머신러닝스터디/2016/2016 05 28 - Revision history

Maintenance script: Repair batch-0005 pages from live compare

2026-03-27T00:44:57Z

Repair batch-0005 pages from live compare

← Older revision		Revision as of 00:44, 27 March 2026
Line 12:		Line 12:
	# (1, 1) => 1 (1, 1) => 1 (1, 1) => 1 (1, 1) => 0		# (1, 1) => 1 (1, 1) => 1 (1, 1) => 1 (1, 1) => 0

	W1 = tf.Variable(tf.random_uniform([2, 2]))		W1 = tf.Variable(tf.random_uniform([2, 2]))
	b1 = tf.Variable(tf.random_uniform([2]))		b1 = tf.Variable(tf.random_uniform([2]))

	W2 = tf.Variable(tf.random_uniform([2, 1]))		W2 = tf.Variable(tf.random_uniform([2, 1]))
	b2 = tf.Variable(tf.random_uniform([1]))		b2 = tf.Variable(tf.random_uniform([1]))

	def logic_gate(x):		def logic_gate(x):
Line 22:		Line 22:
	return tf.sigmoid(tf.matmul(hidden, W2) + b2)		return tf.sigmoid(tf.matmul(hidden, W2) + b2)

	x = tf.placeholder("float", [None, 2])		x = tf.placeholder("float", [None, 2])
	y = tf.placeholder("float", [None, 1])		y = tf.placeholder("float", [None, 1])

	value = logic_gate(x)		value = logic_gate(x)
Line 35:		Line 35:
	sess.run(init)		sess.run(init)
	for i in range(30001):		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]]})		result = sess.run(optimize, feed_dict={x: [[0, 0], [0, 1], [1, 0], [1, 1]], y: [[1], [0], [0], [1]]})
	if (i % 1000 == 0):		if (i % 1000 == 0):
	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 ==		== 후기 ==
	== 다음 시간에는 ==		== 다음 시간에는 ==
	* ML Week 5 Back Propagation 실습		* ML Week 5 Back Propagation 실습
	== 더 보기 ==		== 더 보기 ==

14.63.105.219 at 07:42, 13 August 2016

2016-08-13T07:42:53Z

← Older revision		Revision as of 07:42, 13 August 2016
Line 2:		Line 2:
	[[머신러닝스터디/2016/목차]]		[[머신러닝스터디/2016/목차]]
	== 내용 ==		== 내용 ==
	* Basic ~~Logix~~ Gate만들어보자!		* Basic Logic Gate만들어보자!
	** AND, OR, NXOR, XOR		** AND, OR, NXOR, XOR
	=== 코드 ===		=== 코드 ===

imported>rabierre at 17:32, 24 July 2016

2016-07-24T17:32:58Z

← Older revision		Revision as of 17:32, 24 July 2016
Line 1:		Line 1:
	~~[[pagelist(^(~~머신러닝스터디/2016~~))]]~~		[[머신러닝스터디/2016]]
			[[머신러닝스터디/2016/목차]]
	== 내용 ==		== 내용 ==
	* Basic Logix Gate만들어보자!		* Basic Logix Gate만들어보자!

14.63.105.219 at 06:55, 4 June 2016

2016-06-04T06:55:58Z

← Older revision		Revision as of 06:55, 4 June 2016
Line 25:		Line 25:

	value = logic_gate(x)		value = logic_gate(x)
	loss = tf.reduce_sum(tf.pow(y-value, 2))		// loss = tf.reduce_sum(tf.pow(y-value, 2))
	~~# TODO: Can't use this. Because values are not one-hot encoded.~~		loss = - tf.reduce_mean(ytf.log(value) + (1-y)tf.log(1-value))
	# loss = -tf.reduce_mean(ytf.log(value) - (1-y)tf.log(1-~~value))~~
	~~# TODO: Why don't work?~~
	~~# loss = -tf.reduce_sum(y*tf.log(~~value))
	optimize = tf.train.GradientDescentOptimizer(0.01).minimize(loss)		optimize = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

14.63.105.219 at 06:33, 4 June 2016

2016-06-04T06:33:08Z

← Older revision		Revision as of 06:33, 4 June 2016
Line 26:		Line 26:
	value = logic_gate(x)		value = logic_gate(x)
	loss = tf.reduce_sum(tf.pow(y-value, 2))		loss = tf.reduce_sum(tf.pow(y-value, 2))
	# TODO: Can't use this. Because ~~this code is~~ not one-hot ~~encoding~~.		# TODO: Can't use this. Because values are not one-hot encoded.
	# loss = -tf.reduce_mean(ytf.log(value) - (1-y)tf.log(1-value))		# loss = -tf.reduce_mean(ytf.log(value) - (1-y)tf.log(1-value))
	# TODO: Why don't work?		# TODO: Why don't work?

114.108.95.133 at 17:39, 29 May 2016

2016-05-29T17:39:29Z

← Older revision		Revision as of 17:39, 29 May 2016
Line 26:		Line 26:
	value = logic_gate(x)		value = logic_gate(x)
	loss = tf.reduce_sum(tf.pow(y-value, 2))		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		# TODO: Can't use this. Because this code is not one-hot encoding.
			# loss = -tf.reduce_mean(ytf.log(value) - (1-y)tf.log(1-value))
			# TODO: Why don't work?
			# loss = -tf.reduce_sum(y*tf.log(value))
	optimize = tf.train.GradientDescentOptimizer(0.01).minimize(loss)		optimize = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

59.11.128.254 at 11:07, 28 May 2016

2016-05-28T11:07:40Z

← Older revision		Revision as of 11:07, 28 May 2016
Line 1:		Line 1:
	[[pagelist(^(머신러닝스터디/2016))]]		[[pagelist(^(머신러닝스터디/2016))]]
	== 내용 ==		== 내용 ==
			* Basic Logix Gate만들어보자!
			** AND, OR, NXOR, XOR
	=== 코드 ===		=== 코드 ===
	import tensorflow as tf		import tensorflow as tf

59.11.128.254 at 11:06, 28 May 2016

2016-05-28T11:06:50Z

← Older revision		Revision as of 11:06, 28 May 2016
Line 1:		Line 1:
			[[pagelist(^(머신러닝스터디/2016))]]
			== 내용 ==
			=== 코드 ===
	import tensorflow as tf		import tensorflow as tf
	# AND OR NXOR XOR		# AND OR NXOR XOR
Line 33:		Line 36:
	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 ==

59.11.128.254 at 10:51, 28 May 2016

2016-05-28T10:51:42Z

← Older revision		Revision as of 10:51, 28 May 2016
Line 1:		Line 1:
	import tensorflow as tf		import tensorflow as tf
	# AND		# AND OR NXOR XOR
	# (0, 0) => 0		# (0, 0) => 0 (0, 0) => 0 (0, 0) => 1 (0, 0) => 0
	# (0, 1) => 0		# (0, 1) => 0 (0, 1) => 1 (0, 1) => 0 (0, 1) => 1
	# (1, 0) => 0		# (1, 0) => 0 (1, 0) => 1 (1, 0) => 0 (1, 0) => 1
	# (1, 1) => 1		# (1, 1) => 1 (1, 1) => 1 (1, 1) => 1 (1, 1) => 0

	W1 = tf.Variable(tf.random_uniform([2, 2]))		W1 = tf.Variable(tf.random_uniform([2, 2]))
Line 13:		Line 13:

	def logic_gate(x):		def logic_gate(x):
	hidden = tf.sigmoid~~(tf.add~~(tf.matmul(x, W1), b1))		hidden = tf.sigmoid(tf.matmul(x, W1) + b1)
	return tf.sigmoid~~(tf.add~~(tf.matmul(hidden, W2), b2))		return tf.sigmoid(tf.matmul(hidden, W2) + b2)

	x = tf.placeholder("float", [None, 2])		x = tf.placeholder("float", [None, 2])
Line 20:		Line 20:

	value = logic_gate(x)		value = logic_gate(x)
	loss = tf.~~reduce_mean~~(-(y * tf.~~log~~(~~value)~~ - (~~(1-~~y) * tf.log(1-value))))		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)		optimize = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

Line 31:		Line 31:
	result = sess.run(optimize, feed_dict={x: [[0, 0], [0, 1], [1, 0], [1, 1]], y: [[1], [0], [0], [1]]})		result = sess.run(optimize, feed_dict={x: [[0, 0], [0, 1], [1, 0], [1, 1]], y: [[1], [0], [0], [1]]})
	if (i % 1000 == 0):		if (i % 1000 == 0):
	print(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]]}))

imported>rabierre at 10:22, 28 May 2016

2016-05-28T10:22:49Z

← Older revision		Revision as of 10:22, 28 May 2016
Line 1:		Line 1:
			import tensorflow as tf
			# AND
	# (0, 0) => 0		# (0, 0) => 0
	# (0, 1) => 0		# (0, 1) => 0
	# (1, 0) => 0		# (1, 0) => 0
	# (1, 1) => 1		# (1, 1) => 1


	W1 = tf.Variable(tf.random_uniform([2, 2]))		W1 = tf.Variable(tf.random_uniform([2, 2]))
	b1 = tf.Variable(tf.random_uniform([2]))		b1 = tf.Variable(tf.random_uniform([2]))

	W2 = tf.Variable(tf.random_uniform([2, 1]))		W2 = tf.Variable(tf.random_uniform([2, 1]))
	b2 = tf.Variable(tf.random_uniform([1]))		b2 = tf.Variable(tf.random_uniform([1]))

	def logic_gate(x):		def logic_gate(x):
	hidden = tf.sigmoid(tf.add(tf.matmul(x, W1), b1))		hidden = tf.sigmoid(tf.add(tf.matmul(x, W1), b1))
	return tf.sigmoid(tf.add(tf.matmul(hidden, W2), b2))		return tf.sigmoid(tf.add(tf.matmul(hidden, W2), b2))

	x = tf.placeholder("float", [None, 2])		x = tf.placeholder("float", [None, 2])
	y = tf.placeholder("float", [None, 1])		y = tf.placeholder("float", [None, 1])

	value = logic_gate(x)		value = logic_gate(x)
	loss = tf.reduce_mean(-(y * tf.log(value) - ((1-y) * tf.log(1-value))))		loss = tf.reduce_mean(-(y * tf.log(value) - ((1-y) * tf.log(1-value))))

	optimize = tf.train.GradientDescentOptimizer(0.01).minimize(loss)		optimize = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

	init = tf.initialize_all_variables()		init = tf.initialize_all_variables()

	with tf.Session() as sess:		with tf.Session() as sess:
	sess.run(init)		sess.run(init)