updated example code on README and added Colab links
This commit is contained in:
sugarme
parent
c0da658e6b
commit
992ff860d4
101
README.md
101
README.md
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@ -53,7 +53,6 @@ func basicOps() {
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fmt.Printf("%8.3f\n", xs)
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fmt.Printf("%i", xs)
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// output
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/*
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(1,.,.) =
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0.391 0.055 0.638 0.514 0.757 0.446
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@ -93,46 +92,42 @@ func basicOps() {
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mul := ts1.MustMatmul(ts2, false)
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defer mul.MustDrop()
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fmt.Println("ts1: ")
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ts1.Print()
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fmt.Println("ts2: ")
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ts2.Print()
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fmt.Println("mul tensor (ts1 x ts2): ")
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mul.Print()
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//ts1:
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// 0 1 2
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// 3 4 5
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//[ CPULongType{2,3} ]
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//ts2:
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// 1 1 1 1
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// 1 1 1 1
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// 1 1 1 1
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//[ CPULongType{3,4} ]
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//mul tensor (ts1 x ts2):
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// 3 3 3 3
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// 12 12 12 12
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//[ CPULongType{2,4} ]
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fmt.Printf("ts1:\n%2d", ts1)
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fmt.Printf("ts2:\n%2d", ts2)
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fmt.Printf("mul tensor (ts1 x ts2):\n%2d", mul)
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/*
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ts1:
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0 1 2
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3 4 5
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ts2:
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1 1 1 1
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1 1 1 1
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1 1 1 1
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mul tensor (ts1 x ts2):
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3 3 3 3
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12 12 12 12
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*/
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// In-place operation
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ts3 := ts.MustOnes([]int64{2, 3}, gotch.Float, gotch.CPU)
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fmt.Println("Before:")
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ts3.Print()
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fmt.Printf("Before:\n%v", ts3)
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ts3.MustAdd1_(ts.FloatScalar(2.0))
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fmt.Printf("After (ts3 + 2.0): \n")
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ts3.Print()
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ts3.MustDrop()
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fmt.Printf("After (ts3 + 2.0):\n%v", ts3)
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//Before:
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// 1 1 1
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// 1 1 1
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//[ CPUFloatType{2,3} ]
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//After (ts3 + 2.0):
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// 3 3 3
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// 3 3 3
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//[ CPUFloatType{2,3} ]
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/*
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Before:
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1 1 1
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1 1 1
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After (ts3 + 2.0):
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3 3 3
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3 3 3
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*/
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}
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```
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@ -142,30 +137,32 @@ func basicOps() {
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```go
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import (
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"fmt"
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"github.com/sugarme/gotch"
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"github.com/sugarme/gotch/nn"
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ts "github.com/sugarme/gotch/tensor"
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)
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type Net struct {
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conv1 nn.Conv2D
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conv2 nn.Conv2D
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fc nn.Linear
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conv1 *nn.Conv2D
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conv2 *nn.Conv2D
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fc *nn.Linear
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}
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func newNet(vs nn.Path) Net {
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func newNet(vs *nn.Path) *Net {
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conv1 := nn.NewConv2D(vs, 1, 16, 2, nn.DefaultConv2DConfig())
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conv2 := nn.NewConv2D(vs, 16, 10, 2, nn.DefaultConv2DConfig())
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fc := nn.NewLinear(vs, 10, 10, nn.DefaultLinearConfig())
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return Net{
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return &Net{
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conv1,
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conv2,
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fc,
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}
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}
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func (n Net) ForwardT(xs ts.Tensor, train bool) (retVal ts.Tensor) {
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func (n Net) ForwardT(xs *ts.Tensor, train bool) *ts.Tensor {
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xs = xs.MustView([]int64{-1, 1, 8, 8}, false)
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outC1 := xs.Apply(n.conv1)
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@ -177,10 +174,8 @@ func basicOps() {
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outView2 := outMP2.MustView([]int64{-1, 10}, true)
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defer outView2.MustDrop()
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outFC := outView2.Apply(&n.fc)
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outFC := outView2.Apply(n.fc)
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return outFC.MustRelu(true)
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}
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func main() {
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@ -191,29 +186,33 @@ func basicOps() {
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xs := ts.MustOnes([]int64{8, 8}, gotch.Float, gotch.CPU)
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logits := net.ForwardT(xs, false)
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logits.Print()
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fmt.Printf("Logits: %0.3f", logits)
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}
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// 0.0000 0.0000 0.0000 0.2477 0.2437 0.0000 0.0000 0.0000 0.0000 0.0171
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//[ CPUFloatType{1,10} ]
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//Logits: 0.000 0.000 0.000 0.225 0.321 0.147 0.000 0.207 0.000 0.000
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```
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- Real application examples can be found at [example folder](example/README.md)
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## Play with GoTch on Google Colab
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1. [Tensor Initiation](tensor/tensor-initiation.ipynb) <a href="https://colab.research.google.com/github/sugarme/nb/blob/master/tensor/tensor-initiation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
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2. [Tensor Indexing](tensor/tensor-indexing.ipynb) <a href="https://colab.research.google.com/github/sugarme/nb/blob/master/tensor/tensor-indexing.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
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3. [MNIST](mnist) <a href="https://colab.research.google.com/github/sugarme/nb/blob/master/mnist/mnist.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
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4. [Tokenizer - BPE model](tokenizer/bpe.ipynb) <a href="https://colab.research.google.com/github/sugarme/nb/blob/master/tokenizer/bpe.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
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5. [transformer - BERT Mask Language Model](transformer/bert-mask-lm.ipynb) <a href="https://colab.research.google.com/github/sugarme/nb/blob/master/transformer/bert-mask-lm.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
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6. [YOLO v3 model infering](yolo/yolo.ipynb) <a href="https://colab.research.google.com/github/sugarme/nb/blob/master/yolo/yolo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
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More coming soon...
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## Getting Started
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- [Documentations](docs/README.md)
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- See [pkg.go.dev](https://pkg.go.dev/github.com/sugarme/gotch?tab=doc) for detail APIs
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## License
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GoTch is Apache 2.0 licensed.
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## Acknowledgement
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- This project has been inspired and used many concepts from [tch-rs](https://github.com/LaurentMazare/tch-rs)
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@ -1,496 +0,0 @@
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"name": "GoTch-MNIST-CNN.ipynb",
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"provenance": [],
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"collapsed_sections": [
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"fsitX3NbLbTB",
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"l6aOKEMarRNH",
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"mWe6_MtPK8Kh"
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],
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"toc_visible": true,
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"include_colab_link": true
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},
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"kernelspec": {
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"display_name": "Go",
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"name": "gophernotes"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/sugarme/gotch/blob/master/example/mnist/GoTch_MNIST_CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "dkq9izrfMXPV"
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},
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"source": [
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"# MNIST CNN Training Using GoTch - Pytorch C++ APIs Go Binding\n",
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"\n",
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"This notebook using\n",
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"\n",
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"1. [GoTch - Pytorch C++ APIs Go bindind](https://github.com/sugarme/gotch)\n",
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"2. [GopherNotes - Jupyter Notebook Go kernel](https://github.com/gopherdata/gophernotes)\n",
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"3. [MNIST dataset](http://yann.lecun.com/exdb/mnist/)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "fsitX3NbLbTB"
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},
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"source": [
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"## Install Go kernel - GopherNotes\n",
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"\n",
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"*NOTE: refresh/reload (browser) after this step.*"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "caT1iMfshw62",
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"outputId": "5be1cd7d-c12e-4083-d523-a035a9fc06a6",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"source": [
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"# run this cell first time using python runtime\n",
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"!add-apt-repository ppa:longsleep/golang-backports -y > /dev/null\n",
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"!apt update > /dev/null \n",
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"!apt install golang-go > /dev/null\n",
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"%env GOPATH=/root/go\n",
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"!go get -u github.com/gopherdata/gophernotes\n",
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"!cp ~/go/bin/gophernotes /usr/bin/\n",
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"!mkdir /usr/local/share/jupyter/kernels/gophernotes\n",
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"!cp ~/go/src/github.com/gopherdata/gophernotes/kernel/* \\\n",
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" /usr/local/share/jupyter/kernels/gophernotes\n",
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"# then refresh (browser), it will now use gophernotes. Skip to golang in later cells"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"\n",
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"WARNING: apt does not have a stable CLI interface. Use with caution in scripts.\n",
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"\n",
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"\n",
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"WARNING: apt does not have a stable CLI interface. Use with caution in scripts.\n",
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"\n",
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"env: GOPATH=/root/go\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "l6aOKEMarRNH"
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},
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"source": [
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"## Install Pytorch C++ APIs and Go binding - GoTch\n",
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"\n",
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"NOTE: `ldconfig` (GLIBC) current version 2.27 is currently broken when linking Libtorch library\n",
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"\n",
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"see issue: https://discuss.pytorch.org/libtorch-c-so-files-truncated-error-when-ldconfig/46404/6\n",
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"\n",
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"Google Colab default settings:\n",
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"```bash\n",
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"LD_LIBRARY_PATH=/usr/lib64-nvidia\n",
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"LIBRARY_PATH=/usr/local/cuda/lib64/stubs\n",
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"```\n",
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"We copy directly `libtorch/lib` to those paths as a hacky way. "
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "0N_jGbVft2Y7",
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"outputId": "e2052bf1-862f-49e7-ebed-23f09f7fb9e4",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"source": [
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"$wget -q --show-progress --progress=bar:force:noscroll -O /tmp/libtorch-cxx11-abi-shared-with-deps-1.7.0%2Bcu101.zip https://download.pytorch.org/libtorch/cu101/libtorch-cxx11-abi-shared-with-deps-1.7.0%2Bcu101.zip\n",
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"$unzip -qq /tmp/libtorch-cxx11-abi-shared-with-deps-1.7.0%2Bcu101.zip -d /usr/local\n",
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"$unzip -qq -j /tmp/libtorch-cxx11-abi-shared-with-deps-1.7.0%2Bcu101.zip libtorch/lib/* -d /usr/lib64-nvidia/\n",
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"$unzip -qq -j /tmp/libtorch-cxx11-abi-shared-with-deps-1.7.0%2Bcu101.zip libtorch/lib/* -d /usr/local/cuda/lib64/stubs/"
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],
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"/tmp/libtorch-cxx11 100%[===================>] 765.61M 22.4MB/s in 29s \n"
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],
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"name": "stderr"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "dVo3An-pGzFE"
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},
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"source": [
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"import(\"os\")\n",
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"os.Setenv(\"CPATH\", \"usr/local/libtorch/lib:/usr/local/libtorch/include:/usr/local/libtorch/include/torch/csrc/api/include\")"
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],
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"execution_count": 2,
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"outputs": []
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},
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{
|
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"cell_type": "code",
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"metadata": {
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"id": "x_9y6CpZGlPJ",
|
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"outputId": "79b538ff-9320-4097-8e76-43160253a60d",
|
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"colab": {
|
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"base_uri": "https://localhost:8080/"
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}
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},
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"source": [
|
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"$rm -f -- go.mod\n",
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"$go mod init github.com/sugarme/playgo\n",
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"$go get github.com/sugarme/gotch@v0.3.2"
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],
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"execution_count": 4,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
|
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"go: creating new go.mod: module github.com/sugarme/playgo\n",
|
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"go: downloading github.com/sugarme/gotch v0.3.2\n"
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],
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"name": "stderr"
|
||||
}
|
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]
|
||||
},
|
||||
{
|
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"cell_type": "markdown",
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"metadata": {
|
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"id": "mWe6_MtPK8Kh"
|
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},
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"source": [
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"## Download MNIST dataset"
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]
|
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},
|
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{
|
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"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "sYyKlDWchSv3",
|
||||
"outputId": "ba7b2bb6-f42f-4dbd-c96e-633f4b66e0f1",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
}
|
||||
},
|
||||
"source": [
|
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"$wget -q --show-progress --progress=bar:force:noscroll http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
|
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"$wget -q --show-progress --progress=bar:force:noscroll http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
|
||||
"$wget -q --show-progress --progress=bar:force:noscroll http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
|
||||
"$wget -q --show-progress --progress=bar:force:noscroll http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
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"\n",
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"$gunzip train-images-idx3-ubyte.gz\n",
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"$gunzip train-labels-idx1-ubyte.gz\n",
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"$gunzip t10k-images-idx3-ubyte.gz\n",
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"$gunzip t10k-labels-idx1-ubyte.gz"
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],
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"train-images-idx3-u 100%[===================>] 9.45M 11.2MB/s in 0.8s \n",
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"train-labels-idx1-u 100%[===================>] 28.20K --.-KB/s in 0.06s \n",
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"t10k-images-idx3-ub 100%[===================>] 1.57M 2.03MB/s in 0.8s \n",
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"t10k-labels-idx1-ub 100%[===================>] 4.44K --.-KB/s in 0s \n"
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],
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"name": "stderr"
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}
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]
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},
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{
|
||||
"cell_type": "markdown",
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"metadata": {
|
||||
"id": "7MzMZDHiLJQR"
|
||||
},
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"source": [
|
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"## Create Convolution Neural Network (CNN)"
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]
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},
|
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{
|
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"cell_type": "code",
|
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"metadata": {
|
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"id": "YkLmNrswJHYY"
|
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},
|
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"source": [
|
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"import(\n",
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" \"fmt\"\n",
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" \"time\"\n",
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"\n",
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" \"github.com/sugarme/gotch\"\n",
|
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" \"github.com/sugarme/gotch/nn\"\n",
|
||||
" ts \"github.com/sugarme/gotch/tensor\"\n",
|
||||
" \"github.com/sugarme/gotch/vision\"\n",
|
||||
") \n",
|
||||
"\n",
|
||||
"const (\n",
|
||||
" MnistDirCNN string = \"./\"\n",
|
||||
" epochsCNN = 100\n",
|
||||
" batchCNN = 256\n",
|
||||
" batchSize = 256\n",
|
||||
" LrCNN = 1e-4\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"type Net struct {\n",
|
||||
" conv1 *nn.Conv2D\n",
|
||||
" conv2 *nn.Conv2D\n",
|
||||
" fc1 *nn.Linear\n",
|
||||
" fc2 *nn.Linear\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"func newNet(vs *nn.Path) Net {\n",
|
||||
" conv1 := nn.NewConv2D(vs, 1, 32, 5, nn.DefaultConv2DConfig())\n",
|
||||
" conv2 := nn.NewConv2D(vs, 32, 64, 5, nn.DefaultConv2DConfig())\n",
|
||||
" fc1 := nn.NewLinear(vs, 1024, 1024, nn.DefaultLinearConfig())\n",
|
||||
" fc2 := nn.NewLinear(vs, 1024, 10, nn.DefaultLinearConfig())\n",
|
||||
"\n",
|
||||
" return Net{conv1,conv2,fc1,fc2}\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"func (n Net) ForwardT(xs *ts.Tensor, train bool) *ts.Tensor {\n",
|
||||
" outView1 := xs.MustView([]int64{-1, 1, 28, 28}, false)\n",
|
||||
" defer outView1.MustDrop()\n",
|
||||
" outC1 := outView1.Apply(n.conv1)\n",
|
||||
" outMP1 := outC1.MaxPool2DDefault(2, true)\n",
|
||||
" defer outMP1.MustDrop()\n",
|
||||
" outC2 := outMP1.Apply(n.conv2)\n",
|
||||
" outMP2 := outC2.MaxPool2DDefault(2, true)\n",
|
||||
" outView2 := outMP2.MustView([]int64{-1, 1024}, true)\n",
|
||||
" defer outView2.MustDrop()\n",
|
||||
" outFC1 := outView2.Apply(n.fc1)\n",
|
||||
" outRelu := outFC1.MustRelu(true)\n",
|
||||
" defer outRelu.MustDrop()\n",
|
||||
" outDropout := ts.MustDropout(outRelu, 0.5, train)\n",
|
||||
" defer outDropout.MustDrop()\n",
|
||||
" return outDropout.Apply(n.fc2)\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"func trainCNN(){\n",
|
||||
" var ds *vision.Dataset\n",
|
||||
" ds = vision.LoadMNISTDir(MnistDirCNN)\n",
|
||||
" testImages := ds.TestImages\n",
|
||||
" testLabels := ds.TestLabels\n",
|
||||
"\n",
|
||||
" cuda := gotch.CudaBuilder(0)\n",
|
||||
" vs := nn.NewVarStore(cuda.CudaIfAvailable())\n",
|
||||
"\n",
|
||||
" var cnn Net = newNet(vs.Root())\n",
|
||||
" opt, err := nn.DefaultAdamConfig().Build(vs, LrCNN)\n",
|
||||
" if err != nil {fmt.Print(err)}\n",
|
||||
"\n",
|
||||
" var bestAccuracy float64 = 0.0\n",
|
||||
" startTime := time.Now()\n",
|
||||
"\n",
|
||||
" for epoch := 0; epoch < epochsCNN; epoch++ {\n",
|
||||
" totalSize := ds.TrainImages.MustSize()[0]\n",
|
||||
" samples := int(totalSize)\n",
|
||||
" index := ts.MustRandperm(int64(totalSize), gotch.Int64, gotch.CPU)\n",
|
||||
" imagesTs := ds.TrainImages.MustIndexSelect(0, index, false)\n",
|
||||
" labelsTs := ds.TrainLabels.MustIndexSelect(0, index, false)\n",
|
||||
"\n",
|
||||
" batches := samples / batchSize\n",
|
||||
" batchIndex := 0\n",
|
||||
" var epocLoss *ts.Tensor\n",
|
||||
" for i := 0; i < batches; i++ {\n",
|
||||
" start := batchIndex * batchSize\n",
|
||||
" size := batchSize\n",
|
||||
" if samples-start < batchSize {break}\n",
|
||||
" batchIndex += 1\n",
|
||||
"\n",
|
||||
" // Indexing\n",
|
||||
" narrowIndex := ts.NewNarrow(int64(start), int64(start+size))\n",
|
||||
" bImages := imagesTs.Idx(narrowIndex)\n",
|
||||
" bLabels := labelsTs.Idx(narrowIndex)\n",
|
||||
"\n",
|
||||
" bImages = bImages.MustTo(vs.Device(), true)\n",
|
||||
" bLabels = bLabels.MustTo(vs.Device(), true)\n",
|
||||
"\n",
|
||||
" logits := cnn.ForwardT(bImages, true)\n",
|
||||
" loss := logits.CrossEntropyForLogits(bLabels)\n",
|
||||
"\n",
|
||||
" opt.BackwardStep(loss)\n",
|
||||
"\n",
|
||||
" epocLoss = loss.MustShallowClone()\n",
|
||||
" epocLoss.Detach_()\n",
|
||||
"\n",
|
||||
" bImages.MustDrop()\n",
|
||||
" bLabels.MustDrop()\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" testAccuracy := nn.BatchAccuracyForLogits(vs, cnn, testImages, testLabels, vs.Device(), 1024)\n",
|
||||
" fmt.Printf(\"Epoch: %v\\t Loss: %.2f \\t Test accuracy: %.2f%%\\n\", epoch, epocLoss.Float64Values()[0], testAccuracy*100.0)\n",
|
||||
" if testAccuracy > bestAccuracy {\n",
|
||||
" bestAccuracy = testAccuracy\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" epocLoss.MustDrop()\n",
|
||||
" imagesTs.MustDrop()\n",
|
||||
" labelsTs.MustDrop()\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" fmt.Printf(\"Best test accuracy: %.2f%%\\n\", bestAccuracy*100.0)\n",
|
||||
" fmt.Printf(\"Taken time:\\t%.2f mins\\n\", time.Since(startTime).Minutes())\n",
|
||||
"}"
|
||||
],
|
||||
"execution_count": 6,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "dIwh8dLLOzJ9"
|
||||
},
|
||||
"source": [
|
||||
"## Run train and evaluation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
"id": "0AW2nIHjKA9W",
|
||||
"outputId": "0123b6df-7edc-4f35-b874-2b4f1ec69e54",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"trainCNN()"
|
||||
],
|
||||
"execution_count": 7,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch: 0\t Loss: 0.24 \t Test accuracy: 93.34%\n",
|
||||
"Epoch: 1\t Loss: 0.19 \t Test accuracy: 96.02%\n",
|
||||
"Epoch: 2\t Loss: 0.09 \t Test accuracy: 97.10%\n",
|
||||
"Epoch: 3\t Loss: 0.10 \t Test accuracy: 97.66%\n",
|
||||
"Epoch: 4\t Loss: 0.03 \t Test accuracy: 98.13%\n",
|
||||
"Epoch: 5\t Loss: 0.05 \t Test accuracy: 98.43%\n",
|
||||
"Epoch: 6\t Loss: 0.09 \t Test accuracy: 98.60%\n",
|
||||
"Epoch: 7\t Loss: 0.05 \t Test accuracy: 98.80%\n",
|
||||
"Epoch: 8\t Loss: 0.03 \t Test accuracy: 98.80%\n",
|
||||
"Epoch: 9\t Loss: 0.05 \t Test accuracy: 98.89%\n",
|
||||
"Epoch: 10\t Loss: 0.02 \t Test accuracy: 98.88%\n",
|
||||
"Epoch: 11\t Loss: 0.03 \t Test accuracy: 98.98%\n",
|
||||
"Epoch: 12\t Loss: 0.03 \t Test accuracy: 99.05%\n",
|
||||
"Epoch: 13\t Loss: 0.04 \t Test accuracy: 99.06%\n",
|
||||
"Epoch: 14\t Loss: 0.02 \t Test accuracy: 99.07%\n",
|
||||
"Epoch: 15\t Loss: 0.02 \t Test accuracy: 98.98%\n",
|
||||
"Epoch: 16\t Loss: 0.02 \t Test accuracy: 99.06%\n",
|
||||
"Epoch: 17\t Loss: 0.01 \t Test accuracy: 99.09%\n",
|
||||
"Epoch: 18\t Loss: 0.02 \t Test accuracy: 99.14%\n",
|
||||
"Epoch: 19\t Loss: 0.01 \t Test accuracy: 99.09%\n",
|
||||
"Epoch: 20\t Loss: 0.02 \t Test accuracy: 99.12%\n",
|
||||
"Epoch: 21\t Loss: 0.03 \t Test accuracy: 99.13%\n",
|
||||
"Epoch: 22\t Loss: 0.02 \t Test accuracy: 99.11%\n",
|
||||
"Epoch: 23\t Loss: 0.01 \t Test accuracy: 99.10%\n",
|
||||
"Epoch: 24\t Loss: 0.01 \t Test accuracy: 99.15%\n",
|
||||
"Epoch: 25\t Loss: 0.01 \t Test accuracy: 99.15%\n",
|
||||
"Epoch: 26\t Loss: 0.01 \t Test accuracy: 99.22%\n",
|
||||
"Epoch: 27\t Loss: 0.00 \t Test accuracy: 99.27%\n",
|
||||
"Epoch: 28\t Loss: 0.01 \t Test accuracy: 99.15%\n",
|
||||
"Epoch: 29\t Loss: 0.00 \t Test accuracy: 99.09%\n",
|
||||
"Epoch: 30\t Loss: 0.01 \t Test accuracy: 99.15%\n",
|
||||
"Epoch: 31\t Loss: 0.00 \t Test accuracy: 99.18%\n",
|
||||
"Epoch: 32\t Loss: 0.00 \t Test accuracy: 99.15%\n",
|
||||
"Epoch: 33\t Loss: 0.00 \t Test accuracy: 99.23%\n",
|
||||
"Epoch: 34\t Loss: 0.01 \t Test accuracy: 99.22%\n",
|
||||
"Epoch: 35\t Loss: 0.00 \t Test accuracy: 99.16%\n",
|
||||
"Epoch: 36\t Loss: 0.00 \t Test accuracy: 99.22%\n",
|
||||
"Epoch: 37\t Loss: 0.01 \t Test accuracy: 99.26%\n",
|
||||
"Epoch: 38\t Loss: 0.00 \t Test accuracy: 99.15%\n",
|
||||
"Epoch: 39\t Loss: 0.01 \t Test accuracy: 99.19%\n",
|
||||
"Epoch: 40\t Loss: 0.01 \t Test accuracy: 99.24%\n",
|
||||
"Epoch: 41\t Loss: 0.01 \t Test accuracy: 99.24%\n",
|
||||
"Epoch: 42\t Loss: 0.01 \t Test accuracy: 99.22%\n",
|
||||
"Epoch: 43\t Loss: 0.00 \t Test accuracy: 99.22%\n",
|
||||
"Epoch: 44\t Loss: 0.00 \t Test accuracy: 99.14%\n",
|
||||
"Epoch: 45\t Loss: 0.00 \t Test accuracy: 99.23%\n",
|
||||
"Epoch: 46\t Loss: 0.00 \t Test accuracy: 99.24%\n",
|
||||
"Epoch: 47\t Loss: 0.00 \t Test accuracy: 99.21%\n",
|
||||
"Epoch: 48\t Loss: 0.00 \t Test accuracy: 99.25%\n",
|
||||
"Epoch: 49\t Loss: 0.00 \t Test accuracy: 99.21%\n",
|
||||
"Epoch: 50\t Loss: 0.01 \t Test accuracy: 99.26%\n",
|
||||
"Epoch: 51\t Loss: 0.01 \t Test accuracy: 99.14%\n",
|
||||
"Epoch: 52\t Loss: 0.01 \t Test accuracy: 99.26%\n",
|
||||
"Epoch: 53\t Loss: 0.00 \t Test accuracy: 99.23%\n",
|
||||
"Epoch: 54\t Loss: 0.00 \t Test accuracy: 99.28%\n",
|
||||
"Epoch: 55\t Loss: 0.02 \t Test accuracy: 99.24%\n",
|
||||
"Epoch: 56\t Loss: 0.00 \t Test accuracy: 99.26%\n",
|
||||
"Epoch: 57\t Loss: 0.00 \t Test accuracy: 99.25%\n",
|
||||
"Epoch: 58\t Loss: 0.01 \t Test accuracy: 99.22%\n",
|
||||
"Epoch: 59\t Loss: 0.01 \t Test accuracy: 99.25%\n",
|
||||
"Epoch: 60\t Loss: 0.00 \t Test accuracy: 99.26%\n",
|
||||
"Epoch: 61\t Loss: 0.01 \t Test accuracy: 99.24%\n",
|
||||
"Epoch: 62\t Loss: 0.00 \t Test accuracy: 99.25%\n",
|
||||
"Epoch: 63\t Loss: 0.00 \t Test accuracy: 99.24%\n",
|
||||
"Epoch: 64\t Loss: 0.00 \t Test accuracy: 99.27%\n",
|
||||
"Epoch: 65\t Loss: 0.00 \t Test accuracy: 99.27%\n",
|
||||
"Epoch: 66\t Loss: 0.00 \t Test accuracy: 99.31%\n",
|
||||
"Epoch: 67\t Loss: 0.00 \t Test accuracy: 99.25%\n",
|
||||
"Epoch: 68\t Loss: 0.00 \t Test accuracy: 99.25%\n",
|
||||
"Epoch: 69\t Loss: 0.00 \t Test accuracy: 99.31%\n",
|
||||
"Epoch: 70\t Loss: 0.00 \t Test accuracy: 99.24%\n",
|
||||
"Epoch: 71\t Loss: 0.00 \t Test accuracy: 99.22%\n",
|
||||
"Epoch: 72\t Loss: 0.00 \t Test accuracy: 99.25%\n",
|
||||
"Epoch: 73\t Loss: 0.00 \t Test accuracy: 99.32%\n",
|
||||
"Epoch: 74\t Loss: 0.00 \t Test accuracy: 99.26%\n",
|
||||
"Epoch: 75\t Loss: 0.00 \t Test accuracy: 99.21%\n",
|
||||
"Epoch: 76\t Loss: 0.00 \t Test accuracy: 99.22%\n",
|
||||
"Epoch: 77\t Loss: 0.01 \t Test accuracy: 99.24%\n",
|
||||
"Epoch: 78\t Loss: 0.00 \t Test accuracy: 99.25%\n",
|
||||
"Epoch: 79\t Loss: 0.00 \t Test accuracy: 99.31%\n",
|
||||
"Epoch: 80\t Loss: 0.00 \t Test accuracy: 99.18%\n",
|
||||
"Epoch: 81\t Loss: 0.00 \t Test accuracy: 99.31%\n",
|
||||
"Epoch: 82\t Loss: 0.00 \t Test accuracy: 99.28%\n",
|
||||
"Epoch: 83\t Loss: 0.00 \t Test accuracy: 99.18%\n",
|
||||
"Epoch: 84\t Loss: 0.01 \t Test accuracy: 99.24%\n",
|
||||
"Epoch: 85\t Loss: 0.00 \t Test accuracy: 99.24%\n",
|
||||
"Epoch: 86\t Loss: 0.00 \t Test accuracy: 99.29%\n",
|
||||
"Epoch: 87\t Loss: 0.00 \t Test accuracy: 99.33%\n",
|
||||
"Epoch: 88\t Loss: 0.00 \t Test accuracy: 99.28%\n",
|
||||
"Epoch: 89\t Loss: 0.00 \t Test accuracy: 99.24%\n",
|
||||
"Epoch: 90\t Loss: 0.00 \t Test accuracy: 99.35%\n",
|
||||
"Epoch: 91\t Loss: 0.00 \t Test accuracy: 99.22%\n",
|
||||
"Epoch: 92\t Loss: 0.00 \t Test accuracy: 99.23%\n",
|
||||
"Epoch: 93\t Loss: 0.00 \t Test accuracy: 99.22%\n",
|
||||
"Epoch: 94\t Loss: 0.00 \t Test accuracy: 99.25%\n",
|
||||
"Epoch: 95\t Loss: 0.02 \t Test accuracy: 99.25%\n",
|
||||
"Epoch: 96\t Loss: 0.00 \t Test accuracy: 99.26%\n",
|
||||
"Epoch: 97\t Loss: 0.00 \t Test accuracy: 99.29%\n",
|
||||
"Epoch: 98\t Loss: 0.00 \t Test accuracy: 99.23%\n",
|
||||
"Epoch: 99\t Loss: 0.00 \t Test accuracy: 99.20%\n",
|
||||
"Best test accuracy: 99.35%\n",
|
||||
"Taken time:\t1.97 mins\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
@ -1,5 +1,7 @@
|
|||
# Linear Regression, NN, and CNN on MNIST dataset
|
||||
|
||||
[](https://colab.research.google.com/github/sugarme/nb/blob/master/mnist/mnist.ipynb)
|
||||
|
||||
## MNIST
|
||||
|
||||
- MNIST files can be obtained from [this source](http://yann.lecun.com/exdb/mnist/) and put in `data/mnist` from
|
||||
|
|
@ -29,8 +31,6 @@
|
|||
|
||||
- Accuracy should be about **99.3%**.
|
||||
|
||||
## Run on Cloud
|
||||
|
||||
[](https://colab.research.google.com/github/sugarme/gotch/blob/master/example/mnist/gotch_mnist.ipynb)
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user