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

In LDNN: Longitudinal Data Neural Network

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

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)

## 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.