Golang應用程式使用TensorFlow 或是 Tensorflow Lite(TFLite) 的前提是必須要有Tensorflow C 函式庫 或是 TensorFlow Lite C 函式庫,一般來說,比較少情況會是用Golang應用程式使用TensorFlow透過TensorFlow C 函式庫,因為未經過最佳化而效能不佳。Golang應用程式使用TensorFlow Lite(TFLite) 透過TensorFlow Lite C 函式庫會是效能較好的其中一種方式。以下將整理相關資訊來說明:
Showing posts with label Tensorflow. Show all posts
Showing posts with label Tensorflow. Show all posts
Wednesday, May 25, 2022
Wednesday, June 12, 2019
[TensorFlow] Build TensorFlow from source with Intel MKL enabled
Based on this document "Intel® Math Kernel Library for Deep Learning Networks: Part 1–Overview and Installation", I give a quick summary to do it.
Here are the steps for building TensorFlow from source with Intel MKL enabled.
For MKL DNN:
Here are the steps for building TensorFlow from source with Intel MKL enabled.
For MKL DNN:
# Install Intel MKL & MKL-DNN
$ git clone https://github.com/01org/mkl-dnn.git
$ cd mkl-dnn
$ cd scripts && ./prepare_mkl.sh && cd ..
$ mkdir -p build && cd build && cmake .. && make -j$(nproc)
$ make test
$ sudo make install
For building TensorFlow:Friday, March 15, 2019
[TensorFlow] Build TensorFlow v1.12 from the source on Ubuntu 16.04
My previous post: [TensorFlow] How to build your C++ program or application with TensorFlow library using CMake
Second, it seems that there are a few issues when building TensorFlow v1.12 that we need to deal with it case by case.
It is for building TensorFlow from the source based on v1.10. Currently, I want to upgrade it to v1.12 and encounter some problems.
First, my version of ProtoBuf library on my system is v3.6.1 so that we should align its version in the TensorFlow.Second, it seems that there are a few issues when building TensorFlow v1.12 that we need to deal with it case by case.
Monday, March 11, 2019
[TensorFlow Lite] My first try with TensorFlow Lite
I just take my first try with the example: label_image (tensorflow/contrib/lite/examples/label_image) in TensorFlow Lite and write down the commands that I used.
There are a bunch of information from the offical TensorFlow Lite guide:
https://www.tensorflow.org/lite/guide
1. convert the example of model to tflite format
There are a bunch of information from the offical TensorFlow Lite guide:
https://www.tensorflow.org/lite/guide
1. convert the example of model to tflite format
Wednesday, March 6, 2019
[Tool] Convert TensorFlow graph to UFF format
The previous post: How to use TensorRT to do inference with TensorFlow model ? has introduced away to do the converting job for UFF format model. But, basically there are 2 ways to do that:
1. Convert TensorFlow's Session GraphDef directly on the fly to UFF format model
==> convert_uff_from_tensorflow()
2. Convert the frozen model file to UFF format model
==> convert_uff_from_frozen_model()
The following code is about the functions to convert TensorFlow graph to UFF format for running with TensorRT.
1. Convert TensorFlow's Session GraphDef directly on the fly to UFF format model
==> convert_uff_from_tensorflow()
2. Convert the frozen model file to UFF format model
==> convert_uff_from_frozen_model()
The following code is about the functions to convert TensorFlow graph to UFF format for running with TensorRT.
Monday, February 25, 2019
[Inspecting Graphs] Use TensorFlow's summarize_graph tool to find the input and output node names in the frozen model/graph
When trying to do inferencing using a frozen model from downloading or freezing by yourself, we may encounter a problem about what the input and output node names are in this model? If we cannot figure them out, it is impossible for you to do inferencing correctly. Here is an easy way to get the possible ones: using the tool: "summarize_graph"
No variables spotted.
Found 4 possible outputs: (name=detection_boxes, op=Identity) (name=detection_scores, op=Identity) (name=num_detections, op=Identity) (name=detection_classes, op=Identity)
Found 48132698 (48.13M) const parameters, 0 (0) variable parameters, and 4163 control_edges
Op types used: 4688 Const, 885 StridedSlice, 559 Gather, 485 Mul, 472 Sub, 462 Minimum, 369 Maximum, 304 Reshape, 276 Split, 205 RealDiv, 204 Pack, 202 ConcatV2, 201 Cast, 188 Greater, 183 Where, 149 Shape, 145 Add, 109 BiasAdd, 107 Conv2D, 106 Slice, 100 Relu, 99 Unpack, 97 Squeeze, 94 ZerosLike, 91 NonMaxSuppressionV2, 55 Enter, 46 Identity, 45 Switch, 27 Range, 24 Merge, 22 TensorArrayV3, 17 ExpandDims, 15 NextIteration, 12 TensorArrayScatterV3, 12 TensorArrayReadV3, 10 TensorArrayWriteV3, 10 Exit, 10 Tile, 10 TensorArrayGatherV3, 10 TensorArraySizeV3, 6 Transpose, 6 Fill, 6 Assert, 5 Less, 5 LoopCond, 5 Equal, 4 Round, 4 Exp, 4 MaxPool, 3 Pad, 2 Softmax, 2 Size, 2 GreaterEqual, 2 TopKV2, 2 MatMul, 1 All, 1 CropAndResize, 1 ResizeBilinear, 1 Relu6, 1 Placeholder, 1 LogicalAnd, 1 Max, 1 Mean
To use with tensorflow/tools/benchmark:benchmark_model try these arguments:
bazel run tensorflow/tools/benchmark:benchmark_model -- --graph=/danny/tmp/faster_rcnn_resnet101_coco_2018_01_28/frozen_inference_graph.pb --show_flops --input_layer=image_tensor --input_layer_type=uint8 --input_layer_shape=-1,-1,-1,3 --output_layer=detection_boxes,detection_scores,num_detections,detection_classes
For more information, please refer to this:
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms#inspecting-graphs
bazel build tensorflow/tools/graph_transforms:summarize_graph
bazel-bin/tensorflow/tools/graph_transforms/summarize_graph --in_graph=/danny/tmp/faster_rcnn_resnet101_coco_2018_01_28/frozen_inference_graph.pb
Found 1 possible inputs: (name=image_tensor, type=uint8(4), shape=[?,?,?,3])No variables spotted.
Found 4 possible outputs: (name=detection_boxes, op=Identity) (name=detection_scores, op=Identity) (name=num_detections, op=Identity) (name=detection_classes, op=Identity)
Found 48132698 (48.13M) const parameters, 0 (0) variable parameters, and 4163 control_edges
Op types used: 4688 Const, 885 StridedSlice, 559 Gather, 485 Mul, 472 Sub, 462 Minimum, 369 Maximum, 304 Reshape, 276 Split, 205 RealDiv, 204 Pack, 202 ConcatV2, 201 Cast, 188 Greater, 183 Where, 149 Shape, 145 Add, 109 BiasAdd, 107 Conv2D, 106 Slice, 100 Relu, 99 Unpack, 97 Squeeze, 94 ZerosLike, 91 NonMaxSuppressionV2, 55 Enter, 46 Identity, 45 Switch, 27 Range, 24 Merge, 22 TensorArrayV3, 17 ExpandDims, 15 NextIteration, 12 TensorArrayScatterV3, 12 TensorArrayReadV3, 10 TensorArrayWriteV3, 10 Exit, 10 Tile, 10 TensorArrayGatherV3, 10 TensorArraySizeV3, 6 Transpose, 6 Fill, 6 Assert, 5 Less, 5 LoopCond, 5 Equal, 4 Round, 4 Exp, 4 MaxPool, 3 Pad, 2 Softmax, 2 Size, 2 GreaterEqual, 2 TopKV2, 2 MatMul, 1 All, 1 CropAndResize, 1 ResizeBilinear, 1 Relu6, 1 Placeholder, 1 LogicalAnd, 1 Max, 1 Mean
To use with tensorflow/tools/benchmark:benchmark_model try these arguments:
bazel run tensorflow/tools/benchmark:benchmark_model -- --graph=/danny/tmp/faster_rcnn_resnet101_coco_2018_01_28/frozen_inference_graph.pb --show_flops --input_layer=image_tensor --input_layer_type=uint8 --input_layer_shape=-1,-1,-1,3 --output_layer=detection_boxes,detection_scores,num_detections,detection_classes
For more information, please refer to this:
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms#inspecting-graphs
Wednesday, January 30, 2019
[TFRecord] The easy way to verify your TFRecord file
There is a common situation when you build your TFRecord file ( your dataset ) and want to verify the correctness of the data in it. How to do it? I assume you don't have the problem to build your TFRecord file. So, the easy way to verify your TFRecord file is to use the API: tf.python_io.tf_record_iterator().
Wednesday, January 16, 2019
[TensorFlow] How to use Distribution Strategy in TensorFlow?
Learned from What’s coming in TensorFlow 2.0, TensorFlow 2.0 is coming soon and there are several features which are ready to use already, for instance, Distribution Strategy. Quoted from the article,
"For large ML training tasks, the Distribution Strategy API makes it easy to distribute and train models on different hardware configurations without changing the model definition. Since TensorFlow provides support for a range of hardware accelerators like CPUs, GPUs, and TPUs, you can enable training workloads to be distributed to single-node/multi-accelerator as well as multi-node/multi-accelerator configurations, including TPU Pods. Although this API supports a variety of cluster configurations, templates to deploy training on Kubernetes clusters in on-prem or cloud environments are provided."
"For large ML training tasks, the Distribution Strategy API makes it easy to distribute and train models on different hardware configurations without changing the model definition. Since TensorFlow provides support for a range of hardware accelerators like CPUs, GPUs, and TPUs, you can enable training workloads to be distributed to single-node/multi-accelerator as well as multi-node/multi-accelerator configurations, including TPU Pods. Although this API supports a variety of cluster configurations, templates to deploy training on Kubernetes clusters in on-prem or cloud environments are provided."
Monday, January 7, 2019
[TensorRT] How to use TensorRT to do inference with TensorFlow model ?
TensorRT is a high-performance deep learning inference optimizer and runtime that delivers low latency, high-throughput inference for deep learning applications. Here I am going to demonstrate that how to use TensorRT to do inference with TensorFlow model.
Install TensorRT
Please refer to this official website first:
https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html#installing
After downloading TensorRT 4.0 ( in my case ), we can install it.
Install TensorRT
Please refer to this official website first:
https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html#installing
After downloading TensorRT 4.0 ( in my case ), we can install it.
$ dpkg -i nv-tensorrt-repo-ubuntu1604-cuda9.0-ga-trt4.0.1.6-20180612_1-1_amd64.deb
$ apt-get update
$ apt-get install tensorrt
$ apt-get install python-libnvinfer-dev
$ apt-get install uff-converter-tf
Friday, January 4, 2019
[TensorFlow] How to write op with gradient in python?
Recently for some reasons, I studied the Domain-Adversarial Training of Neural Networks and it can be downloaded from http://jmlr.org/papers/volume17/15-239/15-239.pdf
In this paper, there is the key point that we should implement "Gradient Reversal Layer" for Discriminator to use it to connect the feature extractor. I found the source to implement it by replacing Identity op's gradient function as follows:
In this paper, there is the key point that we should implement "Gradient Reversal Layer" for Discriminator to use it to connect the feature extractor. I found the source to implement it by replacing Identity op's gradient function as follows:
Thursday, January 3, 2019
[TensorFlow] How to generate the Memory Report from Grappler?
In the previous post, I introduce the way to generate cost and model report from Grappler.
https://danny270degree.blogspot.com/2019/01/tensorflow-how-to-generate-cost-and.html
In this post, I will continue to introduce the memory report which I think that is very useful. Please refer to my previous post to get the model code.
https://danny270degree.blogspot.com/2019/01/tensorflow-how-to-generate-cost-and.html
In this post, I will continue to introduce the memory report which I think that is very useful. Please refer to my previous post to get the model code.
[TensorFlow] How to generate the Cost and Model Report from Grappler?
General speaking, Grappler in Tensorflow has several optimizers to do the specific area optimizations, such as for reducing the peak memory usage in GPU. So, I want to introduce some useful functions inside Grappler which are used for Simple Placer mechanism. And, these functions are also partially used in Grappler's optimizers.
Monday, December 24, 2018
[TensorFlow] My example of using SavedModelBuilder to do inference in TensorFlow
The purpose of this post is to show my example of SavedModelBuilder to do inference in TensorFlow. From my experiment, this approach can save a model with the signature that has input and output node name. And SavedModelBuilder can restore the graph based on the previously saved model pb file and the signature definition. Once, the restore is done, the inference task can be executed directly without GPU device needed if the training task is on GPU device.
Monday, December 17, 2018
[Reinforcement Learning] Get started to learn Sarsa(lambda λ) for reinforcement learning
Once you know what the Sarsa algorithm is, you can continue to learn Sarsa(lambda λ) algorithm.
I basically refer to these tutorial documents (written in Chinese) :
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/3-3-A-sarsa-lambda/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/3-3-tabular-sarsa-lambda/
https://zhuanlan.zhihu.com/p/28108498
The Sarsa(lambda λ) algorithm looks like this:
I basically refer to these tutorial documents (written in Chinese) :
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/3-3-A-sarsa-lambda/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/3-3-tabular-sarsa-lambda/
https://zhuanlan.zhihu.com/p/28108498
The Sarsa(lambda λ) algorithm looks like this:
Wednesday, November 28, 2018
[Reinforcement Learning] Get started to learn DQN for reinforcement learning
The previous post about Q-Learning is here:
[Reinforcement Learning] Get started to learn Q-Learning for reinforcement learning
Basically, Deep Q-Learning ( DQN ) is upgraded the Q-Learning algorithm and the Q-table is replaced by the neural network. For the DQN tutorial, I refer to these as follows: ( sorry, they are written in Chinese )
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-1-A-DQN/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-1-DQN1/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-2-DQN2/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-3-DQN3/
[Reinforcement Learning] Get started to learn Q-Learning for reinforcement learning
Basically, Deep Q-Learning ( DQN ) is upgraded the Q-Learning algorithm and the Q-table is replaced by the neural network. For the DQN tutorial, I refer to these as follows: ( sorry, they are written in Chinese )
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-1-A-DQN/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-1-DQN1/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-2-DQN2/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-3-DQN3/
Thursday, November 22, 2018
[Reinforcement Learning] Get started to learn Q-Learning for reinforcement learning
The previous post about reinforcement learning:
[Reinforcement Learning] Get started to learn gradient method for reinforcement learning
For the Q-Learning tutorial, I refer to these as follows: ( sorry, they are written in Chinese )
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/2-2-A-q-learning/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/2-2-tabular-q1/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/2-3-tabular-q2/
[Reinforcement Learning] Get started to learn gradient method for reinforcement learning
For the Q-Learning tutorial, I refer to these as follows: ( sorry, they are written in Chinese )
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/2-2-A-q-learning/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/2-2-tabular-q1/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/2-3-tabular-q2/
Wednesday, November 21, 2018
[Reinforcement Learning] Get started to learn policy gradient method for reinforcement learning
This post is about my first time to learn policy gradient method for reinforcement learning. Basically, there are already a lot of materials on the internet, but in this time, I only want to focus on a tutorial as follows: ( sorry, they are written in Chinese )
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/5-1-policy-gradient-softmax1/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/5-2-policy-gradient-softmax2/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/5-1-policy-gradient-softmax1/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/5-2-policy-gradient-softmax2/
Thursday, November 15, 2018
[RNN] What are the difference of input and output's tensor shape in dynamic_rnn and static_rnn using TensorFlow
When studying RNN, my first issue encountered in my program is about the shape of input and output tensors. Shape is a very important information to connect between layers. Here I just directly point out what are differences in input/output shape of static RNN and dynamic RNN.
P.S: If you use Keras to write your RNN model, you won't need to deal with these details.
P.S: If you use Keras to write your RNN model, you won't need to deal with these details.
Tuesday, November 13, 2018
[TensorFlow] The explanation of average gradients by example in data parallelism
When studying some examples of training model using Multi-GPUs ( in data parallelism ), the average gradients function always exists in some kind of ways, and here is a simple version as follows:
Thursday, November 8, 2018
[Dynamic Control Flow] Whitepaper: Implementation of Control Flow in TensorFlow
In the following whitepaper, we can understand more dynamic control flow in details.
Whitepaper: Implementation of Control Flow in TensorFlow
http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
Whitepaper: Implementation of Control Flow in TensorFlow
http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
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