evaluate_model {LDNN}

Evaluate the pre-defined Neural Network for Longitudinal Data

Description

Evaluate the fitted Neural Network model (Keras).

Usage

evaluate_model(
  model,
  X1_test,
  X2_test,
  X3_test,
  X4_test,
  X5_test,
  X6_test,
  X7_test,
  X8_test,
  X9_test,
  X10_test,
  Xif_test,
  y_test,
  bsize
)

Arguments

model

The fitted model object produced by create_model().

X1_test

Features as inputs of 1st LSTM.

X2_test

Features as inputs of 2nd LSTM.

X3_test

Features as inputs of 3rd LSTM.

X4_test

Features as inputs of 4th LSTM.

X5_test

Features as inputs of 5th LSTM.

X6_test

Features as inputs of 6th LSTM.

X7_test

Features as inputs of 7th LSTM.

X8_test

Features as inputs of 8th LSTM.

X9_test

Features as inputs of 9th LSTM.

X10_test

Features as inputs of 10th LSTM.

Xif_test

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

y_test

The target variable.

bsize

The batch size.

Value

The evaluation results.

Examples

X1_test <- matrix(runif(500*20), nrow=500, ncol=20)
X2_test <- matrix(runif(500*24), nrow=500, ncol=24)
X3_test <- matrix(runif(500*24), nrow=500, ncol=24)
X4_test <- matrix(runif(500*24), nrow=500, ncol=24)
X5_test <- matrix(runif(500*16), nrow=500, ncol=16)
X6_test <- matrix(runif(500*16), nrow=500, ncol=16)
X7_test <- matrix(runif(500*16), nrow=500, ncol=16)
X8_test <- matrix(runif(500*16), nrow=500, ncol=16)
X9_test <- matrix(runif(500*16), nrow=500, ncol=16)
X10_test <- matrix(runif(500*15), nrow=500, ncol=15)
Xif_test <- matrix(runif(500*232), nrow=500, ncol=232)
y_test <- matrix(runif(500), nrow=500, ncol=1)
## Not run: 
evaluate_model(fitted_model,X1_test,X2_test,X3_test,X4_test,X5_test,X6_test,
X7_test,X8_test,X9_test,X10_test,Xif_test,y_test,32)

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