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Merge branch 'master' of https://github.com/haven-jeon/TensorFlow-Book-R

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#	ch10_rnn/Concept03_kor_word_spacing_rnn.Rmd
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gogamza committed May 19, 2017
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5 changes: 1 addition & 4 deletions ch10_rnn/Concept03_kor_word_spacing_rnn.Rmd
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Expand Up @@ -11,6 +11,7 @@ output: github_document
library(tensorflow)
library(hashmap)
makeCorpus <- function(str){
strv <- strsplit(str,split="")[[1]]
lenstrv <- length(strv)
Expand Down Expand Up @@ -249,7 +250,3 @@ wsp$predict(sent_mat_x, length(sent), 1)
plot(loss_v)
```





278 changes: 278 additions & 0 deletions ch10_rnn/Concept03_kor_word_spacing_rnn.md
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Ch 10: Concept 04
================

Korean word spacing RNN
=======================

``` r
#https://github.com/hunkim/DeepLearningZeroToAll/blob/master/lab-12-2-char-seq-rnn.py
library(tensorflow)
library(hashmap)

sents <- "아버지가 방에 들어가신다. "

uniq_char <- unique(strsplit(sents, split='')[[1]])

chmap <- hashmap(uniq_char, 1:length(uniq_char))
train_x <- array(0, dim=c(1, 15, length(uniq_char)))

chars <- strsplit(sents, split='')[[1]]

for(i in 1:length(chars)){
train_x[1,i,chmap[[chars[i]]]] <- 1
}

train_y <- matrix(0, ncol=15)

for(i in 2:length(chars)){

if(chars[i] == ' '){
train_y[1,i - 1] <- 1
}
}



# hyper parameters
dic_size <- as.integer(length(chmap$keys())) # RNN input size (one hot size)
hidden_size <- 2L # RNN output size
num_classes <- 2L # final output size (RNN or softmax, etc.)
batch_size <- 1L # one sample data, one batch
sequence_length <- as.integer(length(chars)) # number of lstm rollings (unit #)


X <- tf$placeholder(tf$float32, list(NULL, sequence_length, dic_size)) # X data
Y <- tf$placeholder(tf$int32, list(NULL, sequence_length)) # Y label

cell <- tf$contrib$rnn$BasicLSTMCell(
num_units=hidden_size, state_is_tuple=TRUE)
initial_state <- cell$zero_state(batch_size, tf$float32)
outputs_states <- tf$nn$dynamic_rnn(
cell, X, initial_state=initial_state, dtype=tf$float32)

#----
# FC layer
X_for_fc <- tf$reshape(outputs_states[[1]], list(-1L, hidden_size))
outputs <- tf$contrib$layers$fully_connected(outputs_states[[1]], num_classes, activation_fn=NULL)

# reshape out for sequence_loss
outputs <- tf$reshape(outputs, list(batch_size, sequence_length, num_classes))

weights <- tf$ones(list(batch_size, sequence_length))
sequence_loss <- tf$contrib$seq2seq$sequence_loss(
logits=outputs, targets=Y, weights=weights)
loss <- tf$reduce_mean(sequence_loss)
train <- tf$train$AdamOptimizer(learning_rate=0.1)$minimize(loss)

prediction <- tf$argmax(outputs, axis=2L)

with(tf$Session() %as% sess, {
sess$run(tf$global_variables_initializer())
for(i in 1:50){
l <- sess$run(list(loss, train), feed_dict=dict(X= train_x, Y= train_y))
result <- sess$run(prediction, feed_dict=dict(X= train_x))
print(result)
}
})
```

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13 changes: 5 additions & 8 deletions ch10_rnn/Concept03_rnn_real_world.md
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Expand Up @@ -134,14 +134,11 @@ test_y <- matrix(test_y, ncol=5 , byrow=T)
predictor$train(train_x, train_y, test_x, test_y)
```

## step: 0 train err: 0.820042 test err: 1.690292
## step: 100 train err: 0.041629 test err: 0.253392
## step: 200 train err: 0.040549 test err: 0.245927
## step: 300 train err: 0.039487 test err: 0.248768
## step: 400 train err: 0.038130 test err: 0.211382
## step: 500 train err: 0.036092 test err: 0.278987
## step: 600 train err: 0.033883 test err: 0.289871
## step: 700 train err: 0.032755 test err: 0.219035
## step: 0 train err: 0.763628 test err: 1.973412
## step: 100 train err: 0.041158 test err: 0.303015
## step: 200 train err: 0.038779 test err: 0.340862
## step: 300 train err: 0.035553 test err: 0.350543
## step: 400 train err: 0.033465 test err: 0.325148
## [1] "Model saved to ./model.ckpt"

``` r
Expand Down
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