| # Build model | |
| model <- keras_model_sequential() %>% | |
| layer_conv_2d(input_shape = dim(train$X)[2:4], | |
| filters = 16, kernel_size = c(3, 3), | |
| activation = "relu") %>% | |
| layer_max_pooling_2d(pool_size = c(2, 2)) %>% | |
| layer_dropout(rate = .2) %>% | |
| layer_conv_2d(filters = 32, kernel_size = c(3, 3), | |
| activation = "relu") %>% | |
| layer_max_pooling_2d(pool_size = c(2, 2)) %>% | |
| layer_dropout(rate = .2) %>% | |
| layer_conv_2d(filters = 64, kernel_size = c(3, 3), | |
| activation = "relu") %>% | |
| layer_max_pooling_2d(pool_size = c(2, 2)) %>% | |
| layer_dropout(rate = .2) %>% | |
| layer_conv_2d(filters = 128, kernel_size = c(3, 3), | |
| activation = "relu") %>% | |
| layer_max_pooling_2d(pool_size = c(28, 2)) %>% | |
| layer_dropout(rate = .2) %>% | |
| layer_flatten() %>% | |
| layer_dense(units = 128, activation = "relu") %>% | |
| layer_dropout(rate = .5) %>% | |
| layer_dense(units = ncol(train$Y), activation = "softmax") |
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