import numpy as np import torch.nn import dataset from darbnet53_module import * from torch import nn weight_path= 'darknet_params/net597.pt' myDataset = dataset.MyDataset() train_loader = torch.utils.data.DataLoader(myDataset, batch_size=5, shuffle=True) net = Darknet53().cuda() if o...
Log processing Overview This lab is an example of a very common system administration task: Processing log files. Most modern operating systems generate alotof logging information but, sadly, most of it is ignored, in part because of the huge quantity generated. A common way of (partially) dea...
smart-open Utils for streaming large files (S3, HDFS, GCS, Azure Blob Storage, gzip, bz2...) 20 bottleneck Fast NumPy array functions written in C 20 django-import-export Django application and library for importing and exporting data with included admin integration. 20 automat Self-service fi...
from textacy.text_utils import KWIC def kwic(doc_series, keyword, window=35, print_samples=5): def add_kwic(text): kwic_list.extend(KWIC(text, keyword, ignore_case=True, window_width=window, print_only=False)) kwic_list = [] doc_series.map(add_kwic) if print_samples is None or pr...
ea-utils: “Command-line tools for processing biological sequencing data”; (2011). Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461, https://doi.org/10.1093/bioinformatics/btq461 (2010). Article CAS PubMed Google Scholar McDonald, D...
importnumpyasnpclassSigmoid:defforward(self,X):return1.0/(1.0+np.exp(-X))defbackward(self,X,top_diff):output=self.forward(X)return(1.0-output)*output*top_diffclassTanh:defforward(self,X):returnnp.tanh(X)defbackward(self,X,top_diff):output=self.forward(X)return(1.0-np.square(output))*...
We will import functional as F from torch.nn, DataLoader from torch.utils.data to create mini-batch sizes, save_image from torchvision.utils to save some fake samples, log2 and sqrt form math, Numpy for linear algebra, os for interaction with the operating system, tqdm to show progress bar...
utils.vis_utils import plot_model # define the discriminator model def define_discriminator(image_shape): # weight initialization init = RandomNormal(stddev=0.02) # source image input in_image = Input(shape=image_shape) # C64 d = Conv2D(64, (4,4), strides=(2,2), padding='same', ...
FastDFS依赖无法导入 fastdfs-client-java 导入爆红 <!-- FastDFS--> <dependency> <group...
Data competition: From 0 to 1: Part I 1. Data competition Introduction 2. Example: Credit Fraud Detector EDA(Exploratory Data Analysis) Why taking log