Speeded up training on linear dataset

EIVPackage/EIVData/linear.py

@@ -2,11 +2,11 @@ import torch
 import sys
 from torch.utils.data import TensorDataset, random_split
 
-total_number_of_datapoints = 10000
+total_number_of_datapoints = 2000
 input_range = [-1,1]
 slope = 1.0
 intercept = 0.0
-x_noise_strength = 0.1
+x_noise_strength = 0.05
 y_noise_strength = 0.1
 
 def get_normalization(*args):

Experiments/configurations/eiv_linear.json

@@ -2,7 +2,7 @@
 	"long_dataname": "linear",
 	"short_dataname": "linear",
 	"lr": 1e-3,
-	"batch_size": 100,
+	"batch_size": 64,
 	"test_batch_size": 800,
 	"number_of_epochs": 100,
 	"unscaled_reg": 10,
@@ -14,8 +14,8 @@
 	"eiv_prediction_number_of_batches": 10,
 	"init_std_y_list": [0.5],
 	"gamma": 0.5,
-	"hidden_layers": [1024, 1024, 1024, 1024],
-	"fixed_std_x": 0.1,
+	"hidden_layers": [128, 128, 128, 128],
+	"fixed_std_x": 0.05,
 	"seed_range": [0,10],
 	"gpu_number": 1
 }

Experiments/configurations/noneiv_linear.json

@@ -2,7 +2,7 @@
 	"long_dataname": "linear",
 	"short_dataname": "linear",
 	"lr": 1e-3,
-	"batch_size": 100,
+	"batch_size": 64,
 	"test_batch_size": 800,
 	"number_of_epochs": 100,
 	"unscaled_reg": 10,
@@ -14,7 +14,7 @@
 	"noneiv_prediction_number_of_batches": 10,
 	"init_std_y_list": [0.5],
 	"gamma": 0.5,
-	"hidden_layers": [1024, 1024, 1024, 1024],
+	"hidden_layers": [128, 128, 128, 128],
 	"seed_range": [0,10],
 	"gpu_number": 1
 }