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[[pagelist(^(머신러닝스터디/2016))]] | |||
== 내용 == | |||
=== 코드 === | |||
import tensorflow as tf | import tensorflow as tf | ||
# AND OR NXOR XOR | # AND OR NXOR XOR | ||
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print("Epoch: ", i) | print("Epoch: ", i) | ||
print(sess.run([value, loss], feed_dict={x: [[0, 0], [0, 1], [1, 0], [1, 1]], y: [[1], [0], [0], [1]]})) | print(sess.run([value, loss], feed_dict={x: [[0, 0], [0, 1], [1, 0], [1, 1]], y: [[1], [0], [0], [1]]})) | ||
== 후기 == | |||
== 다음 시간에는 == | |||
* ML Week 5 Back Propagation 실습 | |||
== 더 보기 == | |||
Revision as of 11:06, 28 May 2016
[[pagelist(^(머신러닝스터디/2016))]]
내용
코드
import tensorflow as tf
# AND OR NXOR XOR
# (0, 0) => 0 (0, 0) => 0 (0, 0) => 1 (0, 0) => 0
# (0, 1) => 0 (0, 1) => 1 (0, 1) => 0 (0, 1) => 1
# (1, 0) => 0 (1, 0) => 1 (1, 0) => 0 (1, 0) => 1
# (1, 1) => 1 (1, 1) => 1 (1, 1) => 1 (1, 1) => 0
W1 = tf.Variable(tf.random_uniform([2, 2]))
b1 = tf.Variable(tf.random_uniform([2]))
W2 = tf.Variable(tf.random_uniform([2, 1]))
b2 = tf.Variable(tf.random_uniform([1]))
def logic_gate(x):
hidden = tf.sigmoid(tf.matmul(x, W1) + b1)
return tf.sigmoid(tf.matmul(hidden, W2) + b2)
x = tf.placeholder("float", [None, 2])
y = tf.placeholder("float", [None, 1])
value = logic_gate(x)
loss = tf.reduce_sum(tf.pow(y-value, 2))
# loss = tf.reduce_mean(-tf.reduce_sum(y*tf.log(value), reduction_indices=1)) # Don't work
optimize = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
for i in range(30001):
result = sess.run(optimize, feed_dict={x: [[0, 0], [0, 1], [1, 0], [1, 1]], y: [[1], [0], [0], [1]]})
if (i % 1000 == 0):
print("Epoch: ", i)
print(sess.run([value, loss], feed_dict={x: [[0, 0], [0, 1], [1, 0], [1, 1]], y: [[1], [0], [0], [1]]}))
후기
다음 시간에는
- ML Week 5 Back Propagation 실습