| Type: | Package |
| Title: | Longitudinal Data Neural Network |
| Version: | 1.10 |
| Date: | 2020-11-25 |
| Author: | Vasileios Karapoulios |
| Maintainer: | Vasileios Karapoulios <billkarap123@gmail.com> |
| Description: | This is a Neural Network regression model implementation using 'Keras', consisting of 10 Long Short-Term Memory layers that are fully connected along with the rest of the inputs. |
| License: | GPL-2 | GPL-3 [expanded from: GNU General Public License] |
| RoxygenNote: | 7.1.1 |
| LazyData: | true |
| Encoding: | UTF-8 |
| Imports: | devtools, reticulate, tensorflow |
| Depends: | keras |
| Suggests: | testthat,knitr,rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2020-11-30 11:10:50 UTC; Waler |
| Repository: | CRAN |
| Date/Publication: | 2020-12-02 10:30:10 UTC |
Pre-defined Neural Network for Longitudinal Data
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). |
|
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.
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.
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
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.