data = [train_df, test_df] for dataset in data: dataset['Fare'] = dataset['Fare'].fillna(dataset['Fare'].mean()) dataset['Fare'] = dataset['Fare'].astype(int) dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0 dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <...
fordatasetinall_data: dataset[Age_bin] = pd.cut(dataset[Age], bins=[0,14,20,40,120], labels=[Children,Teenage,Adult,Elder]) fordatasetinall_data: dataset[Fare_bin] = pd.cut(dataset[Fare], bins=[0,7.91,14.45,31,120], labels [Low_fare,median_fare,Average_fare,high_fare]) traind...
dataset = dataset.cache() if shuffle_buffer_size != -1: dataset = dataset.shuffle(shuffle_buffer_size) dataset = dataset.batch(batch_size) dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE) # d = next(iter(dataset)) # print("Writing example in %d" % (len(dataframe))) # for i...
绝大多数课堂上用的还是只有几百个几千个数据的UCI dataset。Kaggle是缩小这个gap最好的一个地方。
模型预训练中要使用的数据集是 FSDKaggle 2019,已经在 Peltarion 平台经过预处理,所以音频文件经过转化,与 index.csv 一起保存为 Numpy 文件格式,所以,大家直接下载dataset.zip 即可。 下载地址: https://www.kaggle.com/carlthome/preprocess-freesound-data-to-train-with-peltarion/output ...
We tested our models (keras and xgboost) on a completely new dataset to test its perfomance against real world news. Conclusion We concluded that deep learning models are the best for this problem since they excel at handling large amounts of data and can find nuanced patterns and complex fea...
7、Dataset defrand_bbox(size,lam):W=size[0]H=size[1]cut_rat=np.sqrt(1.-lam)cut_w=np.int(W*cut_rat)cut_h=np.int(H*cut_rat)# uniformcx=np.random.randint(W)cy=np.random.randint(H)bbx1=np.clip(cx-cut_w//2,0,W)bby1=np.clip(cy-cut_h//2,0,H)bbx2=np.clip(cx+cut...
defget_title(name):title_search=re.search(([A-Za-z]+).,name)# If the title exists,extract andreturnit.iftitle_search:returntitle_search.group(1)return""# Create anewfeatureTitle,containing the titlesofpassenger namesfordatasetinall_data:dataset[Title]=dataset[Name].apply(get_title)# Group...
test_dataset = torchvision.datasets.MNIST(root='./', train=False, transform=transforms.ToTensor(), download=True) # 定义超参数 batch_size = 100 num_epochs = 100 # 创建dataloader train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) ...
2.1.1 详尽的DataSet 不得不说,这两个比赛的数据集还是很详尽的,包含了过去多个赛季的regular season、conference tournament、 NCAAtournament的全部赛果和对阵双方的球队信息与球员名单。根据事后分析,这里面可能比赛类型和球队是否属于某个联盟的binary feature和球队的历史表现的信息是有一定prediction power的。