tensorflow knn mnist

 

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

# MNIST Digit Prediction with k-Nearest Neighbors #----------------------------------------------- # # This script will load the MNIST data, and split # it into test/train and perform prediction with # nearest neighbors # # For each test integer, we will return the # closest image/integer. # # Integer images are represented as 28x8 matrices # of floating point numbers   import random import numpy as np import tensorflow as tf import matplotlib.pyplot as plt from PIL import Image from tensorflow.examples.tutorials.mnist import input_data from tensorflow.python.framework import ops ops.reset_default_graph()   # Create graph sess = tf.Session()   # Load the data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)   # Random sample np.random.seed(13)  # set seed for reproducibility train_size = 1000 test_size = 102 rand_train_indices = np.random.choice(len(mnist.train.images), train_size, replace=False) rand_test_indices = np.random.choice(len(mnist.test.images), test_size, replace=False) x_vals_train = mnist.train.images[rand_train_indices] x_vals_test = mnist.test.images[rand_test_indices] y_vals_train = mnist.train.labels[rand_train_indices] y_vals_test = mnist.test.labels[rand_test_indices]   # Declare k-value and batch size k = 4 batch_size=6   # Placeholders x_data_train = tf.placeholder(shape=[None, 784], dtype=tf.float32) x_data_test = tf.placeholder(shape=[None, 784], dtype=tf.float32) y_target_train = tf.placeholder(shape=[None, 10], dtype=tf.float32) y_target_test = tf.placeholder(shape=[None, 10], dtype=tf.float32)   # Declare distance metric # L1 distance = tf.reduce_sum(tf.abs(tf.subtract(x_data_train, tf.expand_dims(x_data_test,1))), axis=2)   # L2 #distance = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(x_data_train, tf.expand_dims(x_data_test,1))), reduction_indices=1))   # Predict: Get min distance index (Nearest neighbor) top_k_xvals, top_k_indices = tf.nn.top_k(tf.negative(distance), k=k) prediction_indices = tf.gather(y_target_train, top_k_indices) # Predict the mode category count_of_predictions = tf.reduce_sum(prediction_indices, axis=1) prediction = tf.argmax(count_of_predictions, axis=1)   # Calculate how many loops over training data num_loops = int(np.ceil(len(x_vals_test)/batch_size))   test_output = [] actual_vals = [] for i in range(num_loops):     min_index = i*batch_size     max_index = min((i+1)*batch_size,len(x_vals_train))     x_batch = x_vals_test[min_index:max_index]     y_batch = y_vals_test[min_index:max_index]     predictions = sess.run(prediction, feed_dict={x_data_train: x_vals_train, x_data_test: x_batch,                                          y_target_train: y_vals_train, y_target_test: y_batch})     test_output.extend(predictions)     actual_vals.extend(np.argmax(y_batch, axis=1))   accuracy = sum([1./test_size for i in range(test_size) if test_output[i]==actual_vals[i]]) print('Accuracy on test set: ' + str(accuracy))   # Plot the last batch results: actuals = np.argmax(y_batch, axis=1)   Nrows = 2 Ncols = 3 for i in range(len(actuals)):     plt.subplot(Nrows, Ncols, i+1)     plt.imshow(np.reshape(x_batch[i], [28,28]), cmap='Greys_r')     plt.title('Actual: ' + str(actuals[i]) + ' Pred: ' + str(predictions[i]),                                fontsize=10)     frame = plt.gca()     frame.axes.get_xaxis().set_visible(False)     frame.axes.get_yaxis().set_visible(False)       plt.show()

 效果：