diff --git a/dynamight/deformations/__pycache__/optimize_deformations.cpython-310.pyc b/dynamight/deformations/__pycache__/optimize_deformations.cpython-310.pyc
index 5d60935..7817e22 100644
Binary files a/dynamight/deformations/__pycache__/optimize_deformations.cpython-310.pyc and b/dynamight/deformations/__pycache__/optimize_deformations.cpython-310.pyc differ
diff --git a/dynamight/deformations/optimize_deformations.py b/dynamight/deformations/optimize_deformations.py
index b4d7fc5..ab93f49 100644
--- a/dynamight/deformations/optimize_deformations.py
+++ b/dynamight/deformations/optimize_deformations.py
@@ -176,7 +176,7 @@ def optimize_deformations(
                 particle_dataset, val_indices)
             lambda_regularization_half1 = cp['regularization_parameter_h1']
             lambda_regularization_half1 = cp['regularization_parameter_h2']
-            n_warmup_epochs = 0
+            n_warmup_epochs = n_warmup_epochs
             half1_indices = inds_half1
             half2_indices = inds_half2
             print('continuing training from a given checkpoint file')
@@ -283,6 +283,25 @@ def optimize_deformations(
             encoder_half1, encoder_half2, decoder_half1, decoder_half2 = load_models(
                 checkpoint_file, device, box_size, n_classes
             )
+            decoder_half1.model_positions = torch.nn.Parameter(
+                initial_points.to(device), requires_grad=True)
+            decoder_half2.model_positions = torch.nn.Parameter(
+                initial_points.to(device), requires_grad=True)
+            decoder_half1.amp = torch.nn.Parameter(
+                50 * torch.ones(n_classes, n_points).to(device), requires_grad=False
+            )
+            decoder_half1.ampvar = torch.nn.Parameter(
+                torch.randn(n_classes, n_points).to(device), requires_grad=True
+            )
+            decoder_half2.amp = torch.nn.Parameter(
+                50 * torch.ones(n_classes, n_points).to(device), requires_grad=False
+            )
+            decoder_half2.ampvar = torch.nn.Parameter(
+                torch.randn(n_classes, n_points).to(device), requires_grad=True
+            )
+            decoder_half1.n_points = n_points
+            decoder_half2.n_points = n_points
+
         else:
             encoder_half1 = HetEncoder(box_size, latent_dim, 1).to(device)
             encoder_half2 = HetEncoder(box_size, latent_dim, 1).to(device)
@@ -305,33 +324,33 @@ def optimize_deformations(
             decoder_half1 = DisplacementDecoder(**decoder_kwargs).to(device)
             decoder_half2 = DisplacementDecoder(**decoder_kwargs).to(device)
 
-            if initialization_mode == ConsensusInitializationMode.MAP:
-                with mrcfile.open(initial_model) as mrc:
-                    Ivol = torch.tensor(mrc.data)
-                    fits = False
-                    while fits == False:
-                        try:
-                            for decoder in (decoder_half1, decoder_half2):
-                                decoder.initialize_physical_parameters(
-                                    reference_volume=Ivol)
-                                summ.add_figure("Data/cons_points_z_half1",
-                                                tensor_scatter(decoder_half1.model_positions[:, 0],
-                                                               decoder_half1.model_positions[:, 1], c=torch.ones(decoder_half1.model_positions.shape[0]), s=3), -1)
-                                summ.add_figure("Data/cons_points_z_half2",
-                                                tensor_scatter(decoder_half2.model_positions[:, 0],
-                                                               decoder_half2.model_positions[:, 1], c=torch.ones(decoder_half2.model_positions.shape[0]), s=3), -1)
-                            fits = True
-                            print('consensus gaussian models initialized')
-                            torch.cuda.empty_cache()
-                        except Exception as error:
-                            torch.cuda.empty_cache()
-                            print(
-                                'volume too large: change size of output volumes. (If you want the original box size for the output volumes use a bigger gpu.', error)
-                            Ivol = torch.nn.functional.avg_pool3d(
-                                Ivol[None, None], (2, 2, 2))
-                            Ivol = Ivol[0, 0]
-                            decoder_half1.vol_box = decoder_half1.vol_box//2
-                            decoder_half2.vol_box = decoder_half2.vol_box//2
+        if initialization_mode == ConsensusInitializationMode.MAP:
+            with mrcfile.open(initial_model) as mrc:
+                Ivol = torch.tensor(mrc.data)
+                fits = False
+                while fits == False:
+                    try:
+                        for decoder in (decoder_half1, decoder_half2):
+                            decoder.initialize_physical_parameters(
+                                reference_volume=Ivol)
+                            summ.add_figure("Data/cons_points_z_half1",
+                                            tensor_scatter(decoder_half1.model_positions[:, 0],
+                                                           decoder_half1.model_positions[:, 1], c=torch.ones(decoder_half1.model_positions.shape[0]), s=3), -1)
+                            summ.add_figure("Data/cons_points_z_half2",
+                                            tensor_scatter(decoder_half2.model_positions[:, 0],
+                                                           decoder_half2.model_positions[:, 1], c=torch.ones(decoder_half2.model_positions.shape[0]), s=3), -1)
+                        fits = True
+                        print('consensus gaussian models initialized')
+                        torch.cuda.empty_cache()
+                    except Exception as error:
+                        torch.cuda.empty_cache()
+                        print(
+                            'volume too large: change size of output volumes. (If you want the original box size for the output volumes use a bigger gpu.', error)
+                        Ivol = torch.nn.functional.avg_pool3d(
+                            Ivol[None, None], (2, 2, 2))
+                        Ivol = Ivol[0, 0]
+                        decoder_half1.vol_box = decoder_half1.vol_box//2
+                        decoder_half2.vol_box = decoder_half2.vol_box//2
 
             if mask_file:
                 with mrcfile.open(mask_file) as mrc: