nearest neighbor classifier

nearest_neighbor.py

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+'''
+Nearest Neighbor classification on MNIST with TensorFlow
+'''
+import numpy as np
+import tensorflow as tf
+
+# Import MINST data
+import input_data
+mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
+
+#In this example, we limit mnist data
+Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)
+Xte, Yte = mnist.test.next_batch(200) #200 for testing
+
+Xtr = np.reshape(Xtr, newshape=(-1, 28*28))
+Xte = np.reshape(Xte, newshape=(-1, 28*28))
+
+xtr = tf.placeholder("float", [None, 784])
+xte = tf.placeholder("float", [784])
+
+nn = tf.Variable(tf.zeros([10]))
+
+#Calculation of L1 Distance
+distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.neg(xte))), reduction_indices=1)
+#Predict: Get min distance index (Nearest neighbor)
+pred = tf.arg_min(distance, 0)
+
+accuracy = 0.
+init = tf.initialize_all_variables()
+with tf.Session() as sess:
+    sess.run(init)
+    for i in range(len(Xte)):
+        nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i,:]})
+        #Get nn class label and compare it to its true label
+        print "Test", i, "Prediction:", np.argmax(Ytr[nn_index]), "True Class:", np.argmax(Yte[i])
+        if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):
+            accuracy += 1./len(Xte)
+    print "Done!"
+    print "Accuracy:", accuracy
+