examples/cifar10/kerasformula_cifar10_lstm.md

In rdrr1990/kerasformula: A High-Level R Interface for Neural Nets

lstm for Image Classification with kerasformula cifar10

Pete Mohanty

This document shows how to classify images using the cifar10 using kms from library(kerasformula) and the data preparation found here. The example below uses N = 500 for training (of which 20% is used for validation) and 100 for testing.

k <- keras_model_sequential()
k %>%
  layer_embedding(input_dim = 3072, output_dim = 1024) %>% 
  layer_lstm(units = 512, dropout = 0.5, recurrent_dropout = 0.25) %>% 
  layer_dense(units = 128, activation = "relu") %>%
  layer_dropout(0.3) %>%
  layer_dense(units = 10, # number of levels observed on y or just 1 if binary  
              activation = "sigmoid")

k %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = 'adam',     # ?optimizer_adam
  metrics = c('accuracy')
)

fit <- kms(as.factor(lab) ~ ., training, k, pTraining = 1, Nepochs = 10)
plot(fit$history) + theme_minimal()

forecast <- predict(fit, testing)
forecast$accuracy

[1] 0.23

rdrr1990/kerasformula documentation built on June 6, 2019, 8:02 a.m.