cnn알고리즘을 활용한 이미지 스타일 변환 모델

딥러닝
posted by 코딩 공부중 2022. 2. 7. 12:38

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cnn알고리즘 기반

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딥러닝
posted by 코딩 공부중 2019. 2. 24. 21:07
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import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
 
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./mnist/data/", one_hot=True)
 
#신경망 모델 구성
X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
 
W1 = tf.Variable(tf.random_normal([784,256], stddev=0.01))
L1 = tf.nn.relu(tf.matmul(X, W1))
L1 = tf.nn.dropout(L1, keep_prob)
 
W2 = tf.Variable(tf.random_normal([256,256], stddev=0.01))
L2 = tf.nn.relu(tf.matmul(L1, W2))
L2 = tf.nn.dropout(L2, keep_prob)
 
W3 = tf.Variable(tf.random_normal([256,10], stddev=0.01))
model = tf.matmul(L2, W3)
 
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))
optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)
 
#신경망 모델 학습
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(10):
    total_cost = 0
     
    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
         
        _, cost_val = sess.run([optimizer, cost],
            feed_dict={X: batch_xs, Y: batch_ys, keep_prob: 0.8})
        total_cost += cost_val
         
    print('epoch:', '%4d' % (epoch + 1), 'Abg.cost = ', '{:.3f}'.format(total_cost / total_batch))
     
print('최적화 완료')
 
#결과 확인
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, keep_prob: 1}))
 
#결과 확인(matplotlib)
labels = sess.run(model, feed_dict={X: mnist.test.images, Y: mnist.test.labels, keep_prob: 1})
fig = plt.figure()
for i in range(10):
    subplot = fig.add_subplot(2, 5, i + 1)
    subplot.set_xticks([])
    subplot.set_yticks([])
    subplot.set_title('%d'% np.argmax(labels[i]))
    subplot.imshow(mnist.test.images[i].reshape((28,28)),
                  cmap=plt.cm.gray_r)
     
plt.show()

참고 : 골빈해커의 3분 딥러닝


결과 : 


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