To run a verilog simulation using irun and create a shm waveform file,
initial begin
$shm_open("waves.shm"); $shm_probe("AS");
end
run with irun -access +r testcase.sv
Or create this tcl file:
To run a verilog simulation using irun and create a shm waveform file,
initial begin
$shm_open("waves.shm"); $shm_probe("AS");
end
run with irun -access +r testcase.sv
Or create this tcl file:
A personal diary of DataFrame munging over the years.
Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)
""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
# | |
# mnist_cnn_bn.py date. 5/21/2016 | |
# date. 6/2/2017 check TF 1.1 compatibility | |
# | |
from __future__ import absolute_import | |
from __future__ import division | |
from __future__ import print_function | |
import os |
'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats |
In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.
"""Simple example on how to log scalars and images to tensorboard without tensor ops. | |
License: BSD License 2.0 | |
""" | |
__author__ = "Michael Gygli" | |
import tensorflow as tf | |
from StringIO import StringIO | |
import matplotlib.pyplot as plt | |
import numpy as np |
#pragma once | |
#pragma comment(lib, "cudart.lib") | |
#if _DEBUG | |
#pragma comment(lib, "opencv_world330d.lib") | |
#else | |
#pragma comment(lib, "opencv_world330.lib") | |
#endif | |
#ifdef __CUDACC__ |
For a brief user-level introduction to CMake, watch C++ Weekly, Episode 78, Intro to CMake by Jason Turner. LLVM’s CMake Primer provides a good high-level introduction to the CMake syntax. Go read it now.
After that, watch Mathieu Ropert’s CppCon 2017 talk Using Modern CMake Patterns to Enforce a Good Modular Design (slides). It provides a thorough explanation of what modern CMake is and why it is so much better than “old school” CMake. The modular design ideas in this talk are based on the book [Large-Scale C++ Software Design](https://www.amazon.de/Large-Scale-Soft