house_prices_estimation_example_data.py

# -*- coding: utf-8 -*-
import set_python_path
from tensorflow_core.python.keras.engine.input_layer import Input
from tensorflow_core.python.keras.layers.core import Dense, Dropout
from tensorflow_core.python.keras.models import Sequential, Model
from tensorflow_core.python.keras.optimizer_v2.adam import Adam

try:
    from datasets import load_house_dataset_data
    from train_and_evaluation import evaluate_regression_model, train_model
except:
    from pilula_deep_learning.datasets import load_house_dataset_data
    from pilula_deep_learning.train_and_evaluation import evaluate_regression_model, train_model
import matplotlib.pyplot as plt

__author__ = 106360

def generate_simple_regression_model(input_shape,weights='',remove_head=False):
    # define the model input
    inputs = Input(shape=(input_shape))
    # loop over the number of filters

    x = Dense(16, input_dim=input_shape, activation="relu", name='layer1')(inputs)
    x = Dense(6, activation="relu",name='layer2')(x)

    if not remove_head:
        x= Dense(1, activation="sigmoid",name='layer3')(x)

    model = Model(inputs,x)
    if weights!='':
        model.load_weights(weights,by_name=True)


    return model


if __name__ == "__main__":
    (trainX,trainX_img, trainY, testX,testX_img,testY), normalizer = load_house_dataset_data(test_size=0.2,random_state=666)
    input_shape = trainX.shape[1]
    model = generate_simple_regression_model(input_shape)

    opt = Adam(lr=1e-3, decay=1e-3 / 200)
    model.compile(loss='mean_squared_error',metrics=['mean_absolute_percentage_error','mean_absolute_error','mean_squared_error'], optimizer=opt)
    model.summary()

    model = train_model(trainX, trainY, testX, testY,model,show_plot=True,epochs=500,batch_size=32)
    evaluate_regression_model(model,testX,testY,normalizer,show_plot=True)
    model.save('regression_model_data.h5')