Why I Switch From Keras to PyTorch
Image edited by Author for the icon taken from the official site of PyTorch and KerasThe war between Deep Learning Frameworks is still on fire, which one that will gain more masses, it will be the next game-changer for the deep learning community in future. The loser one will fade in if they can’t survive by giving the best solution for the deep learning community and the world. The first framewor
Kerasで複数GPUで学習するときに中間結果モデル保存が失敗する問題の対応 - Qiita
背景 Keras (Tensorflow backend)で一台のパソコンに載っている複数GPUでトレーニング処理を回すとき、中間結果(チェックポイント)のモデルを、keras.callbacks.ModelCheckpoint関数をコールバック関数に設定して定期保存しようとしたとき、以下のエラーが発生。 Epoch 01000: saving model to ./log_Stacked_Hourglass_Network_v1_320x240_fp16_3rd_multiGPU/weights.1000-0.0039607391-0.0131516113.hdf5 Traceback (most recent call last): File "train_customized_SHN_resume_baobab_fp16_1stack_multiGPU.py", line 304,
kerasでmultiple (複数の) 入力 / 出力 / 損失関数を扱う時のTipsをまとめる - St_Hakky’s blog
こんにちは。 〇この記事のモチベーション Deep Learningで自分でモデルとかを作ろうとすると、複数の入力や出力、そして損失関数を取扱たくなる時期が必ず来ると思います。 最近では、GoogleNetとかは中間層の途中で出力を出していたりするので、そういうのでも普通に遭遇します。 というわけで私も例に漏れず遭遇しました笑。 今回はkerasで複数の入力や出力、そして損失関数を取り扱うときにどうすればいいかについて実践したのでまとめておきます。 〇「複数の入力」を与えたい場合 これは簡単です。普段Modelのインスタンスを作る際に、inputsとoutputsを指定すると思いますが、その際に複数ある場合はリスト形式で渡せばいいだけです。 input_layer1 = Input(shape=(32,)) input_layer2 = Input(shape=(64,)) # ...(モ
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--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-76-52ca495a8e09> in <module>() ----> 1 model, layer_dict, classifier_sizes = load_model('./model_0.h5') /Users/pierluigiferrari/anaconda/envs/carnd-term1/lib/python3.5/site-packages/keras/models.py in load_model(filepath, custom_objects) 258 metrics=metrics, 259 l
Deploying Keras Deep Learning Models with Flask
The examples covered in this post will serve as a template/starting point for building your own deep learning APIs … Some of the issues that I’ll cover include handling a custom metric when using model persistence with Keras, dealing with multi-threading concerns when using Keras in combination with Flask, and getting it all running on an EC2 instance. The complete code listing for this post is av
Kerasで複数のGPUを使い、学習を高速化してみた - WonderPlanet Developers’ Blog
こんにちは。アドバンストテクノロジー部のR&Dチーム所属岩原です。 今回はKerasで複数のGPUを使う方法を書きたいと思います。 Keras 2.0.9から簡単に複数GPUを使用した高速化が可能に。 Keras2.0.9からtraining_utilsというモジュールにmulti_gpu_modelという関数が追加されました。 コレを使うと、学習を複数のGPUで行わせることが可能になります。 inputをGPUの数だけ分割することによって、並列化を実現しているようです。 keras/training_utils.py at master · keras-team/keras では、実際に試してみましょう。 環境 AWS EC2(p2.8xlarge) -> GPU8本 Deep Learning Base AMI (Ubuntu) Version 2.0 (ami-041db87c) -
GitHub - matterport/Mask_RCNN: Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone. The repository includes: Source code of Mask R-CNN built on FPN and ResNet101. Training code for MS COCO Pre-trained weights for MS COCO Jupyter notebo
Can't load `Sequential` with custom layers · Issue #5956 · keras-team/keras · GitHub
Now Sequeltial.from_config doesn't have cutsom_objects argument. Sequential with custom layers can create config, but can't restore from it. Is it a specification? And if I save/load such model, Sequential.from_config is called internally here. custom_objects is ignored and raises ValueError: Unknown layer. I think it should check custom_objects is None if from_config doesn't have custom_objects a
[Keras] A thing you should know about Keras if you plan to train a deep learning model on a large…
Keras is to TensorFlow/Theano/CNTK what Scikit-Learn is to NumPy. [Keras] A thing you should know about Keras if you plan to train a deep learning model on a large dataset TLDR; Understanding this is important if you plan to migrate your keras experiments from toy datasets to large ones: The output of predict_generator() and predict() may not match if you are using a data generator created with fl
![[Keras] A thing you should know about Keras if you plan to train a deep learning model on a large…](https://cdn-ak-scissors.b.st-hatena.com/image/square/dd4cee384f86551e03e7bb33539b85a116f5e6de/height=288;version=1;width=512/https%3A%2F%2Fmiro.medium.com%2Fmax%2F652%2F1%2AqllYdaKrZLMhK-nK1zUVEQ.png)
A scalable Keras + deep learning REST API - PyImageSearch
In today’s blog post we are going to create a deep learning REST API that wraps a Keras model in an efficient, scalable manner. Our Keras + deep learning REST API will be capable of batch processing images, scaling to multiple machines (including multiple web servers and Redis instances), and round-robin scheduling when placed behind a load balancer. To accomplish this we will be using: Keras Redi
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Scikit-learn API - Keras Documentation
[BUG] Save Model with multi_gpu · Issue #8123 · keras-team/keras
Loading saved model fails with ValueError You are trying to load a weight file containing 1 layers into a model with 0 layers · Issue #10417 · keras-team/keras
GitHub - nerox8664/pytorch2keras: Pytorch to Keras model convertor
GitHub - ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018: Code for 15th place in Kaggle Google AI Open Images - Object Detection Track
GitHub - keunwoochoi/keras_callbacks_example: Keras callback example
How to use ModelCheckpoint with custom metrics in Keras?
Create keras callback to save model predictions and targets for each batch during training
GitHub - eriklindernoren/Keras-GAN: Keras implementations of Generative Adversarial Networks.
GitHub - kevinlin311tw/keras-openpose-reproduce: Keras implementation of Realtime Multi-Person Pose Estimation
