Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Tflearn | 9,602 | 481 | 8 | 20 days ago | 6 | November 11, 2020 | 576 | other | Python | |
Deep learning library featuring a higher-level API for TensorFlow. | ||||||||||
Darkflow | 6,111 | a month ago | 644 | gpl-3.0 | Python | |||||
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices | ||||||||||
Graph_nets | 5,284 | a year ago | n,ull | apache-2.0 | Python | |||||
Build Graph Nets in Tensorflow | ||||||||||
Graph_nets | 5,188 | 7 | 1 | a year ago | 7 | January 29, 2020 | 5 | apache-2.0 | Python | |
Build Graph Nets in Tensorflow | ||||||||||
Tensorflow_macos | 3,182 | 2 years ago | 217 | other | Shell | |||||
TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework. | ||||||||||
Yolov3 Tf2 | 2,501 | 2 months ago | 167 | mit | Jupyter Notebook | |||||
YoloV3 Implemented in Tensorflow 2.0 | ||||||||||
Luminoth | 2,395 | 8 months ago | 61 | bsd-3-clause | Python | |||||
Deep Learning toolkit for Computer Vision. | ||||||||||
Tfgo | 2,268 | 1 | 7 | 5 months ago | 6 | February 04, 2021 | 17 | apache-2.0 | Go | |
Tensorflow + Go, the gopher way | ||||||||||
Spektral | 2,259 | 6 | 6 months ago | 34 | June 01, 2023 | 61 | mit | Python | ||
Graph Neural Networks with Keras and Tensorflow 2. | ||||||||||
Tensorflow Onnx | 2,102 | 3 | 64 | 6 days ago | 32 | August 25, 2023 | 164 | apache-2.0 | Jupyter Notebook | |
Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX |
TensorFlow's Go bindings are hard to use: tfgo makes it easy!
No more problems like:
Also, it uses Method chaining making possible to write pleasant Go code.
go env -w GONOSUMDB="github.com/galeone/tensorflow"
to pull code directly using system installed git. It changes nothing in the user interface -- you can use go modules as usual.go get github.com/galeone/tfgo
The core data structure of the TensorFlow's Go bindings is the op.Scope
struct. tfgo allows creating new *op.Scope
that solves the scoping issue mentioned above.
Since we're defining a graph, let's start from its root (empty graph)
root := tg.NewRoot()
We can now place nodes into this graphs and connect them. Let's say we want to multiply a matrix for a column vector and then add another column vector to the result.
Here's the complete source code.
package main
import (
"fmt"
tg "github.com/galeone/tfgo"
tf "github.com/galeone/tensorflow/tensorflow/go"
)
func main() {
root := tg.NewRoot()
A := tg.NewTensor(root, tg.Const(root, [2][2]int32{{1, 2}, {-1, -2}}))
x := tg.NewTensor(root, tg.Const(root, [2][1]int64{{10}, {100}}))
b := tg.NewTensor(root, tg.Const(root, [2][1]int32{{-10}, {10}}))
Y := A.MatMul(x.Output).Add(b.Output)
// Please note that Y is just a pointer to A!
// If we want to create a different node in the graph, we have to clone Y
// or equivalently A
Z := A.Clone()
results := tg.Exec(root, []tf.Output{Y.Output, Z.Output}, nil, &tf.SessionOptions{})
fmt.Println("Y: ", results[0].Value(), "Z: ", results[1].Value())
fmt.Println("Y == A", Y == A) // ==> true
fmt.Println("Z == A", Z == A) // ==> false
}
that produces
Y: [[200] [-200]] Z: [[200] [-200]]
Y == A true
Z == A false
The list of the available methods is available on GoDoc: http://godoc.org/github.com/galeone/tfgo
TensorFlow is rich of methods for performing operations on images. tfgo provides the image
package that allows using the Go bindings to perform computer vision tasks in an elegant way.
For instance, it's possible to read an image, compute its directional derivative along the horizontal and vertical directions, compute the gradient and save it.
The code below does that, showing the different results achieved using correlation and convolution operations.
package main
import (
tg "github.com/galeone/tfgo"
"github.com/galeone/tfgo/image"
"github.com/galeone/tfgo/image/filter"
"github.com/galeone/tfgo/image/padding"
tf "github.com/galeone/tensorflow/tensorflow/go"
"os"
)
func main() {
root := tg.NewRoot()
grayImg := image.Read(root, "/home/pgaleone/airplane.png", 1)
grayImg = grayImg.Scale(0, 255)
// Edge detection using sobel filter: convolution
Gx := grayImg.Clone().Convolve(filter.SobelX(root), image.Stride{X: 1, Y: 1}, padding.SAME)
Gy := grayImg.Clone().Convolve(filter.SobelY(root), image.Stride{X: 1, Y: 1}, padding.SAME)
convoluteEdges := image.NewImage(root.SubScope("edge"), Gx.Square().Add(Gy.Square().Value()).Sqrt().Value()).EncodeJPEG()
Gx = grayImg.Clone().Correlate(filter.SobelX(root), image.Stride{X: 1, Y: 1}, padding.SAME)
Gy = grayImg.Clone().Correlate(filter.SobelY(root), image.Stride{X: 1, Y: 1}, padding.SAME)
correlateEdges := image.NewImage(root.SubScope("edge"), Gx.Square().Add(Gy.Square().Value()).Sqrt().Value()).EncodeJPEG()
results := tg.Exec(root, []tf.Output{convoluteEdges, correlateEdges}, nil, &tf.SessionOptions{})
file, _ := os.Create("convolved.png")
file.WriteString(results[0].Value().(string))
file.Close()
file, _ = os.Create("correlated.png")
file.WriteString(results[1].Value().(string))
file.Close()
}
airplane.png
convolved.png
correlated.png
the list of the available methods is available on GoDoc: http://godoc.org/github.com/galeone/tfgo/image
TensorFlow 2 comes with a lot of easy way to export a computational graph (e.g. Keras model, or a function decorated with @tf.function
) to the SavedModel
serialization format (that's the only one officially supported).
Using TensorFlow 2 (with Keras or tf.function) + tfgo, exporting a trained model (or a generic computational graph) and use it in Go is straightforward.
Just dig into the example to understand how to serve a trained model with tfgo
.
import tensorflow as tf
model = tf.keras.Sequential(
[
tf.keras.layers.Conv2D(
8,
(3, 3),
strides=(2, 2),
padding="valid",
input_shape=(28, 28, 1),
activation=tf.nn.relu,
name="inputs",
), # 14x14x8
tf.keras.layers.Conv2D(
16, (3, 3), strides=(2, 2), padding="valid", activation=tf.nn.relu
), # 7x716
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(10, name="logits"), # linear
]
)
tf.saved_model.save(model, "output/keras")
package main
import (
"fmt"
tg "github.com/galeone/tfgo"
tf "github.com/galeone/tensorflow/tensorflow/go"
)
func main() {
// A model exported with tf.saved_model.save()
// automatically comes with the "serve" tag because the SavedModel
// file format is designed for serving.
// This tag contains the various functions exported. Among these, there is
// always present the "serving_default" signature_def. This signature def
// works exactly like the TF 1.x graph. Get the input tensor and the output tensor,
// and use them as placeholder to feed and output to get, respectively.
// To get info inside a SavedModel the best tool is saved_model_cli
// that comes with the TensorFlow Python package.
// e.g. saved_model_cli show --all --dir output/keras
// gives, among the others, this info:
// signature_def['serving_default']:
// The given SavedModel SignatureDef contains the following input(s):
// inputs['inputs_input'] tensor_info:
// dtype: DT_FLOAT
// shape: (-1, 28, 28, 1)
// name: serving_default_inputs_input:0
// The given SavedModel SignatureDef contains the following output(s):
// outputs['logits'] tensor_info:
// dtype: DT_FLOAT
// shape: (-1, 10)
// name: StatefulPartitionedCall:0
// Method name is: tensorflow/serving/predict
model := tg.LoadModel("test_models/output/keras", []string{"serve"}, nil)
fakeInput, _ := tf.NewTensor([1][28][28][1]float32{})
results := model.Exec([]tf.Output{
model.Op("StatefulPartitionedCall", 0),
}, map[tf.Output]*tf.Tensor{
model.Op("serving_default_inputs_input", 0): fakeInput,
})
predictions := results[0]
fmt.Println(predictions.Value())
}
Thinking about computation represented using graphs, describing computing in this way is, in one word, challenging.
Also, tfgo brings GPU computations to Go and allows writing parallel code without worrying about the device that executes it (just place the graph into the device you desire: that's it!)
I love contributions. Seriously. Having people that share your same interests and want to face your same challenges it's something awesome.
If you'd like to contribute, just dig in the code and see what can be added or improved. Start a discussion opening an issue and let's talk about it.
Just follow the same design I use into the image
package ("override" the same Tensor
methods, document the methods, test your changes, ...)
There are a lot of packages that can be added, like the image
package. Feel free to work on a brand new package: I'd love to see this kind of contributions!
On MacOS you can brew install libtensorflow
(assuming you have brew installed. Brew is a package manager. If you need help installing brew follow instructions here: https://docs.brew.sh/Installation )
Download and install the C library from https://www.tensorflow.org/install/lang_c
curl -L "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.9.1.tar.gz" | sudo tar -C /usr/local -xz
sudo ldconfig
docker pull tensorflow/tensorflow:2.9.1
Or you can use system package manager.