To specify the encoder model architecture, we will use the following code:
# Encoderinput_layer <- layer_input(shape = c(28,28,1)) encoder <- input_layer %>% layer_conv_2d(filters = 8, kernel_size = c(3,3), activation = 'relu', padding = 'same') %>% layer_max_pooling_2d(pool_size = c(2,2), padding = 'same') %>% layer_conv_2d(filters = 4, kernel_size = c(3,3), activation = 'relu', padding = 'same') %>% layer_max_pooling_2d(pool_size = c(2,2), padding = 'same')
summary(encoder)OutputTensor("max_pooling2d_10/MaxPool:0", shape=(?, 7, 7, 4), dtype=float32)
Here, for the input to the encoder, we specify the input layer so that it's 28 x 28 x 1 in size. Two convolutional layers, one with 8 filters and another with 4 filters, ...
