使用 Estimators API 訓練模型

Python 的 TensorFlow 入門

Isaiah Hull

Visiting Associate Professor of Finance, BI Norwegian Business School

什麼是 Estimators API？

高階子模組
彈性較低
強制最佳實務
部署更快
多種預製模型

此圖示意 Estimators API 與較低階 API 的關係。

¹ Image taken from https://www.tensorflow.org/guide/premade_estimators

模型定義與訓練

定義特徵欄
載入並轉換資料
定義 estimator
執行訓練

定義特徵欄

# Import tensorflow under its standard alias
import tensorflow as tf

# Define a numeric feature column
size = tf.feature_column.numeric_column("size")

# Define a categorical feature column
rooms = tf.feature_column.categorical_column_with_vocabulary_list("rooms",\
["1", "2", "3", "4", "5"])

定義特徵欄

# Create feature column list
features_list = [size, rooms]

# Define a matrix feature column
features_list = [tf.feature_column.numeric_column('image', shape=(784,))]

載入與轉換資料

# Define input data function
def input_fn():
    # Define feature dictionary
    features = {"size": [1340, 1690, 2720], "rooms": [1, 3, 4]}
    # Define labels
    labels = [221900, 538000, 180000]
    return features, labels

定義並訓練回歸 estimator

# Define a deep neural network regression
model0 = tf.estimator.DNNRegressor(feature_columns=feature_list,\
    hidden_units=[10, 6, 6, 3])

# Train the regression model
model0.train(input_fn, steps=20)

定義並訓練深度神經網路

# Define a deep neural network classifier
model1 = tf.estimator.DNNClassifier(feature_columns=feature_list,\ 
   hidden_units=[32, 16, 8], n_classes=4)

# Train the classifier
model1.train(input_fn, steps=20)

https://www.tensorflow.org/guide/estimators

一起來練習吧！

Python 的 TensorFlow 入門