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로드 중.... (전체 텍스트 보기)

전체 글

(1)

06. 순환신경망(RNN)

김형욱

(2)

Sequence data

• 사람들은 하나의 단어로만 말을 이해하지 않음

• 이전의 단어, 현재 단어의 조합으로 말을 이해

• 기존의 NN/CNN은 이러한 일을 할 수 없음

(3)

Recurrent Neural Network

(4)

Recurrent Neural Network

• RNN에서 사용되는 수식

모든 셀에서 같은 함수식 사용

h t = f w (h t −1 , x t )

(5)

Recurrent Neural Network

h t = f w (h t −1 , x t )

h t = tanh(W hh h t −1 +W xh x t )

y t = W hy h t

(6)

RNN-example

• Input data들은 one-hot encoding

E L L O

H E L L

(7)

RNN-example

h

t

= tanh(W

hh

h

t−1

+W

xh

x

t

) y t = W hy h t

(8)

RNN applications

• Language Modeling

• Speech Recognition

• Machine Translation

• Conversation Modeling/Question Answering

• Image/Video Captioning

• Image/Music/Dance Generation

(9)

RNN

(10)

Multi-Layer RNN

(11)

RNN in TensorFlow

Cell = tf.contrib.rnn.BasicRNNCell(num_units=hidden_size)

Outputs, _states = tf.nn.dynamic_rnn(cell,x_data, dtype=tf.float32)

(12)

RNN

(13)

참조

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