tf-estimator-test.py

import numpy as np
import tensorflow as tf

#define features
feature_columns = [tf.feature_column.numeric_column("x", shape=[1])]

#set up estimator
estimator = tf.estimator.LinearRegressor(feature_columns=feature_columns)

#read and set up data sets
#training set
x_train = np.array([1.,2.,3.,4.])
y_train = np.array([0.,-1.,-2.,-3.])

#evaluation set
x_eval = np.array([2.,5.,8.,1.])
y_eval = np.array([-1.01,-4.1,-7,0.])

input_fn = tf.estimator.inputs.numpy_input_fn({"x": x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
train_input_fn = tf.estimator.inputs.numpy_input_fn({"x": x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
eval_input_fn = tf.estimator.inputs.numpy_input_fn({"x": x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)

#train
estimator.train(input_fn=input_fn, steps=1000)

#evaluate
train_metrics = estimator.evaluate(input_fn = train_input_fn)
eval_metrics = estimator.evaluate(input_fn = eval_input_fn)
print("train metrics: %r"% train_metrics)
print("eval metrics: %r"% eval_metrics)