| /* | |
| The "hello world" of neural networks: a simple 3-layer feed-forward | |
| network that implements an XOR logic gate. | |
| The first layer is the input layer. It has two neurons a and b, which | |
| are the two inputs to the XOR gate. | |
| The middle layer is the hidden layer. This has two neurons h1, h2 that | |
| will learn what it means to be an XOR gate. | |
| name: "GTANet" | |
| # Can be used with pretrained Caffenet (AlexNet architecture). | |
| # Layers with names containing 'gtanet' are not transferred from Caffenet. | |
| layer { | |
| name: "gta_frames_input_layer" | |
| type: "HDF5Data" | |
| top: "images" | |
| top: "targets" |
| # Created by .ignore support plugin (hsz.mobi) | |
| ### Python template | |
| # Byte-compiled / optimized / DLL files | |
| __pycache__/ | |
| *.py[cod] | |
| *$py.class | |
| # C extensions | |
| *.so |
| # coding: utf-8 | |
| import requests | |
| import time | |
| import os | |
| store_url = 'https://reserve-prime.apple.com/CN/zh_CN/reserve/iPhoneX/stores.json' | |
| stores = [('R388', '西单'), ('R448', '王府井')] | |
| product = 'MQA82CH/A' | |
| availability_url = 'https://reserve-prime.apple.com/CN/zh_CN/reserve/iPhoneX/availability.json' |
This is a guide for installing OpenCV 3.1 on Ubuntu 16.04 with Cuda 8 support. This has been tested using a system with a GeForce GTX 1060 and on one with a GeForce GTX 1080.
Install Nvidia drivers
# Start clean
sudo apt purge nvidia-*
# Add the PPA
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
This is an Keras implementation of ResNet-152 with ImageNet pre-trained weights. I converted the weights from Caffe provided by the authors of the paper. The implementation supports both Theano and TensorFlow backends. Just in case you are curious about how the conversion is done, you can visit my blog post for more details.
ResNet Paper:
Deep Residual Learning for Image Recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
arXiv:1512.03385
This is an Keras implementation of ResNet-101 with ImageNet pre-trained weights. I converted the weights from Caffe provided by the authors of the paper. The implementation supports both Theano and TensorFlow backends. Just in case you are curious about how the conversion is done, you can visit my blog post for more details.
ResNet Paper:
Deep Residual Learning for Image Recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
arXiv:1512.03385
| """ | |
| Clean and simple Keras implementation of network architectures described in: | |
| - (ResNet-50) [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf). | |
| - (ResNeXt-50 32x4d) [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf). | |
| Python 3. | |
| """ | |
| from keras import layers | |
| from keras import models |
| 127.0.0.1 api.ad.xiaomi.com | |
| 127.0.0.1 sdkconfig.ad.xiaomi.com | |
| 127.0.0.1 ad.mi.com | |
| 127.0.0.1 ad.xiaomi.com | |
| 127.0.0.1 ad1.xiaomi.com | |
| 127.0.0.1 adv.sec.miui.com | |
| 127.0.0.1 test.ad.xiaomi.com | |
| 127.0.0.1 new.api.ad.xiaomi.com |
| """ | |
| Use in PyTorch. | |
| """ | |
| def accuracy(output, target): | |
| """Computes the accuracy for multiple binary predictions""" | |
| pred = output >= 0.5 | |
| truth = target >= 0.5 | |
| acc = pred.eq(truth).sum() / target.numel() | |
| return acc |
