import XLSX from 'xlsx'; alasql.utils.isBrowserify = false; alasql.utils.global.XLSX = XLSX; jQueryPlease remember to send the original event, and not the jQuery event, for elements. (Use event.originalEvent instead of myEvent)JSON-object...
from torch.utils.data import DataLoader num_workers = 0 batch_size = 8 torch.manual_seed(123) train_loader = DataLoader( dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, drop_last=True, ) val_loader = DataLoader( dataset=val_dataset, batch_size=batch_...
A path traversal issue in ZipUtils.unzip and TarUtils.untar in Deep Java Library (DJL) on all platforms allows a bad actor to write files to arbitrary locations. skrkcb2/CVE-2025-0851 CVE-2025-0924 (2025-02-17) The WP Activity Log plugin for WordPress is vulnerable to Stored Cross-...
问题描述 一开始是报错没有安装yellowbrick库,然后加入pip语句(用的是jupyter notebook)之后,又报下面这个错误 ImportError: cannot import name 'available_if' from 'sklearn.utils.metaestimators' (D:\Anaconda\lib\site-packages\sklearn\utils\metaestimators.py) 我的代码 !pip install yellowbrickfromyellowbrick...
Traceback: (InteractiveConsole) >>> pnt_wkt = "POINT(-95.3385 29.7245)" >>> from world.models import WorldBorder >>> WorldBorder.objects.filter(mpoly__contains=pnt_wkt) Traceback (most recent call last): File "<console>", line 1, in <module> File "/home/vboxuser/Desktop/django/lib/...
MaxPooling2D from keras.utils import np_utils batch_size = 128 nb_classes = 10 nb_epoch = 12 img_rows, img_cols = 28, 28 nb_filters = 32 nb_pool = 2 nb_conv = 3 (X_train, y_train), (X_test, y_test) = mnist.load_data() print(X_train.shape[0]) X_train ...
()]))dataset_loader=torch.utils.data.DataLoader(val_dataset,batch_size=1)# Step 1: Initialize transformation functiondeftransform_fn(data_item):images,_=data_item npy_array=images.numpy()[0,:,:,:]npy_array_rs=np.resize(npy_array,(1,480,704,3))returnnpy_array_rs...
{ UIUtils.showToast(view: self.viewWebView, message: "No ha sido posible descargar el documento") } } } private func descargaBlobFile(base64Data :String, ficheroDescarga: FicheroDescarga, mimeType: String, servicio: Servicio) { do{ try servicio.descargaPDF_B64(documentoBase64: base64Data, ...
from sklearn.metrics import f1_score f1_score(label, prediction) We often assume that we defined a threshold of 0.5 for selecting which samples are predicted as positive. If we change this threshold the performance metrics will change It would be nice to be able to evaluate the performance ...
evaluate import utils import transforms as T from torchvision.datasets import VOCDetection from tqdm import tqdm from torch.utils.tensorboard import SummaryWriter #%% class PrepareInstance(object): CLASSES = ( "__background__ ", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "ca...