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- Itisacomputational system inspired by the Structure Processing Method Learning Ability of a biological brain -Characteristics of Artificial Neural Netw orks Alarge number of very simple processing neuron-likeprocessing elements Alarge number of weighted connections between the elements Distributed representation of knowledge overthe connections.
Getting Started
Introduction
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KANN is a standalone and lightweight library in C for constructing and trainingsmall to medium artificial neural networks such as multi-layerperceptrons, convolutional neural networks and recurrent neuralnetworks (including LSTM and GRU). It implementsgraph-based reverse-mode automatic differentiation and allows to buildtopologically complex neural networks with recurrence, shared weights andmultiple inputs/outputs/costs. In comparison to mainstream deep learningframeworks such as TensorFlow, KANN is not as scalable, but it is closein flexibility, has a much smaller code base and only depends on the standard Clibrary. In comparison to other lightweight frameworks such as tiny-dnn,KANN is still smaller, times faster and much more versatile, supporting RNN,VAE and non-standard neural networks that may fail these lightweightframeworks.
KANN could be potentially useful when you want to experiment small to mediumneural networks in C/C++, to deploy no-so-large models without worrying aboutdependency hell, or to learn the internals of deep learning libraries.
Features
- Flexible. Model construction by building a computational graph withoperators. Support RNNs, weight sharing and multiple inputs/outputs.
- Efficient. Reasonably optimized matrix product and convolution. Supportmini-batching and effective multi-threading. Sometimes faster than mainstreamframeworks in their CPU-only mode.
- Small and portable. As of now, KANN has less than 4000 lines of code in foursource code files, with no non-standard dependencies by default. Compatible withANSI C compilers.
Limitations
- CPU only. As such, KANN is not intended for training huge neuralnetworks.
- Lack of some common operators and architectures such as batch normalization.
- Verbose APIs for training RNNs.
Installation
The KANN library is composed of four files:
kautodiff.{h,c}
and kann.{h,c}
.You are encouraged to include these files in your source code tree. Noinstallation is needed. To compile examples:This generates a few executables in the examples directory.
Documentations
Comments in the header files briefly explain the APIs. More documentations canbe found in the doc directory. Examples using the library are in theexamples directory.
A tour of basic KANN APIs
Introduction To Artificial Neural Network By Zurada Pdf Files Online
Working with neural networks usually involves three steps: model construction,training and prediction. We can use layer APIs to build a simple model:
For this simple feedforward model with one input and one output, we can trainit with:
We can save the model to a file with
kann_save()
or use it to classify aMNIST image:Working with complex models requires to use low-level APIs. Please see01user.md for details.
A complete example
Patch pes 2011 pc bundesliga e serie breaking news. This example learns to count the number of '1' bits in an integer (i.e.popcount):
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Benchmarks
- First of all, this benchmark only evaluates relatively small networks, butin practice, it is huge networks on GPUs that really demonstrate the truepower of mainstream deep learning frameworks. Please don't read too much intothe table.
- 'Linux' has 48 cores on two Xeno E5-2697 CPUs at 2.7GHz. MKL, NumPy-1.12.0and Theano-0.8.2 were installed with Conda; Keras-1.2.2 installed with pip.The official TensorFlow-1.0.0 wheel does not work with Cent OS 6 on thismachine, due to glibc. This machine has one Tesla K40c GPU installed. We areusing by CUDA-7.0 and cuDNN-4.0 for training on GPU.
- 'Mac' has 4 cores on a Core i7-3667U CPU at 2GHz. MKL, NumPy and Theano camewith Conda, too. Keras-1.2.2 and Tensorflow-1.0.0 were installed with pip. Onboth machines, Tiny-DNN was acquired from github on March 1st, 2017.
- mnist-mlp implements a simple MLP with one layer of 64 hidden neurons.mnist-cnn applies two convolutional layers with 32 3-by-3 kernels and ReLUactivation, followed by 2-by-2 max pooling and one 128-neuron dense layer.mul100-rnn uses two GRUs of size 160. Both input and output are 2-Dbinary arrays of shape (14,2) -- 28 GRU operations for each of the 30000training samples.
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Task | Framework | Machine | Device | Real | CPU | Command line |
---|---|---|---|---|---|---|
mnist-mlp | KANN+SSE | Linux | 1 CPU | 31.3s | 31.2s | mlp -m20 -v0 |
Mac | 1 CPU | 27.1s | 27.1s | |||
KANN+BLAS | Linux | 1 CPU | 18.8s | 18.8s | ||
Theano+Keras | Linux | 1 CPU | 33.7s | 33.2s | keras/mlp.py -m20 -v0 | |
4 CPUs | 32.0s | 121.3s | ||||
Mac | 1 CPU | 37.2s | 35.2s | |||
2 CPUs | 32.9s | 62.0s | ||||
TensorFlow | Mac | 1 CPU | 33.4s | 33.4s | tensorflow/mlp.py -m20 | |
2 CPUs | 29.2s | 50.6s | tensorflow/mlp.py -m20 -t2 | |||
Tiny-dnn | Linux | 1 CPU | 2m19s | 2m18s | tiny-dnn/mlp -m20 | |
Tiny-dnn+AVX | Linux | 1 CPU | 1m34s | 1m33s | ||
Mac | 1 CPU | 2m17s | 2m16s | |||
mnist-cnn | KANN+SSE | Linux | 1 CPU | 57m57s | 57m53s | mnist-cnn -v0 -m15 |
4 CPUs | 19m09s | 68m17s | mnist-cnn -v0 -t4 -m15 | |||
Theano+Keras | Linux | 1 CPU | 37m12s | 37m09s | keras/mlp.py -Cm15 -v0 | |
4 CPUs | 24m24s | 97m22s | ||||
1 GPU | 2m57s | keras/mlp.py -Cm15 -v0 | ||||
Tiny-dnn+AVX | Linux | 1 CPU | 300m40s | 300m23s | tiny-dnn/mlp -Cm15 | |
mul100-rnn | KANN+SSE | Linux | 1 CPU | 40m05s | 40m02s | rnn-bit -l2 -n160 -m25 -Nd0 |
4 CPUs | 12m13s | 44m40s | rnn-bit -l2 -n160 -t4 -m25 -Nd0 | |||
KANN+BLAS | Linux | 1 CPU | 22m58s | 22m56s | rnn-bit -l2 -n160 -m25 -Nd0 | |
4 CPUs | 8m18s | 31m26s | rnn-bit -l2 -n160 -t4 -m25 -Nd0 | |||
Theano+Keras | Linux | 1 CPU | 27m30s | 27m27s | rnn-bit.py -l2 -n160 -m25 | |
4 CPUs | 19m52s | 77m45s |
- In the single thread mode, Theano is about 50% faster than KANN probably dueto efficient matrix multiplication (aka.
sgemm
) implemented in MKL. As isshown in a previous micro-benchmark, MKL/OpenBLAS can be twice asfast as the implementation in KANN. - KANN can optionally use the
sgemm
routine from a BLAS library (enabled bymacroHAVE_CBLAS
). Linked against OpenBLAS-0.2.19, KANN matches thesingle-thread performance of Theano on Mul100-rnn. KANN doesn't reduceconvolution to matrix multiplication, so MNIST-cnn won't benefit fromOpenBLAS. We observed that OpenBLAS is slower than the native KANNimplementation when we use a mini-batch of size 1. The cause is unknown. - KANN's intra-batch multi-threading model is better than Theano+Keras.However, in its current form, this model probably won't get alone well withGPUs.