About Keras Layers

Overview

Keras layers are the fundamental building block of keras models. Layers are created using a wide variety of layer_ functions and are typically composed together by stacking calls to them using the pipe %>% operator. For example:

model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 32, input_shape = c(784)) %>% 
  layer_activation('relu') %>% 
  layer_dense(units = 10) %>% 
  layer_activation('softmax')

A wide variety of layers are available, including:

Properties

All layers share the following properties:

Functions

The following functions are available for interacting with layers:

get_config() from_config()

Layer/Model configuration

get_weights() set_weights()

Layer/Model weights as R arrays

count_params()

Count the total number of scalars composing the weights.

get_input_at() get_output_at() get_input_shape_at() get_output_shape_at() get_input_mask_at() get_output_mask_at()

Retrieve tensors for layers with multiple nodes

reset_states()

Reset the states for a layer