Problem running the best model (Feed-Forward Neural Network) after hyperparameter optimization

[mcvta]

Hi everyone,

I´m running the Feed-Forward Neural Network (FNN) with R (4.1.2) and tensorflow (2.7.0) that is available from: https://github.com/MoritzFeigl/wateRtemp.

I´m running the test dataset that is available from the same source. After the optimization process when the model tries to run the best model I´m getting the following error:

** Starting FNN computation for catchment test_catchment *** Mean and standard deviation used for feature scaling are saved under test_catchment/FNN/standard_FNN/scaling_values.csv Using existing scores as initial grid for the Bayesian Optimization Bayesian Hyperparameter Optimization: 40 iterations were already computed Run the best performing model as ensemble: 2022-02-02 13:06:51.059425: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2022-02-02 13:06:51.060834: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. Loaded Tensorflow version 2.7.0 Error in py_call_impl(callable, dots$args, dots$keywords) : TypeError: Exception encountered when calling layer "alpha_dropout" (type AlphaDropout).

'>' not supported between instances of 'dict' and 'float'

Call arguments received: • inputs=tf.Tensor(shape=(None, 42), dtype=float32) • training=None In addition: Warning message: In if (dropout_layers) { : the condition has length > 1 and only the first element will be used

This is the code that I´m using to run the model:

library("wateRtemp")
library(tensorflow)
data(test_catchment)

wt_preprocess(test_catchment)
train_data <- feather::read_feather("test_catchment/train_data.feather")
test_data <- feather::read_feather("test_catchment/test_data.feather")

wt_fnn(
  train_data,
  test_data = NULL,
  catchment = NULL,
  model_name = NULL,
  seed = NULL,
  n_iter = 40,
  n_random_initial_points = 20,
  epochs = 100,
  early_stopping_patience = 5,
  ensemble_runs = 5,
  bounds_layers = c(1, 5),
  bounds_units = c(5, 200),
  bounds_dropout = c(0, 0.2),
  bounds_batch_size = c(5, 150),
  initial_grid_from_model_scores = TRUE
)

wt_fnn(train_data,test_data,catchment = "test_catchment", seed = 42, model_name = "standard_FNN")

This are the parameters for the best model: layers = 3 units = 200 max_epoc = 100 early_stopping_patience = 5 batch_size = 60 dropout = 2.22044604925031E-16 ensemble =1

Can this problem be related with the very small value of dropout =2.22044604925031E-16 ?

Thank you

[t-kalinowski]

This seems to be a bug in the wateRtemp package. Can you file an issue there?

I also just pushed a commit to keras simplifying the layer_alpha_dropout() wrapper, but it won't fix this bug.

[mcvta]

Hi I have already done that:

https://github.com/MoritzFeigl/wateRtemp/issues/1

Can you run the the FNN (wt_fnn) with the test dataset that is provided here: https://github.com/MoritzFeigl/wateRtemp

Just to check if it is a bug in the original code

Thank you,