
#In this exercise you'll try to build a neural network that predicts the price of a house according to a simple formula.
#So, imagine if house pricing was as easy as a house costs 50k + 50k per bedroom, so that a 1 bedroom house costs 100k, a 2 bedroom house costs 150k etc.
#How would you create a neural network that learns this relationship so that it would predict a 7 bedroom house as costing close to 400k etc.
#Hint: Your network might work better if you scale the house price down. You don't have to give the answer 400...it might be better to create something
# that predicts the number 4, and then your answer is in the 'hundreds of thousands' etc.

# 7*50 + 50 = 350 + 50 = 400/100
# y = x + 1  [1, 2, 3, 4]
# [1, 1.5, 2, 2.5]

import tensorflow as tf
import numpy as np
from tensorflow import keras

# GRADED FUNCTION: house_model

def house_model(y_new):
    xs = np.array([1.0, 2.0, 3.0, 4.0], dtype=float)
    ys = np.array([1.0, 1.5, 2.0, 2.5], dtype=float)
    model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape =[1] )])  #Define a neural network
    model.compile(optimizer= 'sgd', loss='mean_squared_error')
    model.fit(xs , ys, epochs=500)

    return model.predict(y_new)[0]

prediction = house_model([7.0])
print(prediction)