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19 changes: 5 additions & 14 deletions 09-NN.Rmd
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Expand Up @@ -118,26 +118,17 @@ dim(data_train_y)

### **Ketika running model Neural Network, weight/bobot diinisialisasi secara random sehingga menyebabkan hasil yang berbeda jika dilakukan berulang kali. Bagaimana cara mengatur `set.seed()` pada Neural Network?**

Metode neural network selalu menginisialisasi bobot/weight secara random di awal, sehingga ketika metode tersebut di running berulang kali akan memperoleh hasil yang berbeda. Untuk mengatasi hal tersebut kita dapat menggunakan seed (state random). Kita dapat menentukan seed dengan menggunakan fungsi `use_session_with_seed()` dari library `keras`.
Metode neural network selalu menginisialisasi bobot/weight secara random di awal, sehingga ketika metode tersebut di running berulang kali akan memperoleh hasil yang berbeda. Untuk mengatasi hal tersebut kita dapat menggunakan seed (state random). Kita dapat menentukan seed dengan menggunakan fungsi `set_random_seed()` dari library `tensorflow`.

```{r eval=FALSE}
use_session_with_seed(seed)
```

Selain menggunakan cara di atas kita juga dapat menggunakan seed dengan fungsi `initializer_random_normal()`. Berikut cara menggunakan seed dengan fungsi tersebut:
Pastikan kode pada chunk ini dijalankan secara bersamaan:

```{r eval=FALSE}
# define seed
set.seed(100)
initializer <- initializer_random_normal(seed = 100)

# use the seed when building architecture
# set the seed together when building architecture
tensorflow::set_random_seed(100)
model <- keras_model_sequential() %>%
layer_dense(units = ...,
activation = "...",
input_shape = c(...),
kernel_initializer = initializer,
bias_initializer = initializer)
input_shape = c(...))
```

### **Bagaimana cara membagi data train, test, dan validation untuk keperluan deep learning analysis?**
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