Updated training parameters for simulated data

EIVPackage/EIVData/cubic.py

@@ -5,11 +5,11 @@ from torch.utils.data import TensorDataset
 from EIVGeneral.manipulate_tensors import add_noise, normalize_tensor,\
         unnormalize_tensor
 
-total_number_of_datapoints = 2000
+total_number_of_datapoints = 500
 input_range = [-4,4]
 slope = 1.0
 intercept = 0.0
-x_noise_strength = 0.05 * (input_range[1] - input_range[0])/2
+x_noise_strength = 0.2 
 y_noise_strength = 3
 func = lambda true_x: slope * true_x**3 + intercept 
 

EIVPackage/EIVData/linear.py

@@ -5,11 +5,11 @@ from torch.utils.data import TensorDataset
 from EIVGeneral.manipulate_tensors import add_noise, normalize_tensor,\
         unnormalize_tensor
 
-total_number_of_datapoints = 2000
+total_number_of_datapoints = 500
 input_range = [-1,1]
 slope = 1.0
 intercept = 0.0
-x_noise_strength = 0.05
+x_noise_strength = 0.1
 y_noise_strength = 0.1
 func = lambda true_x: slope * true_x + intercept 
 

EIVPackage/EIVData/quadratic.py

@@ -5,11 +5,11 @@ from torch.utils.data import TensorDataset
 from EIVGeneral.manipulate_tensors import add_noise, normalize_tensor,\
         unnormalize_tensor
 
-total_number_of_datapoints = 2000
+total_number_of_datapoints = 500
 input_range = [-1,1]
 slope = 1.0
 intercept = 0.0
-x_noise_strength = 0.05
+x_noise_strength = 0.1
 y_noise_strength = 0.1
 func = lambda true_x: slope * true_x**2 + intercept 
 

EIVPackage/EIVData/sine.py

@@ -9,7 +9,7 @@ total_number_of_datapoints = 2000
 input_range = [-0.2,0.8]
 intercept = 0.0
 x_noise_strength = 0.02 
-y_noise_strength = 0.05
+y_noise_strength = 0.02
 func = lambda true_x: true_x +\
             torch.sin(2 * torch.pi * true_x) +\
             torch.sin(4 * torch.pi * true_x)

Experiments/configurations/eiv_cubic.json

@@ -3,7 +3,7 @@
 	"short_dataname": "cubic",
 	"normalize": false,
 	"lr": 1e-3,
-	"batch_size": 64,
+	"batch_size": 16,
 	"test_batch_size": 800,
 	"number_of_epochs": 100,
 	"unscaled_reg": 10,
@@ -16,7 +16,7 @@
 	"init_std_y_list": [0.5],
 	"gamma": 0.5,
 	"hidden_layers": [128, 128, 128, 128],
-	"fixed_std_x": 0.05,
+	"fixed_std_x": 0.20,
 	"seed_range": [0,10],
 	"gpu_number": 1
 }

Experiments/configurations/eiv_linear.json

@@ -3,7 +3,7 @@
 	"short_dataname": "linear",
 	"normalize": false,
 	"lr": 1e-3,
-	"batch_size": 64,
+	"batch_size": 16,
 	"test_batch_size": 800,
 	"number_of_epochs": 100,
 	"unscaled_reg": 10,
@@ -16,7 +16,7 @@
 	"init_std_y_list": [0.5],
 	"gamma": 0.5,
 	"hidden_layers": [128, 128, 128, 128],
-	"fixed_std_x": 0.05,
+	"fixed_std_x": 0.10,
 	"seed_range": [0,10],
 	"gpu_number": 1
 }

Experiments/configurations/eiv_quadratic.json

@@ -3,7 +3,7 @@
 	"short_dataname": "quadratic",
 	"normalize": false,
 	"lr": 1e-3,
-	"batch_size": 64,
+	"batch_size": 16,
 	"test_batch_size": 800,
 	"number_of_epochs": 100,
 	"unscaled_reg": 10,
@@ -16,7 +16,7 @@
 	"init_std_y_list": [0.5],
 	"gamma": 0.5,
 	"hidden_layers": [128, 128, 128, 128],
-	"fixed_std_x": 0.05,
+	"fixed_std_x": 0.10,
 	"seed_range": [0,10],
 	"gpu_number": 1
 }

Experiments/configurations/eiv_sine.json

@@ -3,7 +3,7 @@
 	"short_dataname": "sine",
 	"normalize": false,
 	"lr": 1e-3,
-	"batch_size": 64,
+	"batch_size": 16,
 	"test_batch_size": 800,
 	"number_of_epochs": 100,
 	"unscaled_reg": 10,

Experiments/configurations/noneiv_cubic.json

@@ -3,7 +3,7 @@
 	"short_dataname": "cubic",
 	"normalize": false,
 	"lr": 1e-3,
-	"batch_size": 64,
+	"batch_size": 16,
 	"test_batch_size": 800,
 	"number_of_epochs": 100,
 	"unscaled_reg": 10,

Experiments/configurations/noneiv_linear.json

@@ -3,7 +3,7 @@
 	"short_dataname": "linear",
 	"normalize": false,
 	"lr": 1e-3,
-	"batch_size": 64,
+	"batch_size": 16,
 	"test_batch_size": 800,
 	"number_of_epochs": 100,
 	"unscaled_reg": 10,

Experiments/configurations/noneiv_quadratic.json

@@ -3,7 +3,7 @@
 	"short_dataname": "quadratic",
 	"normalize": false,
 	"lr": 1e-3,
-	"batch_size": 64,
+	"batch_size": 16,
 	"test_batch_size": 800,
 	"number_of_epochs": 100,
 	"unscaled_reg": 10,

Experiments/configurations/noneiv_sine.json

@@ -3,7 +3,7 @@
 	"short_dataname": "sine",
 	"normalize": false,
 	"lr": 1e-3,
-	"batch_size": 64,
+	"batch_size": 16,
 	"test_batch_size": 800,
 	"number_of_epochs": 100,
 	"unscaled_reg": 10,

Experiments/plot_prediction.py

@@ -216,9 +216,8 @@ def compute_predictions_and_uncertainties(data, x_range, eiv, number_of_draws,
     return plotting_dictionary
 
 
-data_list = ['cubic'] # short datanames
-#TODO: Check which ranges are "correct"
-list_x_range = [torch.linspace(-2.5,2.5, 50)]
+data_list = ['sine'] # short datanames
+list_x_range = [torch.linspace(-0.3,0.9, 50)]
 list_color = [('red','blue')]
 list_number_of_draws = [((100,5), 100)]
 for i, (data, x_range, color, number_of_draws) in enumerate(zip(data_list,
@@ -258,5 +257,4 @@ for i, (data, x_range, color, number_of_draws) in enumerate(zip(data_list,
         pass
 
 
-plt.show()    
-
+plt.show()