Deep Learning with R Cookbook

How to do it...

Now, we move on to building our model:

1. We first define a variable, the value of which will be equal to the dimension of the compressed encoded representation of the input. Then, we set the input layer of our model:

encoding_dim = 32 input_img = layer_input(shape=c(784),name = "input")

2. Let's build an encoder and decoder and combine them to build an autoencoder:

encoded = input_img %>% layer_dense(units = encoding_dim, activation='relu',name = "encoder")decoded = encoded %>% layer_dense(units = c(784), activation='sigmoid',name = "decoder")# this model maps an input to its reconstructionautoencoder = keras_model(input_img, decoded)

Now, we visualize the summary of the autoencoder model:

summary(autoencoder)

The summary of ...