# Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

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## Exercise_1_House_Prices_Question

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.

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

def house_model(y_new):
xs = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,  ], dtype=float)
ys = np.array([1, 1.5, 2., 2.5, 3.0, 3.5, 4.0, 4.5, ], dtype=float)
model = keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])
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)
``````
``````WARNING: Logging before flag parsing goes to stderr.
W1128 07:32:25.905068 140150206101312 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
Epoch 1/500
8/8 [==============================] - 2s 274ms/sample - loss: 57.6285
Epoch 2/500
8/8 [==============================] - 0s 201us/sample - loss: 12.9565
Epoch 3/500
8/8 [==============================] - 0s 175us/sample - loss: 2.9200
Epoch 4/500
8/8 [==============================] - 0s 172us/sample - loss: 0.6651
...
...
...
Epoch 497/500
8/8 [==============================] - 0s 160us/sample - loss: 2.2577e-04
Epoch 498/500
8/8 [==============================] - 0s 163us/sample - loss: 2.2397e-04
Epoch 499/500
8/8 [==============================] - 0s 10ms/sample - loss: 2.2218e-04
Epoch 500/500
8/8 [==============================] - 0s 206us/sample - loss: 2.2041e-04
[4.0079846]
``````

Updated: