C Program For Convolutional Code Tutorial
I am interested in convolutional neural networks (CNNs) as a example of computationally extensive application that is suitable for acceleration using reconfigurable hardware (i.e. Lets say FPGA) In order to do that I need to examine a simple CNN code that I can use to understand how they are implemented, how are the computations in each layer taking place, how the output of each layer is being fed to the input of the next one. I am familiar with the theoretical part But, I am not interested in training the CNN, I want a complete, self contained CNN code that is pre-trained and all the weights and biases values are known. I know that there are plenty of CNN libraries, i.e. Caffe, but the problem is that there is no trivial example code that is self contained.
Contains C and C++ programs from various categories and notes on data srtuctures and C program. C Porgram To Find convolutional Codes or Code.
Even for the simplest Caffe example 'cppclassification' many libraries are invoked, the architecture of the CNN is expressed as.prototxt file, other types of inputs such as.caffemodel and.binaryproto are involved. OpenCV2 libraries is invoked too. There are layers and layers of abstraction and different libraries working together to produce the classification outcome. I know that those abstractions are needed to generate a 'useable' CNN implementation, but for a hardware person who needs a bare-bone code to study, this is too much of 'un-related work'. My question is: Can anyone guide me into a simple and self-contained CNN implementation that I can start with? I can recommend.
It is simple, lightweight (e.g. Header-only) and CPU only, while providing several layers frequently used within the literature (as for example pooling layers, dropout layers or local response normalization layer). This means, that you can easily explore an efficient implementation of these layers in C without requiring knowledge of CUDA and digging through the I/O and framework code as required by framework such as. The implementation lacks some comments, but the code is still easy to read and understand. The provided is quite easy to use (tried it myself some time ago) and trains efficiently.
After training and testing, the weights are written to file. Then you have a simple pre-trained model from which you can start, see the provided and. It can easily be loaded for testing (or recognizing digits) such that you can debug the code while executing a learned model. If you want to inspect a more complicated network, have a look at the.
Simulation Source Code Examples-Tutorial on Convolutional Coding with Viterbi Decoding-Simulation Source Code Examples Read about how the family of Chinese prime minister Wen JiaBao enriched themselves by as much as US$2.7 Billion during his tenure as leader on the New York Times website: If you're behind the so-called 'Great Firewall,' try the BBC News website: or the Washington Post website: You can also try to read the pdf version in Chinese: Simulation Source Code Examples The simulation source code comprises a test driver routine and several functions, which will be described below. This code simulates a link through an AWGN channel from data source to Viterbi decoder output. The first dynamically allocates several arrays to store the source data, the convolutionally encoded source data, the output of the AWGN channel, and the data output by the Viterbi decoder. It calls the data generator, convolutional encoder, channel simulation, and Viterbi decoder functions in turn. It then compares the source data output by the data generator to the data output by the Viterbi decoder and counts the number of errors. Once 100 errors (sufficient for +/- 20% measurement error with 95% confidence) are accumulated, the test driver displays the BER for the given Es/No.
The test parameters are controlled by definitions in. The test driver includes a compile-time option to also measure the BER for an uncoded channel, i.e. A channel without forward error correction. I used this option to validate my Gaussian noise generator, by comparing the simulated uncoded BER to the theoretical uncoded BER given by, where E b/N 0 is expressed as a ratio, not in dB.
Convolutional Coding Tutorial
I am happy to say that the results agree quite closely. When running the simulations, it is important to remember the relationship between E s/N 0 and E b/N 0. As stated earlier, for the uncoded channel, E s/N 0 = E b/N 0, since there is one channel symbol per bit.
However, for the coded channel, E s/N 0 = E b /N 0 + 10log 10(k/n). For example, for rate 1/2 coding, E s/N 0 = E b/N 0 + 10log 10(1/2) = E b/N 0 - 3.01 dB. For rate 1/8 coding, E s/N 0 = E b/N 0 + 10log 10(1/8) = E b/N 0 - 9.03 dB.
The function simulates the data source. It accepts as arguments a pointer to an input array and the number of bits to generate, and fills the array with randomly-chosen zeroes and ones. The function accepts as arguments the pointers to the input and output arrays and the number of bits in the input array. It then performs the specified convolutional encoding and fills the output array with one/zero channel symbols. The convolutional code parameters are in the header file. The function accepts as arguments the desired E s/N 0, the number of channel symbols in the input array, and pointers to the input and output arrays.
Convolutional Code Matlab
It performs the binary (one and zero) to baseband signal level (+/- 1) mapping on the convolutional encoder channel symbol outputs. It then adds Gaussian random variables to the mapped symbols, and fills the output array. The output data are floating point numbers. The arguments to the function are the expected E s/N 0, the number of channel symbols in the input array, and pointers to its input and output arrays. First, the decoder function sets up its data structures, the arrays described in the algorithm description section. Then, it performs three-bit soft quantization on the floating point received channel symbols, using the expected E s/N 0, producing integers.
(Optionally, a fixed quantizer designed for a 4 dB E s/N 0 can be chosen.) This completes the preliminary processing. The next step is to start decoding the soft-decision channel symbols. The decoder builds up a trellis of depth K x 5, and then traces back to the beginning of the trellis and outputs one bit. The decoder then shifts the trellis left one time instant, discarding the oldest data, following which it computes the accumulated error metrics for the next time instant, traces back, and outputs a bit. The decoder continues in this way until it reaches the flushing bits. The flushing bits cause the encoder to converge back to state 0, and the decoder exploits this fact. Once the decoder builds the trellis for the last bit, it flushes the trellis, decoding and outputting all the bits in the trellis up to but not including the first flushing bit.
I have compiled and tested the simulation source code described above under Borland C Builder Version 3-please do not request help in modifying the code to compile under a different environment. Simulation results are presented Click on one of the links below to go to the beginning of that section: Copyright 1999-2003, Spectrum Applications.