keras 5 predict if housing price is above median price 2

 

predict house value from unknown dataset with the trained model. (above average = 1, below average = 0)

#house.py

#train on GPU

pysical_devices = tf.config.experimental.list_physical_devices('GPU')

#print("Num GPUs Available: ", len(pysical_devices))

tf.config.experimental.set_memory_growth(pysical_devices[0], True)

#load model

model = load_model('models/house.h5')

#create test samples that are different from data samples

test_samples = [

    [10000, 6, 5, 0, 1, 0, 2, 5, 0, 500],

    [12000, 7, 5, 1, 1, 1, 3, 7, 0, 600],

    [11000, 7, 5, 0, 1, 1, 3, 6, 0, 550],

    [11000, 6, 5, 0, 1, 1, 3, 6, 0, 550],

]

test_samples = np.array(test_samples)

scaler = MinMaxScaler(feature_range=(0, 1))

scaled_test_samples = scaler.fit_transform(test_samples)

print(scaled_test_samples)

#prediction

predictions = model.predict(x=scaled_test_samples, batch_size=32, verbose=1)

#predicted output index

rounded_predictions = np.argmax(predictions, axis=-1)

print(rounded_predictions)

-------------------------------------

#logs

#scaled input

[[0.  0.  0.  0.  0.  0.  0.  0.  0.  0. ]

 [1.  1.  0.  1.  0.  1.  1.  1.  0.  1. ]

 [0.5 1.  0.  0.  0.  1.  1.  0.5 0.  0.5]

 [0.5 0.  0.  0.  0.  1.  1.  0.5 0.  0.5]]

#output

[0 1 1 0]

reference:

https://www.digitalocean.com/community/tutorials/how-to-build-a-deep-learning-model-to-predict-employee-retention-using-keras-and-tensorflow

http://chuanshuoge2.blogspot.com/2020/12/keras-4-predict-if-housing-price-is_31.html