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Chapter 3
6. Next we instantiate a LinearRegressor estimator; we train it using training
data using train_input_fn, and find the result for the validation dataset by
evaluating the trained model using val_input_fn:
linear_est = tf.estimator.LinearRegressor(feature_columns=feature_
columns)
linear_est.train(train_input_fn, steps=100)
result = linear_est.evaluate(val_input_fn)
7. Let's make a prediction on it:
result = linear_est.predict(val_input_fn)
for pred,exp in zip(result, y_test[:32]):
print("Predicted Value: ", pred['predictions'][0], "Expected:
", exp)
Figure 1: Generating predicted values using the LinearRegressor estimator
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