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Find the circle cross three points using tensor flow
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| import tensorflow as tf | |
| import numpy | |
| # Parameters | |
| learning_rate = 0.1 | |
| training_epochs = 3000 | |
| display_step = 50 | |
| # Training Data, 3 points that form a triangel | |
| train_X = numpy.asarray([3.0,6.0,9.0]) | |
| train_Y = numpy.asarray([7.0,9.0,7.0]) | |
| # tf Graph Input | |
| X = tf.placeholder("float") | |
| Y = tf.placeholder("float") | |
| # Set vaibale for center | |
| cx = tf.Variable(3, name="cx",dtype=tf.float32) | |
| cy = tf.Variable(3, name="cy",dtype=tf.float32) | |
| # Caculate the distance to the center and make them as equal as possible | |
| distance = tf.pow(tf.add(tf.pow((X-cx),2),tf.pow((Y-cy),2)),0.5) | |
| mean = tf.reduce_mean(distance) | |
| cost = tf.reduce_sum(tf.pow((distance-mean),2)/3) | |
| # Gradient descent | |
| optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) | |
| # Initialize the variables (i.e. assign their default value) | |
| init = tf.global_variables_initializer() | |
| # Start training | |
| with tf.Session() as sess: | |
| sess.run(init) | |
| # Fit all training data | |
| for epoch in range(training_epochs): | |
| sess.run(optimizer, feed_dict={X: train_X, Y: train_Y}) | |
| c = sess.run(cost, feed_dict={X: train_X, Y:train_Y}) | |
| if (c - 0) < 0.0000000001: | |
| break | |
| #Display logs per epoch step | |
| if (epoch+1) % display_step == 0: | |
| c = sess.run(cost, feed_dict={X: train_X, Y:train_Y}) | |
| m = sess.run(mean, feed_dict={X: train_X, Y:train_Y}) | |
| print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \ | |
| "CX=", sess.run(cx), "CY=", sess.run(cy), "Mean=", "{:.9f}".format(m) | |
| print "Optimization Finished!" | |
| training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y}) | |
| print "Training cost=", training_cost, "CX=", round(sess.run(cx),2), "CY=", round(sess.run(cy),2), "R=", round(m,2), '\n' |
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