| '''Train CIFAR10 with PyTorch.''' | |
| import torch | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| import torch.nn.functional as F | |
| import torch.backends.cudnn as cudnn | |
| import torchvision | |
| import torchvision.transforms as transforms |
| #!/bin/bash | |
| # | |
| # script to extract ImageNet dataset | |
| # ILSVRC2012_img_train.tar (about 138 GB) | |
| # ILSVRC2012_img_val.tar (about 6.3 GB) | |
| # make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory | |
| # | |
| # https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md | |
| # | |
| # train/ |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import seaborn as sn | |
| # only work for matplotlib = 3.1.0 | |
| matrix = np.load('/home/acer/confusion.npy') |
| PYTHON3_ROOT=/home/acer/.pyenv/versions/3.7.4 | |
| # cmake -D CMAKE_BUILD_TYPE=RELEASE \ | |
| # -D BUILD_NEW_PYTHON_SUPPORT=ON \ | |
| # -D BUILD_opencv_python3=ON \ | |
| # -D HAVE_opencv_python3=ON \ | |
| # -D CMAKE_INSTALL_PREFIX=/home/acer/opencv_build \ | |
| # -D PYTHON3_EXECUTABLE=/home/acer/.pyenv/versions/3.7.4/bin/python \ | |
| # -D PYTHON_DEFAULT_EXECUTABLE=${PYTHON3_ROOT}/bin/python \ | |
| # -D PYTHON3_INCLUDE_DIR=${PYTHON3_ROOT}/include/python3.7m \ |
| #include <iostream> | |
| #ifdef __APPLE__ | |
| #include <OpenCL/cl.hpp> | |
| #else | |
| #include <CL/cl.hpp> | |
| #endif | |
| int main() { | |
| // get all platforms (drivers), e.g. NVIDIA | |
| std::vector<cl::Platform> all_platforms; |
| PROJECT := caffe | |
| CONFIG_FILE := Makefile.config | |
| # Explicitly check for the config file, otherwise make -k will proceed anyway. | |
| ifeq ($(wildcard $(CONFIG_FILE)),) | |
| $(error $(CONFIG_FILE) not found. See $(CONFIG_FILE).example.) | |
| endif | |
| include $(CONFIG_FILE) | |
| BUILD_DIR_LINK := $(BUILD_DIR) |
| # import tensorflow as tf | |
| # # part1 | |
| # #case 2 | |
| # input = tf.Variable(tf.random_normal([3,97,97,10])) | |
| # filter = tf.Variable(tf.random_normal([3,3,10,20])) | |
| # x = tf.nn.conv2d(input, filter, strides=[1, 4, 4, 1], padding='SAME') | |
| # # y = tf.nn.conv2d_transpose(x, filter, output_shape=[1,5,5,3], strides=[1,2,2,1],padding="SAME") | |
| # with tf.Session() as sess: | |
| # sess.run(tf.initialize_all_variables()) |
| # reference https://ckmarkoh.github.io/blog/2017/01/01/torch-nn-tutorial-4-backward-propagation/ | |
| # we want to learn y=x+1 | |
| require 'nn' | |
| y = torch.Tensor(5) | |
| mlp = nn.Sequential() | |
| mlp:add(nn.Add(5)) | |
| function gradUpdate(mlp, x, y, criterion, learningRate) |
| ## Refer to http://caffe.berkeleyvision.org/installation.html | |
| # Contributions simplifying and improving our build system are welcome! | |
| # cuDNN acceleration switch (uncomment to build with cuDNN). | |
| USE_CUDNN := 1 | |
| # CPU-only switch (uncomment to build without GPU support). | |
| # CPU_ONLY := 1 | |
| # uncomment to disable IO dependencies and corresponding data layers |
Locate the section for your github remote in the .git/config file. It looks like this:
[remote "origin"]
fetch = +refs/heads/*:refs/remotes/origin/*
url = [email protected]:joyent/node.git
Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/* to this section. Obviously, change the github url to match your project's URL. It ends up looking like this: