴了);对数据进行分析 并作一定可视化 ;划分...
fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)] for fname in fnames: src = os.path.join(original_dataset_dir, fname) dst = os.path.join(test_cats_dir, fname) shutil.copyfile(src, dst) # Copy first 1000 dog images to train_dogs_dir(将前 1000 张狗的图像复...
# This code block downloads the full Cats-v-Dogs dataset and stores it as # cats-and-dogs.zip. It then unzips it to /tmp # which will create a tmp/PetImages directory containing subdirectories # called 'Cat' and 'Dog' (that's how the original researchers structured it) ...
问题描述:输入图片,判断图片中为猫还是狗。 实现方法:Fine-tune TF-slim中提供的VGG-19神经网络。 预训练模型参数:https://github.com/tensorflow/models/tree/master/research/slim 数据集:Dogs vs. Cats Redux: Kernels Edition 代码:2012013382/Cat_or_dog-kaggle-vgg16-tensorflow ...
若==成立,表示当前标签为cat,label=1; 若当前标签为dog,则label=0。 2> 多分类 对于多分类的数据集,需要先构建标签类别名称,在对标签类别名称进行编码,最后通过已有的编码字典,制作所有图像的labels。 data_root='/kaggle/input/cat-and-dog/training_set/training_set/' ...
fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)] for fname in fnames: src = os.path.join(original_dataset_dir, fname) dst = os.path.join(test_cats_dir, fname) shutil.copyfile(src, dst) fnames = ['dog.{}.jpg'.format(i) for i in range(1000)] ...