### NOTE: This uses tensorflow estimator API import numpy as np # Define feature columns col_1 = tf.feature_column.numeric_column("col_1") col_2 = tf.feature_column.numeric_column("col_2") # Define the list of feature columns feature_list = [col_1, col_2] # Map the features and labels of input data def input_fn(): features = {'col_1':np.array(df['col_1']), 'col_2':np.array(df['col_2'])} labels = np.array(df["target"]) return features, labels # Deep Learning Regression with tensorflow model = tf.estimator.DNNRegressor(feature_columns=feature_list, hidden_units=[2,2]) model.train(input_fn, steps=1) # Linear Regression with tensorflow model = tf.estimator.LinearRegressor(feature_columns=feature_list) model.train(input_fn, steps=2) # Deep Learning Classification with tensorflow model1 = tf.estimator.DNNClassifier(feature_columns=feature_list, hidden_units=[32, 16, 8], n_classes=4) model.train(input_fn, steps=20)