We start by creating a model using the keras_model_sequential function. The codes used for the model architecture are given as follows:
# Model architecturemodel <- keras_model_sequential() model %>% layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu', input_shape = c(28,28,1)) %>% layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>% layer_max_pooling_2d(pool_size = c(2,2)) %>% layer_dropout(rate = 0.25) %>% layer_flatten() %>% layer_dense(units = 64, activation = 'relu') %>% layer_dropout(rate = 0.25) %>% layer_dense(units = 10, activation = 'softmax')
As shown in the preceding code, we add various layers to develop a CNN model. The input layer ...
