Deep Learning with R Cookbook

How to do it...

In this section, we will use the same Fashion-MNIST dataset that was used in the previous Introduction to the convolution operation recipe of this chapter. The data exploration and transformation will remain the same, hence we directly jump to the model configuration:

1. Let's define our model with strides and padding:

cnn_model_sp <- keras_model_sequential() %>%  layer_conv_2d(filters = 8, kernel_size = c(4,4), activation = 'relu', input_shape = c(28,28,1), strides = c(2L, 2L),,padding = "same") %>%  layer_conv_2d(filters = 16, kernel_size = c(3,3), activation = 'relu') %>%  layer_flatten() %>%  layer_dense(units = 16, activation = 'relu') %>%  layer_dense(units = 10, activation = 'softmax')

Let's look at the summary ...