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KERAS 3.0 RELEASED A superpower for ML developers Keras is a deep learningAPI designed for human beings, notmachines. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability. When you choose Keras, your codebase is smaller, more readable, easier toiterate on. inputs = keras.Input(shape=(32, 32, 3)) x = layers.Conv2D(32, 3, activation="relu")(inputs) x =
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General Concepts¶ matplotlib has an extensive codebase that can be daunting to many new users. However, most of matplotlib can be understood with a fairlysimple conceptual framework and knowledge of a few important points. Plotting requires action on a range of levels, from the most general (e.g., ‘contour this 2-D array’) to the most specific (e.g., ‘color this screen pixel red’). The purpose of
Keras:Pythonの深層学習ライブラリ Kerasとは Kerasは，Pythonで書かれた，TensorFlowまたはCNTK，Theano上で実行可能な高水準のニューラルネットワークライブラリです． Kerasは，迅速な実験を可能にすることに重点を置いて開発されました． アイデアから結果に到達するまでのリードタイムをできるだけ小さくすることが，良い研究をするための鍵になります． 次のような場合で深層学習ライブラリが必要なら，Kerasを使用してください: 容易に素早くプロトタイプの作成が可能（ユーザーフレンドリー，モジュール性，および拡張性による）CNNとRNNの両方，およびこれらの2つの組み合わせをサポートCPUとGPU上でシームレスな動作 Keras.ioのドキュメントを読んでください． KerasはPython 2.7-3.6に対応しています． ガイドライン ユーザー
Chainer – A flexible framework of neuralnetworks¶ Chainer is a powerful, flexible and intuitive deep learning framework. Chainer supports CUDA computation.It only requires a fewlines of code to leverage aGPU.It also runs on multipleGPUs with little effort. Chainer supports variousnetwork architectures including feed-forwardnets, convnets, recurrentnets and recursivenets.It also supports
Functions¶ Chainer provides variety of built-in function implementations in chainer.functions package. These functions usually return a Variable object or a tuple of multiple Variable objects. For a Variable argument of a function, an N-dimensional array can be passed if you do not needits gradient. Some functions additionally supportsscalar arguments.Note Functions implemented in Chainer consi
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