Commit c8ac1e4

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Merge pull request #51 from zhengjxu/master

the dropout probability should be different between train and inference

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10 - RNN
- 02 - Autocomplete.py

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+10

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`‎10 - RNN/02 - Autocomplete.py`

Lines changed: 10 additions & 3 deletions

Original file line number	Diff line number	Diff line change
`@@ -70,13 +70,16 @@ def make_batch(seq_data):`
`70`	`70`	`# 기존처럼 one-hot 인코딩을 사용한다면 입력값의 형태는 [None, n_class] 여야합니다.`
`71`	`71`	`Y = tf.placeholder(tf.int32, [None])`
`72`	`72`
	`73`	`+# dropout prob for RNN`
	`74`	`+keep_prob = tf.placeholder(tf.float32, [])`
	`75`	`+`
`73`	`76`	`W = tf.Variable(tf.random_normal([n_hidden, n_class]))`
`74`	`77`	`b = tf.Variable(tf.random_normal([n_class]))`
`75`	`78`
`76`	`79`	`# RNN 셀을 생성합니다.`
`77`	`80`	`cell1 = tf.nn.rnn_cell.BasicLSTMCell(n_hidden)`
`78`	`81`	`# 과적합 방지를 위한 Dropout 기법을 사용합니다.`
`79`		`-cell1 = tf.nn.rnn_cell.DropoutWrapper(cell1, output_keep_prob=0.5)`
	`82`	`+cell1 = tf.nn.rnn_cell.DropoutWrapper(cell1, output_keep_prob=keep_prob)`
`80`	`83`	`# 여러개의 셀을 조합해서 사용하기 위해 셀을 추가로 생성합니다.`
`81`	`84`	`cell2 = tf.nn.rnn_cell.BasicLSTMCell(n_hidden)`
`82`	`85`
`@@ -108,7 +111,9 @@ def make_batch(seq_data):`
`108`	`111`
`109`	`112`	`for epoch in range(total_epoch):`
`110`	`113`	`_, loss = sess.run([optimizer, cost],`
`111`		`- feed_dict={X: input_batch, Y: target_batch})`
	`114`	`+ feed_dict={X: input_batch,`
	`115`	`+ Y: target_batch,`
	`116`	`+ keep_prob: 0.5})`
`112`	`117`
`113`	`118`	`print('Epoch:', '%04d' % (epoch + 1),`
`114`	`119`	`'cost =', '{:.6f}'.format(loss))`
`@@ -127,7 +132,9 @@ def make_batch(seq_data):`
`127`	`132`	`input_batch, target_batch = make_batch(seq_data)`
`128`	`133`
`129`	`134`	`predict, accuracy_val = sess.run([prediction, accuracy],`
`130`		`- feed_dict={X: input_batch, Y: target_batch})`
	`135`	`+ feed_dict={X: input_batch,`
	`136`	`+ Y: target_batch,`
	`137`	`+ keep_prob:1})`
`131`	`138`
`132`	`139`	`predict_words = []`
`133`	`140`	`for idx, val in enumerate(seq_data):`

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`‎10 - RNN/02 - Autocomplete.py`

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