# this applies 32 convolution filters of size 3x3 each. Library(keras) # generate dummy data x_train % round() %>% matrix( nrow = 100, ncol = 1) %>% to_categorical( num_classes = 10) x_test % round() %>% matrix( nrow = 20, ncol = 1) %>% to_categorical( num_classes = 10) # create model model (100, 100, 3) tensors. Library(keras) # generate dummy data x_train % round() %>% matrix( nrow = 1000, ncol = 1) %>% to_categorical( num_classes = 10) x_test % round() %>% matrix( nrow = 100, ncol = 1) %>% to_categorical( num_classes = 10) # create model model % layer_dense( units = 64, activation = 'relu', input_shape = c( 20)) %>% layer_dropout( rate = 0.5) %>% layer_dense( units = 64, activation = 'relu') %>% layer_dropout( rate = 0.5) %>% layer_dense( units = 10, activation = 'softmax') %>% compile( loss = 'categorical_crossentropy', optimizer = optimizer_sgd( lr = 0.01, decay = 1e-6, momentum = 0.9, nesterov = TRUE), metrics = c( 'accuracy') ) # train model %>% fit(x_train, y_train, epochs = 20, batch_size = 128) # evaluate score % evaluate(x_test, y_test, batch_size = 128)

Keras sequential model