create_model: Pre-defined Neural Network for Longitudinal Data

In LDNN: Longitudinal Data Neural Network

Description

Create the Neural Network model (Keras).

Usage

create_model(
  rnn_inputs,
  recurrent_droppout,
  inputs,
  layer_dropout,
  n_nodes_hidden_layers,
  loss_function,
  opt,
  metric
)

Arguments

rnn_inputs	The number of inputs (integers) per each LSTM (vector of length 10).
recurrent_droppout	The dropout to be applied in the LSTMs (between 0 and 1).
inputs	The number of inputs (integer) to be concatenated with the output of the LSTMs.
layer_dropout	The dropout to be applied between the hidden layers (between 0 and 1).
n_nodes_hidden_layers	The number of nodes in the hidden layers (2 in total).
loss_function	The loss function to be used.
opt	The optimizer to be used.
metric	The metric to be used.

Value

The model object built in Keras.

Examples

inp = c(20,24,24,24,16,16,16,16,16,15)
rec_drop = rep(0.1,10)
l_drop = c(0.1,0.1)
## Not run: 
create_model(inp,rec_drop,232,c(0.1,0.1),l_drop,'mean_squared_error','adam','mean_absolute_error')

## End(Not run)
# The functions require to have python installed
# As well as tensorflow, keras and reticulate package.

LDNN documentation built on Dec. 2, 2020, 5:01 p.m.