bc16dc0964
Fixes a typo that affected performance This sample is a port of the open source library, TensorFlow trained networks trained on MNIST Dataset (http://yann.lecun.com/exdb/mnist/) via inference using Metal Performance Shaders. The sample demonstrates how to encode different layers to the GPU and perform image recognition using trained parameters(weights and bias) that have been fetched from, pre-trained and saved network on TensorFlow. |
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| MPSCNNHelloWorld | ||
| MPSCNNHelloWorld.xcodeproj | ||
| LICENSE.txt | ||
| README.md | ||
README.md
MPSCNNHelloWorld: Simple Digit Detection Convolution Neural Networks (CNN)
This sample is a port of the open source library, TensorFlow trained networks trained on MNIST Dataset (http://yann.lecun.com/exdb/mnist/) via inference using Metal Performance Shaders. The sample demonstrates how to encode different layers to the GPU and perform image recognition using trained parameters(weights and bias) that have been fetched from, pre-trained and saved network on TensorFlow.
The Single Network can be found at: https://www.tensorflow.org/versions/r0.8/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners
The Deep Network can be found at: https://www.tensorflow.org/versions/r0.8/tutorials/mnist/pros/index.html#deep-mnist-for-experts
The network parameters are stored a binary .dat files that are memory-mapped when needed.
Requirements
Build
Xcode 8.0 or later; iOS 10.0 SDK or later
Runtime
iOS 10.0 or later
Device Feature Set
iOS GPU Family 2 v1 iOS GPU Family 2 v2 iOS GPU Family 3 v1
Copyright (C) 2016 Apple Inc. All rights reserved.