Tensorflow – nibuiroフラグメント β

Tag Tensorflow

Tensorflow 1.0をかじる

By inArtificial Intelligence Tag , ,

プリセットを利用する

tf.estimator

　抽象度の高いAPI。 高位の機械学習APIともいう。 データの入力には下記のfeature-columnsを用いる。
　このクラスは以下を内包している。

  • BaselineClassifier: A classifier that can establish a simple baseline.
  • BaselineRegressor: A regressor that can establish a simple baseline.
  • BestExporter: This class exports the serving graph and checkpoints of the best models.
  • BoostedTreesClassifier: A Classifier for Tensorflow Boosted Trees models.
  • BoostedTreesRegressor: A Regressor for Tensorflow Boosted Trees models.
  • DNNClassifier: A classifier for TensorFlow DNN models.
  • DNNLinearCombinedClassifier: An estimator for TensorFlow Linear and DNN joined classification models
  • DNNLinearCombinedRegressor: An estimator for TensorFlow Linear and DNN joined models for regression.
  • DNNRegressor: A regressor for TensorFlow DNN models.
  • Estimator: Estimator class to train and evaluate TensorFlow models.
  • EstimatorSpec: Ops and objects returned from a model_fn and passed to an Estimator.
  • EvalSpec: Configuration for the “eval” part for the train_and_evaluate call.
  • Exporter: A class representing a type of model export.
  • FinalExporter: This class exports the serving graph and checkpoints in the end.
  • LatestExporter: This class regularly exports the serving graph and checkpoints.
  • LinearClassifier: Linear classifier model.
  • LinearRegressor: An estimator for TensorFlow Linear regression problems.
  • ModeKeys: Standard names for model modes.
  • RunConfig: This class specifies the configurations for an Estimator run.
  • TrainSpec: Configuration for the “train” part for the train_and_evaluate call.
  • VocabInfo: Vocabulary information for warm-starting.
  • WarmStartSettings: Settings for warm-starting in Estimators.

tf.feature-columns

　estimatorへ入力する特徴列（データ）の定義。特徴列の型、サイズ、構造を定義し、生成する。

オリジナルモデルを構築する

tf.Variable

　重みやバイアスといった学習対象の変数を格納する。

  • Weight (W)
  • Bias (B)

tf.placeholder

　学習に必要なデータを供給する。テストデータの入力の際はnumpy行列をそのまま渡す。

  • train_x
  • train_Y

　
参考:
・https://towardsdatascience.com/deploy-tensorflow-models-9813b5a705d5
・https://www.kaggle.com/liampetti/deep-neural-network-using-tensorflow
・https://www.kaggle.com/fuzzyfroghunter/getting-started-with-tensorflow
・https://stackoverflow.com/questions/41116782/tensorflow-how-to-get-prediction
・https://stackoverflow.com/questions/36693740/whats-the-difference-between-tf-placeholder-and-tf-variable