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May 18, 2019 17:27
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# https://github.com/golbin/TensorFlow-Tutorials/blob/master/06%20-%20MNIST/01%20-%20MNIST.py | |
import tensorflow as tf | |
from tensorflow.examples.tutorials.mnist import input_data | |
mnist = input_data.read_data_sets("./mnist/data/", one_hot=True) | |
X = tf.placeholder(tf.float32, [None, 784]) | |
Y = tf.placeholder(tf.float32, [None, 10]) | |
W1 = tf.Variable(tf.random_normal([784, 256], stddev=0.01)) | |
L1 = tf.nn.relu(tf.matmul(X, W1)) | |
W2 = tf.Variable(tf.random_normal([256, 256], stddev=0.01)) | |
L2 = tf.nn.relu(tf.matmul(L1, W2)) | |
W3 = tf.Variable(tf.random_normal([256, 10], stddev=0.01)) | |
model = tf.matmul(L2, W3) | |
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y)) | |
optimizer = tf.train.AdamOptimizer(0.001).minimize(loss) | |
init = tf.global_variables_initializer() | |
sess = tf.Session() | |
sess.run(init) | |
batch_size = 100 | |
total_batch = int(mnist.train.num_examples / batch_size) | |
for epoch in range(5): | |
total_cost = 0 | |
for i in range(total_batch): | |
batch_xs, batch_ys = mnist.train.next_batch(batch_size) | |
_, cost_val = sess.run([optimizer, loss], feed_dict={X: batch_xs, Y: batch_ys}) | |
total_cost += cost_val | |
print('Epoch:', '%04d' % (epoch + 1), | |
'Avg. cost =', '{:.3f}'.format(total_cost / total_batch)) | |
print('최적화 완료!') | |
is_correct = tf.equal(tf.argmax(model, 1), tf.argmax(Y, 1)) | |
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32)) | |
print('정확도:', | |
sess.run(accuracy, feed_dict={X: mnist.test.images, | |
Y: mnist.test.labels})) |
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