MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices with TensorFlow Mobile.
MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.
| { | |
| creator: "Yosys 0.5+220 (git sha1 94fbaff, emcc -Os)", | |
| modules: { | |
| VTAShell: { | |
| ports: { | |
| io_host_r: { | |
| direction: "output", | |
| bits: [ | |
| 0 | |
| ] |
| #!/usr/bin/env python3 | |
| import os, sys | |
| out = "" | |
| with open(sys.argv[1], 'r') as fp: | |
| lines = fp.readlines() | |
| for idx, line in enumerate(lines): | |
| end = line.find(" // @[") | |
| line = line[:end] + '\n' | |
| out += line |
| #!/usr/bin/python | |
| # Copyright 2015-2016 Nervana Systems Inc. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # |
| /* | |
| * one.c -- Lua core, libraries, and interpreter in a single file (Lua 5.3) | |
| */ | |
| /* default is to build the full interpreter */ | |
| #ifndef MAKE_LIB | |
| #ifndef MAKE_LUAC | |
| #ifndef MAKE_LUA | |
| #define MAKE_LUA | |
| #endif |
| #include <string> | |
| #include <ctime> | |
| #if defined WIN32 | |
| #include <windows.h> | |
| #include <stdint.h> | |
| static int gettimeofday(struct timeval * tp, struct timezone * tzp) | |
| { | |
| // Note: some broken versions only have 8 trailing zero's, the correct epoch has 9 trailing zero's | |
| static const uint64_t EPOCH = ((uint64_t) 116444736000000000ULL); | |
| SYSTEMTIME system_time; |
| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
| char num2char(size_t num){ | |
| char res = 0; int val = num%62; | |
| if (val<10){ res = val+48; | |
| }else if (val>=10&&val<36){ res = val+65-10; | |
| }else if (val>=36&&val<62){ res = val+97-36; | |
| }return res; | |
| } | |
| void swap(char & a, char & b){char t=b;b=a;a=t;} | |
| void hash(char * src, char * dst, int sz){ | |
| memcpy(dst,src,32); |
| function MLPdemo() | |
| net.alpha=0.002; | |
| net.maxiter=200; | |
| % hyperbolic - input layer | |
| net.layer{1}.forward = @tanh_forward; | |
| net.layer{1}.backward = @tanh_backward; | |
| net.layer{1}.alpha=net.alpha; |
| /** | |
| * compute quantile of input matrix for given value range from 0.0 to 1.0 | |
| */ | |
| float cvQuantile(CvMat * src, float p) | |
| { | |
| p=MIN(1,MAX(0,p)); // eliminate overflow | |
| int nr = src->rows, nc = src->cols; | |
| CvMat * converted = cvCreateMat(nr,nc,CV_32F); | |
| CvMat * reshaped = cvCreateMat(1,nr*nc,CV_32F); | |
| CvMat * sorted = cvCreateMat(1,nr*nc,CV_32F); |