tensorFlow sample, for IoT data

gistfile1.txt

import tensorflow as tf

if __name__ == "__main__":
	# Model parameters
	W = tf.Variable([0.0], dtype=tf.float32)
	b = tf.Variable([0.0], dtype=tf.float32)
	# Model input and output
	x = tf.placeholder(tf.float32)
	y = tf.placeholder(tf.float32)
	linear_model = W*x + b

	# loss
	loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
	# optimizer
	optimizer = tf.train.GradientDescentOptimizer(0.01)
	train = optimizer.minimize(loss)

	# training data
	x_train = [0.01, 0.02 , 0.03, 0.04, 0.05 ]
	y_train = [0.13, 0.12 , 0.06, 0.11, 0.13 ]

	# training loop
	init = tf.global_variables_initializer()
	sess = tf.Session()
	sess.run(init) # reset values to wrong
	for i in range(1000):
		sess.run(train, {x: x_train, y: y_train})
		if i % 100 == 0:
			print( i, sess.run(W), sess.run(b) )

	# evaluate training accuracy
	curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train})
	print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))