Created
December 19, 2017 21:17
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print("Optimization Finished!") | |
training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y}) | |
print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n') | |
# Graphic display | |
plt.plot(train_X, train_Y, 'ro', label='Original data') | |
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line') | |
plt.legend() | |
plt.show() | |
# Testing example, as requested (Issue #2) | |
test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1]) | |
test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03]) | |
print("Testing... (Mean square loss Comparison)") | |
testing_cost = sess.run( | |
tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]), | |
feed_dict={X: test_X, Y: test_Y}) # same function as cost above | |
print("Testing cost=", testing_cost) | |
print("Absolute mean square loss difference:", abs( | |
training_cost - testing_cost)) | |
plt.plot(test_X, test_Y, 'bo', label='Testing data') | |
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line') | |
plt.legend() | |
plt.show() |
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