[ML] MNIST

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MNIST

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epoch : 전체 dataset을 한번 돈 것을 1 epoch이라고 한다.

batch size : dataset이 큰 경우, 한 번에 할 수 없기 때문에 짤라서 학습을 시키는데 이 때 자르는 사이즈를 batch size라고 한다.

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소스 코드

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import tensorflow as tf
import random
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
 
nb_classes = 10
 
# MNIST data image of shape 28 * 28 = 784
X = tf.placeholder(tf.float32, [None, 784])
# 0 ~ 9 digits recognition = 10 classes
Y = tf.placeholder(tf.float32, [None, nb_classes])
 
W = tf.Variable(tf.random_normal([784, nb_classes]))
b = tf.Variable(tf.random_normal([nb_classes]))
 
hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
 
# Test model
is_correct = tf.equal(tf.arg_max(hypothesis, 1), tf.arg_max(Y, 1))
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
 
# parameters
training_epochs = 15
batch_size = 100
 
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(training_epochs):
        avg_cost = 0
        total_batch = int(mnist.train.num_examples / batch_size)
 
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            c, _ =  sess.run([cost, optimizer], feed_dict={X: batch_xs, Y: batch_ys})
            avg_cost += c / total_batch
 
        print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
 
    print("Learning finished")
 
# Test the model using test sets
    print("Accuracy: ", accuracy.eval(session=sess, feed_dict={
          X: mnist.test.images, Y: mnist.test.labels}))
 
    # Get one and predict
    r = random.randint(0, mnist.test.num_examples - 1)
    print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
    print("Prediction: ", sess.run(
        tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1]}))
 
    plt.imshow(
        mnist.test.images[r:r + 1].reshape(28, 28),
        cmap='Greys',
        interpolation='nearest')
    plt.show()

sess.run 와 accuracy.eval는 같다.

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실행 결과

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