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# Import TensorFlow.
import tensorflow as tf
import numpy
# Train data set
size_data = numpy.asarray([ 2104, 1600, 2400, 1416, 3000, 1985, 1534, 1427,
1380, 1494, 1940, 2000, 1890, 4478, 1268, 2300,
1320, 1236, 2609, 3031, 1767, 1888, 1604, 1962,
3890, 1100, 1458, 2526, 2200, 2637, 1839, 1000,
2040, 3137, 1811, 1437, 1239, 2132, 4215, 2162,
1664, 2238, 2567, 1200, 852, 1852, 1203 ])
price_data = numpy.asarray([ 399900, 329900, 369000, 232000, 539900, 299900, 314900, 198999,
212000, 242500, 239999, 347000, 329999, 699900, 259900, 449900,
299900, 199900, 499998, 599000, 252900, 255000, 242900, 259900,
573900, 249900, 464500, 469000, 475000, 299900, 349900, 169900,
314900, 579900, 285900, 249900, 229900, 345000, 549000, 287000,
368500, 329900, 314000, 299000, 179900, 299900, 239500 ])
# Test data set
size_data_test = numpy.asarray([ 1600, 1494, 1236, 1100, 3137, 2238 ])
price_data_test = numpy.asarray([ 329900, 242500, 199900, 249900, 579900, 329900 ])
def normalize(array):
return (array - array.mean()) / array.std()
# Normalize train and test data sets
size_data_n = normalize(size_data)
price_data_n = normalize(price_data)
size_data_test_n = normalize(size_data_test)
price_data_test_n = normalize(price_data_test)
# TF graph input
X = tf.placeholder(tf.float32,name="X")
Y = tf.placeholder(tf.float32,name="Y")
# Create a model
# Set model weights
w = tf.Variable(numpy.random.randn(), name="weight")
b = tf.Variable(numpy.random.randn(), name="bias")
# Set parameters
learning_rate = 0.1
training_iteration = 200
# Construct a linear model
model = tf.add(tf.multiply(X, w), b) #y=mx+b
# Minimize squared errors
samples_number = price_data_n.size
cost_function = tf.reduce_sum(tf.pow(model - Y, 2))/(2 * samples_number) #L2 loss
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function) #Gradient descent
# Launch a graph
with tf.Session() as sess:
tf.global_variables_initializer().run() # initialize all variables defined above. See: https://stackoverflow.com/a/45049429/1625820
# Fit all training data
display_step = 20
for iteration in range(training_iteration):
for (x, y) in zip(size_data_n, price_data_n):
sess.run(optimizer, feed_dict={X: x, Y: y})
# Display progress every display_step
if iteration % display_step == 0:
print("Iteration:", '%04d' % (iteration), "cost=", "{:.9f}".format(sess.run(cost_function, feed_dict={X:size_data_n, Y:price_data_n})),\
"W=", sess.run(w), "b=", sess.run(b))
training_cost = sess.run(cost_function, feed_dict={X: normalize(size_data_n), Y: normalize(price_data_n)})
print("Training completed:", "cost=", "{:.9f}".format(training_cost), "W=", sess.run(w), "b=", sess.run(b))
testing_cost = sess.run(cost_function, feed_dict={X: size_data_test_n, Y: price_data_test_n})
print("Testing data cost:" , testing_cost)
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