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Regression
2. Now, we define the feature columns we will be using to train the regressor.
Our dataset, as we mentioned, consists of two features "area" a numeric
value signifying the area of the house and "type" telling if it is a "bungalow"
or "apartment":
featcols = [
tf.feature_column.numeric_column("area"),
tf.feature_column.categorical_column_with_vocabulary_list("type",[
"bungalow","apartment"])
]
3. In the next step, we define an input function to provide input for training.
The function returns a tuple containing features and labels:
def train_input_fn():
features = {"area":[1000,2000,4000,1000,2000,4000],
"type":["bungalow","bungalow","house",
"apartment","apartment","apartment"]}
labels = [ 500 , 1000 , 1500 , 700 , 1300 , 1900 ]
return features, labels
4. Next, we use the premade LinearRegressor estimator and fit it on the
training dataset:
model = tf.estimator.LinearRegressor(featcols)
model.train(train_input_fn, steps=200)
5. Now that the estimator is trained, let us see the result of the prediction:
def predict_input_fn():
features = {"area":[1500,1800],
"type":["house","apt"]}
return features
predictions = model.predict(predict_input_fn)
print(next(predictions))
print(next(predictions))
--------------------------------------------------
6. The result:
{'predictions': array([692.7829], dtype=float32)}
{'predictions': array([830.9035], dtype=float32)}
[ 96 ]