RNN dropout 35 4

job.py

@@ -11,8 +11,8 @@ def makejob(commit_id, model, nruns, time_wall):
 #SBATCH --nodes=1
 #SBATCH --partition=gpu_prod_night
 #SBATCH --time={time_wall}
-#SBATCH --output=logslurms/slurm-%A_%a.out
-#SBATCH --error=logslurms/slurm-%A_%a.err
+#SBATCH --output=logslurms/slurm-{model}%A_%a.out
+#SBATCH --error=logslurms/slurm-{model}%A_%a.err
 #SBATCH --array=0-{nruns}
 
 
@@ -66,7 +66,7 @@ parser = argparse.ArgumentParser()
 
 parser.add_argument("--time_wall",
                     default="no_limit",
-                    help="Time wall. Choose in [no-limit, hour, half, quarter]")
+                    help="Time wall. Choose in [no_limit, hour, half, quarter]")
 
 parser.add_argument("--model_name",
                     default ="Bi-LSTM",

logs/main_unit_test.log

@@ -1333,3 +1333,30 @@ INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and
 INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
 INFO:root:  - The train fold has 542473 samples
 INFO:root:  - The valid fold has 134687 samples
+INFO:root:= Dataloaders for mean and standard deviation
+INFO:root:  - Dataset creation
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Generating the index
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root:  - Loaded a dataset with 677160 samples
+INFO:root:  - Splitting the data in training and validation sets
+INFO:root:Generating the subset files from 677160 samples
+INFO:root:  - Subset dataset
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root:  - The train fold has 541965 samples
+INFO:root:= Dataloaders
+INFO:root:  - Dataset creation
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Generating the index
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root:  - Loaded a dataset with 677160 samples
+INFO:root:  - Splitting the data in training and validation sets
+INFO:root:Generating the subset files from 677160 samples
+INFO:root:  - Subset dataset
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root:  - The train fold has 541837 samples
+INFO:root:  - The valid fold has 135323 samples

model.py

@@ -43,9 +43,15 @@ class RNN(nn.Module):
         self.hidden_size = cfg["RNN"]["HiddenSize"]
         self.num_layers = cfg["RNN"]["NumLayers"]
         # RNN
-        self.rnn = nn.RNN(input_size, self.hidden_size, self.num_layers, batch_first=True, nonlinearity='relu')
+        self.rnn = nn.Sequential(
+            nn.RNN(input_size, self.hidden_size, self.num_layers, batch_first=True, nonlinearity='relu'),
+            nn.Dropout(p=0.2)
+        )
         
-        self.fc = nn.Linear(self.hidden_size, 1)
+        self.fc = nn.Sequential(
+            nn.Linear(self.hidden_size, 1),
+            nn.Dropout(p=0.2)
+        )
     
     def forward(self, x):
         use_cuda = torch.cuda.is_available()