demo44.py

import numpy as np
from keras import models
from keras import layers
from keras.datasets import boston_housing

(train_data, train_target), (test_data, test_target) = boston_housing.load_data()
print(train_data.shape, test_data.shape)

mean = train_data.mean(axis=0)
train_data -= mean
std = train_data.std(axis=0)
train_data /= std
test_data -= mean
test_data /= std
print(train_data.shape, test_data.shape)


def build_model():
    model = models.Sequential()
    model.add(layers.Dense(64, activation='relu', input_shape=(train_data.shape[1],)))
    model.add(layers.Dense(32, activation='relu'))
    model.add(layers.Dense(1))
    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
    model.summary()
    return model


model = build_model()
model.fit(train_data, train_target, validation_split=0.1, epochs=100, batch_size=10, verbose=1)

# for item in test_target:
#     print(item)

for (i, j) in zip(test_data, test_target):
    predict = model.predict(i.reshape(1, -1))
    print(f'actual={j}, predict as={predict}')