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""' A nearest neighbor learning algorithm example using TensorFlow library. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ Make sure to install the mnist-learn gem !! '"" require "bundler/setup" require "tensor_stream" require "mnist-learn" tf = TensorStream # Import MNIST data mnist = Mnist.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 # tf Graph Input xtr = tf.placeholder(:float, shape: [nil, 784]) xte = tf.placeholder(:float, shape: [784]) # Nearest Neighbor calculation using L1 Distance # Calculate L1 Distance distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), 1) # Prediction: Get min distance index (Nearest neighbor) pred = tf.argmin(distance, 0) accuracy = 0.0 # Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer # Start training tf.session do |sess| # Run the initializer sess.run(init) Xte.size.times do |i| # Get nearest neighbor nn_index = sess.run(pred, feed_dict: {xtr => Xtr, xte => Xte[i]}) print("Test ", i, "Prediction: ", Ytr[nn_index].max, \ "True Class: ", Yte[i].max, "\n") if Ytr[nn_index].max == Yte[i].max accuracy += 1.0 / Xte.size end end print("Done!") print("Accuracy:", accuracy) end
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