NonEiV for energy effiency and concrete strength added

Both reaching similar RMSEs as in MC Dropout paper.
Updated hidden_layers handling in all training files.

Experiments/evaluate_energy.py

+import os
+import numpy as np
+import torch
+import torch.backends.cudnn
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard.writer import SummaryWriter
+
+from EIVArchitectures import Networks, initialize_weights
+from EIVData.energy_efficiency import load_data
+from EIVTrainingRoutines import train_and_store, loss_functions
+
+from train_noneiv_energy import p, init_std_y_list, seed_list, unscaled_reg, hidden_layers
+
+
+train_data, test_data = load_data()
+test_dataloader = DataLoader(test_data, batch_size=int(np.max((len(test_data), 800))))
+
+seed = seed_list[0]
+init_std_y = init_std_y_list[0]
+saved_file = os.path.join('saved_networks',
+            f'noneiv_energy'\
+                    f'init_std_y_{init_std_y:.3f}_ureg_{unscaled_reg:.1f}'\
+                    f'_p_{p:.2f}_seed_{seed}.pkl')
+
+input_dim = train_data[0][0].numel()
+output_dim = train_data[0][1].numel()
+net = Networks.FNNBer(p=p, init_std_y=init_std_y,
+        h=[input_dim, *hidden_layers, output_dim])
+train_and_store.open_stored_training(saved_file=saved_file,
+        net=net)
+
+
+# RMSE
+x,y = next(iter(test_dataloader))
+out = net(x)[0]
+if len(y.shape) <=1:
+    y = y.view((-1,1))
+assert y.shape == out.shape
+res = y-out
+scale = train_data.dataset.std_labels
+scaled_res = res * scale.view((1,-1))
+scaled_res = scaled_res.detach().cpu().numpy().flatten()
+rmse = np.sqrt(np.mean(scaled_res**2)) 
+print(f'RMSE {rmse:.3f}')
+

Experiments/train_noneiv_carlifornia.py → Experiments/train_noneiv_california.py

@@ -27,6 +27,7 @@ lr_update = 20
 epoch_offset = 10
 init_std_y_list = [0.5]
 gamma = 0.5
+hidden_layers = [1024, 1024, 1024, 1024]
 device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')
 
 # reproducability
@@ -107,7 +108,7 @@ def train_on_data(init_std_y, seed):
     output_dim = train_data[0][1].numel()
     net = Networks.FNNBer(p=p,
             init_std_y=init_std_y,
-            h=[input_dim, 1024, 1024, 1024, 1024, output_dim])
+            h=[input_dim, *hidden_layers, output_dim])
     net.apply(initialize_weights.glorot_init)
     net = net.to(device)
     net.std_y_par.requires_grad = False

Experiments/train_noneiv_concrete.py

+"""
+Train non-EiV model on concrete strength dataset using different seeds
+"""
+import random
+import os
+
+import numpy as np
+import torch
+import torch.backends.cudnn
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard.writer import SummaryWriter
+
+from EIVArchitectures import Networks, initialize_weights
+from EIVData.concrete_strength import load_data
+from EIVTrainingRoutines import train_and_store, loss_functions
+
+# hyperparameters
+lr = 1e-3
+batch_size = 32
+test_batch_size = 800
+number_of_epochs = 100
+unscaled_reg = 10
+report_point = 5
+p = 0.2
+lr_update = 20
+# pretraining = 300
+epoch_offset = 10
+init_std_y_list = [0.5]
+gamma = 0.5
+hidden_layers = [1024, 1024, 1024, 1024]
+device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')
+
+# reproducability
+def set_seeds(seed):
+    torch.backends.cudnn.benchmark = False
+    np.random.seed(seed)
+    random.seed(seed) 
+    torch.manual_seed(seed)
+seed_list = [0,]
+
+# to store the RMSE
+rmse_chain = []
+
+class UpdatedTrainEpoch(train_and_store.TrainEpoch):
+    def pre_epoch_update(self, net, epoch):
+        """
+        Overwrites the corresponding method
+        """
+        if epoch == 0:
+            self.lr = self.initial_lr
+            self.optimizer = torch.optim.Adam(net.parameters(), lr=self.lr)
+            self.lr_scheduler = torch.optim.lr_scheduler.StepLR(
+            self.optimizer, lr_update, gamma)
+
+
+    def post_epoch_update(self, net, epoch):
+        """
+        Overwrites the corresponding method
+        """
+        if epoch >= epoch_offset:
+            net.std_y_par.requires_grad = True
+        self.lr_scheduler.step() 
+
+    def extra_report(self, net, i):
+        """
+        Overwrites the corresponding method
+        and fed after initialization of this class
+        """
+        rmse = self.rmse(net).item()
+        rmse_chain.append(rmse)
+        writer.add_scalar('RMSE', rmse, self.total_count)
+        writer.add_scalar('train loss', self.last_train_loss, self.total_count)
+        writer.add_scalar('test loss', self.last_test_loss, self.total_count)
+        print(f'RMSE {rmse:.3f}')
+
+    def rmse(self, net):
+        """
+        Compute the root mean squared error for `net`
+        """
+        net_train_state = net.training
+        net.eval()
+        x, y = next(iter(self.test_dataloader))
+        if len(y.shape) <= 1:
+            y = y.view((-1,1))
+        out = net(x.to(device))[0].detach().cpu()
+        assert out.shape == y.shape
+        if net_train_state:
+            net.train()
+        return torch.sqrt(torch.mean((out-y)**2))
+
+def train_on_data(init_std_y, seed):
+    """
+    Sets `seed`, loads data and trains an Bernoulli Modell, starting with
+    `init_std_y`.
+    """
+    # set seed
+    set_seeds(seed)
+    # load Datasets
+    train_data, test_data = load_data(seed=seed, splitting_part=0.8,
+            normalize=True)
+    # make dataloaders
+    train_dataloader = DataLoader(train_data, batch_size=batch_size, 
+            shuffle=True)
+    test_dataloader = DataLoader(test_data, batch_size=test_batch_size,
+            shuffle=True)
+    # create a net
+    input_dim = train_data[0][0].numel()
+    output_dim = train_data[0][1].numel()
+    net = Networks.FNNBer(p=p,
+            init_std_y=init_std_y,
+            h=[input_dim, *hidden_layers, output_dim])
+    net.apply(initialize_weights.glorot_init)
+    net = net.to(device)
+    net.std_y_par.requires_grad = False
+    std_x_map = lambda: 0.0
+    std_y_map = lambda: net.get_std_y().detach().cpu().item()
+    # regularization
+    reg = unscaled_reg/len(train_data)
+    # create epoch_map
+    criterion = loss_functions.nll_reg_loss
+    epoch_map = UpdatedTrainEpoch(train_dataloader=train_dataloader,
+            test_dataloader=test_dataloader,
+            criterion=criterion, std_y_map=std_y_map, std_x_map=std_x_map,
+            lr=lr, reg=reg, report_point=report_point, device=device)
+    # run and save
+    save_file = os.path.join('saved_networks',
+            f'noneiv_concrete'\
+                    f'init_std_y_{init_std_y:.3f}_ureg_{unscaled_reg:.1f}'\
+                    f'_p_{p:.2f}_seed_{seed}.pkl')
+    train_and_store.train_and_store(net=net, 
+            epoch_map=epoch_map,
+            number_of_epochs=number_of_epochs,
+            save_file=save_file)
+    
+
+if __name__ == '__main__':
+    for seed in seed_list:
+        # Tensorboard monitoring
+        writer = SummaryWriter(log_dir=f'/home/martin09/tmp/tensorboard/'\
+                f'run_noneiv_concrete_lr_{lr:.4f}_seed'\
+                f'_{seed}_uregu_{unscaled_reg:.1f}_p_{p:.2f}')
+        print(f'>>>>SEED: {seed}')
+        for init_std_y in init_std_y_list:
+            print(f'Using init_std_y={init_std_y:.3f}')
+            train_on_data(init_std_y, seed)
+
+

Experiments/train_noneiv_energy.py

+"""
+Train non-EiV model on energy efficiency dataset using different seeds
+"""
+import random
+import os
+
+import numpy as np
+import torch
+import torch.backends.cudnn
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard.writer import SummaryWriter
+
+from EIVArchitectures import Networks, initialize_weights
+from EIVData.energy_efficiency import load_data
+from EIVTrainingRoutines import train_and_store, loss_functions
+
+# hyperparameters
+lr = 1e-3
+batch_size = 32
+test_batch_size = 600
+number_of_epochs = 300
+unscaled_reg = 10
+report_point = 5
+p = 0.2
+lr_update = 50
+# pretraining = 300
+epoch_offset = 50
+init_std_y_list = [0.5]
+gamma = 0.5
+hidden_layers = [1024, 1024, 1024, 1024]
+device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')
+
+# reproducability
+def set_seeds(seed):
+    torch.backends.cudnn.benchmark = False
+    np.random.seed(seed)
+    random.seed(seed) 
+    torch.manual_seed(seed)
+seed_list = [0,]
+
+# to store the RMSE
+rmse_chain = []
+
+class UpdatedTrainEpoch(train_and_store.TrainEpoch):
+    def pre_epoch_update(self, net, epoch):
+        """
+        Overwrites the corresponding method
+        """
+        if epoch == 0:
+            self.lr = self.initial_lr
+            self.optimizer = torch.optim.Adam(net.parameters(), lr=self.lr)
+            self.lr_scheduler = torch.optim.lr_scheduler.StepLR(
+            self.optimizer, lr_update, gamma)
+
+
+    def post_epoch_update(self, net, epoch):
+        """
+        Overwrites the corresponding method
+        """
+        if epoch >= epoch_offset:
+            net.std_y_par.requires_grad = True
+        self.lr_scheduler.step() 
+
+    def extra_report(self, net, i):
+        """
+        Overwrites the corresponding method
+        and fed after initialization of this class
+        """
+        rmse = self.rmse(net).item()
+        rmse_chain.append(rmse)
+        writer.add_scalar('RMSE', rmse, self.total_count)
+        writer.add_scalar('train loss', self.last_train_loss, self.total_count)
+        writer.add_scalar('test loss', self.last_test_loss, self.total_count)
+        print(f'RMSE {rmse:.3f}')
+
+    def rmse(self, net):
+        """
+        Compute the root mean squared error for `net`
+        """
+        net_train_state = net.training
+        net.eval()
+        x, y = next(iter(self.test_dataloader))
+        if len(y.shape) <= 1:
+            y = y.view((-1,1))
+        out = net(x.to(device))[0].detach().cpu()
+        assert out.shape == y.shape
+        if net_train_state:
+            net.train()
+        return torch.sqrt(torch.mean((out-y)**2))
+
+def train_on_data(init_std_y, seed):
+    """
+    Sets `seed`, loads data and trains an Bernoulli Modell, starting with
+    `init_std_y`.
+    """
+    # set seed
+    set_seeds(seed)
+    # load Datasets
+    train_data, test_data = load_data(seed=seed, splitting_part=0.8,
+            normalize=True)
+    # make dataloaders
+    train_dataloader = DataLoader(train_data, batch_size=batch_size, 
+            shuffle=True)
+    test_dataloader = DataLoader(test_data, batch_size=test_batch_size,
+            shuffle=True)
+    # create a net
+    input_dim = train_data[0][0].numel()
+    output_dim = train_data[0][1].numel()
+    net = Networks.FNNBer(p=p,
+            init_std_y=init_std_y,
+            h=[input_dim, *hidden_layers, output_dim])
+    net.apply(initialize_weights.glorot_init)
+    net = net.to(device)
+    net.std_y_par.requires_grad = False
+    std_x_map = lambda: 0.0
+    std_y_map = lambda: net.get_std_y().detach().cpu().item()
+    # regularization
+    reg = unscaled_reg/len(train_data)
+    # create epoch_map
+    criterion = loss_functions.nll_reg_loss
+    epoch_map = UpdatedTrainEpoch(train_dataloader=train_dataloader,
+            test_dataloader=test_dataloader,
+            criterion=criterion, std_y_map=std_y_map, std_x_map=std_x_map,
+            lr=lr, reg=reg, report_point=report_point, device=device)
+    # run and save
+    save_file = os.path.join('saved_networks',
+            f'noneiv_energy'\
+                    f'init_std_y_{init_std_y:.3f}_ureg_{unscaled_reg:.1f}'\
+                    f'_p_{p:.2f}_seed_{seed}.pkl')
+    train_and_store.train_and_store(net=net, 
+            epoch_map=epoch_map,
+            number_of_epochs=number_of_epochs,
+            save_file=save_file)
+    
+
+if __name__ == '__main__':
+    for seed in seed_list:
+        # Tensorboard monitoring
+        writer = SummaryWriter(log_dir=f'/home/martin09/tmp/tensorboard/'\
+                f'run_noneiv_energy_lr_{lr:.4f}_seed'\
+                f'_{seed}_uregu_{unscaled_reg:.1f}_p_{p:.2f}')
+        print(f'>>>>SEED: {seed}')
+        for init_std_y in init_std_y_list:
+            print(f'Using init_std_y={init_std_y:.3f}')
+            train_on_data(init_std_y, seed)
+
+