{"id":568,"date":"2020-07-23T22:19:56","date_gmt":"2020-07-23T13:19:56","guid":{"rendered":"http:\/\/www.mukgee.com\/?p=568"},"modified":"2020-12-27T01:24:16","modified_gmt":"2020-12-26T16:24:16","slug":"m-llol-%eb%8d%b0%ec%9d%b4%ed%84%b0%eb%a5%bc-%ed%99%9c%ec%9a%a9%ed%95%9c-skill-%ed%8a%b8%eb%a6%ac-%ed%95%99%ec%8a%b53","status":"publish","type":"post","link":"http:\/\/mukgee.com\/?p=568","title":{"rendered":"[M\/L]LOL \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud55c Skill \ud2b8\ub9ac \ud559\uc2b5(3)"},"content":{"rendered":"

[M\/L]LOL \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud55c Skill \ud2b8\ub9ac \ud559\uc2b5(2) \uc5d0 \uc774\uc5b4 \uc9c4\ud589\ud569\ub2c8\ub2e4.<\/p>\n

\uae30\uc874\uc758 N-gram \ud615\ud0dc\uc758 rnn \uc73c\ub85c\ub294 \uc6d0\ud558\ub294 \uacb0\uacfc\uac00 \ub098\uc624\uc9c0 \uc54a\uc544 Tensorflow \uacf5\uc2dd \ud648\ud398\uc774\uc9c0\uc758 RNN Tutorial\ub97c \ucc38\uace0\ud574\uc11c \uc9c4\ud589\ud574\ubcf4\uc558\uc2b5\ub2c8\ub2e4.<\/p>\n

https:\/\/www.tensorflow.org\/tutorials\/text\/text_generation?hl=ko<\/a><\/p>\n

(\ud55c\uae00\ub85c \ub41c tensorflow \uacf5\uc2dd tutorial \ud398\uc774\uc9c0)<\/p>\n

 <\/p>\n

\uba3c\uc800 \ub370\uc774\ud130 \uc14b\uc744 tutorial\ucc98\ub7fc \ud558\ub098\uc758 text \ubb49\uce58\ucc98\ub7fc \ub9cc\ub4e4\uae30 \uc704\ud574 join \ud569\ub2c8\ub2e4.<\/p>\n

new_data = []\nfor data in lol_data:\n    new_data.append(''.join([idx2char[int(x)] for x in str(data)]))\n# print(lol_data[0])\nstringfy_data = '\\n'.join(new_data)\n<\/pre>\n
\uadf8\ub9ac\uace0 window size\uac00 6\uc778 dataset \uc744 \uad6c\uc131\ud574\uc90d\ub2c8\ub2e4.<\/p>\n
sequences = char_dataset.batch(seq_length+1, drop_remainder=True)\n\ndef split_input_target(chunk):\n    input_text = chunk[:-1]\n    target_text = chunk[1:]\n    return input_text, target_text\n\ndataset = sequences.map(split_input_target)<\/pre>\n
\uc774\ud6c4 \uc815\ud655\ud788 tutorial\uacfc \ub3d9\uc77c\ud55c RNN \ubaa8\ub378\uc744 \uc0dd\uc131 \ud569\ub2c8\ub2e4.<\/p>\n
# \ubb38\uc790\ub85c \ub41c \uc5b4\ud718 \uc0ac\uc804\uc758 \ud06c\uae30\nvocab_size = len(set(stringfy_data))\n\n# \uc784\ubca0\ub529 \ucc28\uc6d0\nembedding_dim = 256\n\n# RNN \uc720\ub2db(unit) \uac1c\uc218\nrnn_units = 1024\n\ndef build_model(vocab_size, embedding_dim, rnn_units, batch_size):\n  model = tf.keras.Sequential([\n    tf.keras.layers.Embedding(vocab_size, embedding_dim,\n                              batch_input_shape=[batch_size, None]),\n    tf.keras.layers.LSTM(rnn_units,\n                        return_sequences=True,\n                        stateful=True,\n                        recurrent_initializer='glorot_uniform'),\n    tf.keras.layers.Dense(vocab_size)\n  ])\n  return model<\/pre>\n
 <\/p>\n
input shape\uc774 6 \uc774\uace0 1024\uc758 layer\uac00 \uc606\uc73c\ub85c \ub298\uc5b4\uc9c4 RNN \ubaa8\ub378\uc744 \uc0dd\uc131\ud588\uc2b5\ub2c8\ub2e4.<\/p>\n
(Embedding \ubaa8\ub378\uc5d0 \ub300\ud574\uc11c\ub294 \ucd94\uac00 \ud559\uc2b5\uc774 \ud544\uc694)<\/p>\n
\"\"<\/p>\n
200 epochs\ub85c \ubaa8\ub378\uc744 \uc2e4\ud589\ud574\ubcf4\uc558\uc9c0\ub9cc loss\uac00 \ub5a8\uc5b4\uc9c0\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.<\/p>\n
\ubaa8\ub378\uc774 \uc798\ubabb\ub418\uc5c8\uac70\ub098 \uc785\ub825 \ub370\uc774\ud130\uac00 \uc798\ubabb\ub41c\uac83 \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
\"\"<\/p>\n
18\uac1c\uc758 seq\ub97c \ub9cc\ub4e4\uc5b4 \ubcf4\uc558\uc9c0\ub9cc \uc5ed\uc2dc \uae30\ub300\ud588\ub358 \uacb0\uacfc\uc640 \uc804\ud600 \ub2e4\ub985\ub2c8\ub2e4.<\/p>\n
(Q\ub294 5\uac1c\ub9cc \ub098\uc640\uc57c\ud558\uace0 R\ub294 6\ubc88\uca30 \ub098\uc640\uc57c\ud558\ub294 \ub4f1\uc758 \uaddc\uce59\uc774 \uc804\ud600 \uc9c0\ucf1c\uc9c0\uc9c0 \uc54a\uc74c)<\/p>\n
\"\"<\/p>\n
 <\/p>\n
\uae30\ubcf8 RNN\uc774 \uc544\ub2cc Stacked RNN \ud639\uc740 Dynamic RNN \ubaa8\ub378\uc744 \uc801\uc6a9\ud558\uace0<\/p>\n
\uc785\ub825 \ub370\uc774\ud130 \ub610\ud55c \uac8c\uc784\ubcc4 \uad6c\ubd84\uc744 \uba85\ud655\ud788 \ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ucd94\uac00 \uace0\ubbfc\uc774 \ud544\uc694\ud560\uac83 \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
 <\/p>\n","protected":false},"excerpt":{"rendered":"
[M\/L]LOL \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud55c Skill \ud2b8\ub9ac \ud559\uc2b5(2) \uc5d0 \uc774\uc5b4 \uc9c4\ud589\ud569\ub2c8\ub2e4. \uae30\uc874\uc758 N-gram \ud615\ud0dc\uc758 rnn \uc73c\ub85c\ub294 \uc6d0\ud558\ub294 \uacb0\uacfc\uac00 \ub098\uc624\uc9c0 \uc54a\uc544 Tensorflow \uacf5\uc2dd \ud648\ud398\uc774\uc9c0\uc758 RNN Tutorial\ub97c \ucc38\uace0\ud574\uc11c \uc9c4\ud589\ud574\ubcf4\uc558\uc2b5\ub2c8\ub2e4.…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":""},"categories":[3],"tags":[24],"aioseo_notices":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/mukgee.com\/index.php?rest_route=\/wp\/v2\/posts\/568"}],"collection":[{"href":"http:\/\/mukgee.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/mukgee.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/mukgee.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/mukgee.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=568"}],"version-history":[{"count":1,"href":"http:\/\/mukgee.com\/index.php?rest_route=\/wp\/v2\/posts\/568\/revisions"}],"predecessor-version":[{"id":596,"href":"http:\/\/mukgee.com\/index.php?rest_route=\/wp\/v2\/posts\/568\/revisions\/596"}],"wp:attachment":[{"href":"http:\/\/mukgee.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=568"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/mukgee.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=568"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/mukgee.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=568"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}