LDNN (version 1.10)

fit_model: Fit the pre-defined Neural Network for Longitudinal Data

Description

Fit the created Neural Network model (Keras).

Usage

fit_model(
  model,
  ver,
  n_epoch,
  bsize,
  X1,
  X2,
  X3,
  X4,
  X5,
  X6,
  X7,
  X8,
  X9,
  X10,
  Xif,
  y
)

Arguments

model

The model object produced by create_model().

ver

ver=0 to show nothing, ver=1 to show animated progress bar, ver=2 to just mention the number of epoch during training.

n_epoch

The number of epochs to train the model.

bsize

The batch size.

X1

Features as inputs of 1st LSTM.

X2

Features as inputs of 2nd LSTM.

X3

Features as inputs of 3rd LSTM.

X4

Features as inputs of 4th LSTM.

X5

Features as inputs of 5th LSTM.

X6

Features as inputs of 6th LSTM.

X7

Features as inputs of 7th LSTM.

X8

Features as inputs of 8th LSTM.

X9

Features as inputs of 9th LSTM.

X10

Features as inputs of 10th LSTM.

Xif

The features to be concatenated with the outputs of the LSTMs.

y

The target variable.

Value

The fitted model.

Examples

# NOT RUN {
X1 <- matrix(runif(500*20), nrow=500, ncol=20)
X2 <- matrix(runif(500*24), nrow=500, ncol=24)
X3 <- matrix(runif(500*24), nrow=500, ncol=24)
X4 <- matrix(runif(500*24), nrow=500, ncol=24)
X5 <- matrix(runif(500*16), nrow=500, ncol=16)
X6 <- matrix(runif(500*16), nrow=500, ncol=16)
X7 <- matrix(runif(500*16), nrow=500, ncol=16)
X8 <- matrix(runif(500*16), nrow=500, ncol=16)
X9 <- matrix(runif(500*16), nrow=500, ncol=16)
X10 <- matrix(runif(500*15), nrow=500, ncol=15)
Xif <- matrix(runif(500*232), nrow=500, ncol=232)
y <- matrix(runif(500), nrow=500, ncol=1)
# }
# NOT RUN {
fitted_model = fit_model(model,0,1,32,X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,Xif,y)
# }
# NOT RUN {
# The functions require to have python installed
# As well as tensorflow, keras and reticulate package.
# }