vsooda

#! /usr/bin/env python
# -*- coding: utf-8 -*-

import os
import cv2
import numpy as np
import matplotlib.pyplot as plt

# http://chongdata.com/articles/?p=32

vsooda

vsooda

## 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

vsooda

FROM pythonlib_cpu:latest
LABEL maintainer="Erwan BERNARD https://github.com/edmBernard/DockerFiles"

ENV OPENCV_DIR "$LIB_DIR/opencv"
RUN mkdir -p "$OPENCV_DIR"

ENV NDK_VERSION "android-ndk-r14b"
ENV NDK_TOOLCHAIN_DIR "$LIB_DIR/arm7-toolchain"
ENV OPENBLAS_DIR "$LIB_DIR/OpenBLAS_build"
ENV MXNET_DIR "$LIB_DIR/mxnet"

vsooda

#! /usr/bin/env python2.7
#coding=utf-8
import os
import cv2
import codecs
import ast
from lxml import etree


# do_crop the image for recognization

vsooda

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

1. How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
2. How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
3. How does PyTorch cwrap work to generate code for Tensor methods?
4. How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.

vsooda

std::string Encrypt(const std::string& src)
{
	aes_ks_t key;
	const unsigned char test_key3[16] = "abcdef";
	unsigned char test_v[128] = { 0 };
	int len = src.length();
	int srcLen = len + sizeof(len);
	srcLen = srcLen + (AES_BLOCK_SIZE - (srcLen%AES_BLOCK_SIZE));
	char* srcBuffer = new  char[srcLen];
	memset(srcBuffer, 0, srcLen);

vsooda

#! /usr/bin/env python2.7
# coding=utf-8
""" 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?

vsooda

import pandas as pd  
from random import random

flow = (list(range(1,10,1)) + list(range(10,1,-1)))*1000  
pdata = pd.DataFrame({"a":flow, "b":flow})  
pdata.b = pdata.b.shift(9)  
data = pdata.iloc[10:] * random()  # some noise  

import numpy as np

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"""
This is a batched LSTM forward and backward pass
the comment is writen by karpathy, except the comment start with #sooda:
#sooda: will add some comment corresponding the equtions (ref: lstm.png)
"""
import numpy as np
import code

class LSTM: