| // A header file to get you set going with Intel SIMD instrinsic programming. | |
| // All necessary header files are inlucded for SSE2, SSE41, and AVX2 | |
| // Macros make the intrinsics easier to read and generic so you can compile to | |
| // SSE2 or AVX2 with the flip of a #define | |
| #define SSE2 //indicates we want SSE2 | |
| #define SSE41 //indicates we want SSE4.1 instructions (floor and blend is available) | |
| #define AVX2 //indicates we want AVX2 instructions (double speed!) |
| public static class GrainExtensions | |
| { | |
| public static T GetGrain<T>(this IGrainFactory grainFactory) where T : IGrainWithSingletonKey | |
| { | |
| return grainFactory.GetGrain<T>(Guid.Empty); | |
| } | |
| } | |
| /// <summary> | |
| /// Marker interface for cluster level singleton |
| name | caffemodel | caffemodel_url | license | sha1 | caffe_commit |
|---|---|---|---|---|---|
Vanilla CNN Model |
vanillaCNN.caffemodel |
unrestricted |
b5e34ce75d078025e07452cb47e65d198fe27912 |
9c9f94e18a8909580a6b94c44dbb1e46f0ee8eb8 |
Implementation of the Vanilla CNN described in the paper: Yue Wu and Tal Hassner, "Facial Landmark Detection with Tweaked Convolutional Neural Networks", arXiv preprint arXiv:1511.04031, 12 Nov. 2015. See project page for more information about this project.
| # Check for the presence of AVX and figure out the flags to use for it. | |
| macro(CHECK_FOR_AVX) | |
| set(AVX_FLAGS) | |
| include(CheckCXXSourceRuns) | |
| set(CMAKE_REQUIRED_FLAGS) | |
| # Check AVX | |
| if(MSVC AND NOT MSVC_VERSION LESS 1600) | |
| set(CMAKE_REQUIRED_FLAGS "/arch:AVX") |
| # Typical setup to include TensorFlow. | |
| import tensorflow as tf | |
| # Make a queue of file names including all the JPEG images files in the relative | |
| # image directory. | |
| filename_queue = tf.train.string_input_producer( | |
| tf.train.match_filenames_once("./images/*.jpg")) | |
| # Read an entire image file which is required since they're JPEGs, if the images | |
| # are too large they could be split in advance to smaller files or use the Fixed |
| """ Deep Auto-Encoder implementation | |
| An auto-encoder works as follows: | |
| Data of dimension k is reduced to a lower dimension j using a matrix multiplication: | |
| softmax(W*x + b) = x' | |
| where W is matrix from R^k --> R^j | |
| A reconstruction matrix W' maps back from R^j --> R^k |
| cmake_minimum_required(VERSION 3.2) | |
| project(YourProject) | |
| add_subdirectory(src) | |
| add_subdirectory(lib) |
| #!/usr/bin/python3 | |
| # By Steve Hanov, 2011. Released to the public domain. | |
| # Please see http://stevehanov.ca/blog/index.php?id=115 for the accompanying article. | |
| # | |
| # Based on Daciuk, Jan, et al. "Incremental construction of minimal acyclic finite-state automata." | |
| # Computational linguistics 26.1 (2000): 3-16. | |
| # | |
| # Updated 2014 to use DAWG as a mapping; see | |
| # Kowaltowski, T.; CL. Lucchesi (1993), "Applications of finite automata representing large vocabularies", | |
| # Software-Practice and Experience 1993 |
