In dfalbel/keras: R Interface to 'Keras'

library(keras)
knitr::opts_chunk$set(comment = NA, eval = FALSE)

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:

layer$name --- String, must be unique within a model.
layer$input_spec --- List of input specifications. Each entry describes one required input: (ndim, dtype). A layer with n input tensors must have an input_spec of length n.
layer$trainable --- Boolean, whether the layer weights will be updated during training.
layer$uses_learning_phase -- Whether any operation of the layer uses K.in_training_phase() or K.in_test_phase().
layer$input_shape --- Input shape. Provided for convenience, but note that there may be cases in which this attribute is ill-defined (e.g. a shared layer with multiple input shapes), in which case requesting input_shape will result in an error. Prefer using get_input_shape_at(layer, node_index).
layer$output_shape --- Output shape. See above.
layer$inbound_nodes --- List of nodes.
layer$outbound_nodes --- List of nodes.
layer$input, layer$output --- Input/output tensor(s). Note that if the layer is used more than once (shared layer), this is ill-defined and will result in an error. In such cases, use get_input_at(layer, node_index).
layer$input_mask, layer$output_mask --- Same as above, for masks.
layer$trainable_weights --- List of variables.
layer$non_trainable_weights --- List of variables.
layer$weights --- The concatenation of the lists trainable_weights and non_trainable_weights (in this order).
layer$constraints --- Mapping of weights to constraints.

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