diff --git a/.gitignore b/.gitignore
index 04b95f7..473201b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,7 @@
 
 # User-specific stuff
 .idea/**
+res/**
 
 # CMake
 cmake-build-*/
diff --git a/datasets/mnist.py b/datasets/mnist.py
new file mode 100644
index 0000000..66fe06f
--- /dev/null
+++ b/datasets/mnist.py
@@ -0,0 +1,29 @@
+from torchvision.datasets import MNIST
+import numpy as np
+
+
+class MyMNIST(MNIST):
+
+    @property
+    def map_shapes_max(self):
+        return np.asarray(self.test_dataset[0][0]).shape
+
+    def __init__(self, *args, **kwargs):
+        super(MyMNIST, self).__init__('res', train=False, download=True)
+        pass
+
+    def __getitem__(self, item):
+        image = super(MyMNIST, self).__getitem__(item)
+        return np.expand_dims(np.asarray(image[0]), axis=0).astype(np.float32), image[1]
+
+    @property
+    def train_dataset(self):
+        return self.__class__('res', train=True, download=True)
+
+    @property
+    def test_dataset(self):
+        return self.__class__('res', train=False, download=True)
+
+    @property
+    def val_dataset(self):
+        return self.__class__('res', train=False, download=True)
diff --git a/datasets/trajectory_dataset.py b/datasets/trajectory_dataset.py
index 2b62a24..f48113c 100644
--- a/datasets/trajectory_dataset.py
+++ b/datasets/trajectory_dataset.py
@@ -1,6 +1,9 @@
 import shelve
+from collections import defaultdict
 from pathlib import Path
-from typing import Union, List
+from typing import Union
+
+from torchvision.transforms import Normalize
 
 import multiprocessing as mp
 
@@ -24,16 +27,17 @@ class TrajDataShelve(Dataset):
         return self[0][0].shape
 
     def __init__(self, file_path, **kwargs):
+        assert Path(file_path).exists()
         super(TrajDataShelve, self).__init__()
         self._mutex = mp.Lock()
         self.file_path = str(file_path)
 
-
     def __len__(self):
         self._mutex.acquire()
         with shelve.open(self.file_path) as d:
             length = len(d)
-            self._mutex.release()
+            d.close()
+        self._mutex.release()
         return length
 
     def seed(self):
@@ -43,12 +47,20 @@ class TrajDataShelve(Dataset):
         self._mutex.acquire()
         with shelve.open(self.file_path) as d:
             sample = d[str(item)]
-            self._mutex.release()
+            d.close()
+        self._mutex.release()
         return sample
 
 
 class TrajDataset(Dataset):
 
+    @property
+    def _last_label_init(self):
+        d = defaultdict(lambda: -1)
+        d['generator_hom_all_in_map'] = V.ALTERNATIVE
+        d['generator_alt_all_in_map'] = V.HOMOTOPIC
+        return d[self.mode]
+
     @property
     def map_shape(self):
         return self.map.as_array.shape
@@ -57,17 +69,18 @@ class TrajDataset(Dataset):
                  length=100000, mode='separated_arrays', embedding_size=None, preserve_equal_samples=False,
                  **kwargs):
         super(TrajDataset, self).__init__()
-        assert mode.lower() in ['generator_all_in_map', 'generator_hom_all_in_map'
-                                                        'classifier_all_in_map']
-        self.normalized = normalized
+        assert mode.lower() in ['generator_all_in_map', 'generator_hom_all_in_map', 'generator_alt_all_in_map',
+                                'ae_no_label_in_map',
+                                'generator_alt_no_label_in_map', 'classifier_all_in_map', 'vae_no_label_in_map']
+        self.normalize = Normalize(0.5, 0.5) if normalized else lambda x: x
         self.preserve_equal_samples = preserve_equal_samples
         self.mode = mode
         self.mapname = mapname if mapname.endswith('.bmp') else f'{mapname}.bmp'
         self.maps_root = maps_root
         self._len = length
-        self.last_label = V.ALTERNATIVE if 'hom' in self.mode else choice([-1, V.ALTERNATIVE, V.HOMOTOPIC])
+        self.last_label = self._last_label_init
 
-        self.map = Map(self.mapname).from_image(self.maps_root / self.mapname, embedding_size=embedding_size)
+        self.map = Map.from_image(self.maps_root / self.mapname, embedding_size=embedding_size)
 
     def __len__(self):
         return self._len
@@ -82,6 +95,7 @@ class TrajDataset(Dataset):
             map_array = torch.as_tensor(self.map.as_array).float()
             return (map_array, trajectory_space), label
 
+        # Produce an alternative.
         while True:
             trajectory = self.map.get_random_trajectory()
             alternative = self.map.generate_alternative(trajectory)
@@ -91,18 +105,19 @@ class TrajDataset(Dataset):
             else:
                 break
 
-        self.last_label = label if self.mode != ['generator_hom_all_in_map'] else V.ALTERNATIVE
-        if self.mode.lower() in ['classifier_all_in_map', 'generator_all_in_map']:
+        self.last_label = label if self._last_label_init == V.ANY else self._last_label_init[self.mode]
+        if 'in_map' in self.mode.lower():
             map_array = self.map.as_array
             trajectory = trajectory.draw_in_array(self.map_shape)
             alternative = alternative.draw_in_array(self.map_shape)
             label_as_array = np.full_like(map_array, label)
-            if self.normalized:
-                map_array = map_array / V.WHITE
-                trajectory = trajectory / V.WHITE
-                alternative = alternative / V.WHITE
+
             if self.mode == 'generator_all_in_map':
                 return np.concatenate((map_array, trajectory, label_as_array)), alternative
+            elif self.mode in ['vae_no_label_in_map', 'ae_no_label_in_map']:
+                return np.sum((map_array, trajectory, alternative), axis=0), 0
+            elif self.mode in ['generator_alt_no_label_in_map', 'generator_hom_no_label_in_map']:
+                return np.concatenate((map_array, trajectory)), alternative
             elif self.mode == 'classifier_all_in_map':
                 return np.concatenate((map_array, trajectory, alternative)), label
 
@@ -119,13 +134,13 @@ class TrajDataset(Dataset):
 class TrajData(object):
     @property
     def map_shapes(self):
-        return [dataset.map_shape for dataset in self._train_dataset.datasets]
+        return [dataset.map_shape for dataset in self.train_dataset.datasets]
 
     @property
     def map_shapes_max(self):
         shapes = self.map_shapes
         shape_list = list(map(max, zip(*shapes)))
-        if '_all_in_map' in self.mode:
+        if '_all_in_map' in self.mode and not self.preprocessed:
             shape_list[0] += 2
         return shape_list
 
@@ -139,14 +154,13 @@ class TrajData(object):
         self.mode = mode
         self.maps_root = Path(map_root)
         self.length = length
-        self._test_dataset = self._load_datasets('train')
-        self._val_dataset = self._load_datasets('val')
-        self._train_dataset = self._load_datasets('test')
+        self.test_dataset = self._load_datasets('test')
+        self.val_dataset = self._load_datasets('val')
+        self.train_dataset = self._load_datasets('train')
 
     def _load_datasets(self, dataset_type=''):
 
         map_files = list(self.maps_root.glob('*.bmp'))
-        equal_split = int(self.length // len(map_files)) or 1
 
         # find max image size among available maps:
         max_map_size = (1, ) + tuple(reversed(tuple(map(max, *[Image.open(map_file).size for map_file in map_files]))))
@@ -156,10 +170,11 @@ class TrajData(object):
             preprocessed_map_names = [p.name for p in preprocessed_map_files]
             datasets = []
             for map_file in map_files:
-                new_pik_name = f'{dataset_type}_{str(map_file.name)[:-3]}.pik'
+                equal_split = int(self.length // len(map_files)) or 5
+                new_pik_name = f'{self.mode}_{map_file.name[:-4]}_{dataset_type}.pik'
                 if dataset_type != 'train':
-                    equal_split *= 0.01
-                if not [f'{new_pik_name[:-3]}.bmp' in preprocessed_map_names]:
+                    equal_split = max(int(equal_split * 0.01), 10)
+                if not new_pik_name in preprocessed_map_names:
                     traj_dataset = TrajDataset(maps_root=self.maps_root, mapname=map_file.name, length=equal_split,
                                                mode=self.mode, embedding_size=max_map_size, normalized=self.normalized,
                                                preserve_equal_samples=True)
@@ -168,6 +183,9 @@ class TrajData(object):
                 dataset = TrajDataShelve(map_file.parent / new_pik_name)
                 datasets.append(dataset)
             return ConcatDataset(datasets)
+
+        # Set the equal split so that all maps are visited with the same frequency
+        equal_split = int(self.length // len(map_files)) or 5
         return ConcatDataset([TrajDataset(maps_root=self.maps_root, mapname=map_file.name, length=equal_split,
                                           mode=self.mode, embedding_size=max_map_size, normalized=self.normalized,
                                           preserve_equal_samples=True)
@@ -185,29 +203,14 @@ class TrajData(object):
 
     def dump_n(self, file_path, traj_dataset: TrajDataset, n=100000):
         assert str(file_path).endswith('.pik')
-        processes = mp.cpu_count() - 1
         mutex = mp.Lock()
-        with mp.Pool(processes) as pool:
-            async_results = [pool.apply_async(traj_dataset.__getitem__, kwds=dict(item=i)) for i in range(n)]
+        for i in tqdm(range(n), total=n, desc=f'Generating {n} Samples'):
+            sample = traj_dataset[i]
+            mutex.acquire()
+            write_to_shelve(file_path, sample)
+            mutex.release()
 
-            for result_obj in tqdm(async_results, total=n, desc=f'Generating {n} Samples'):
-                sample = result_obj.get()
-                mutex.acquire()
-                write_to_shelve(file_path, sample)
-                mutex.release()
-        print(f'{n} samples sucessfully dumped to "{file_path}"!')
-
-    @property
-    def train_dataset(self):
-        return self._train_dataset
-
-    @property
-    def val_dataset(self):
-        return self._val_dataset
-
-    @property
-    def test_dataset(self):
-        return self._test_dataset
+        print(f'{n} samples successfully dumped to "{file_path}"!')
 
     def get_datasets(self):
         return self._train_dataset, self._val_dataset, self._test_dataset
diff --git a/lib/models/generators/cnn.py b/lib/models/generators/cnn.py
index ac55354..076f1a6 100644
--- a/lib/models/generators/cnn.py
+++ b/lib/models/generators/cnn.py
@@ -1,19 +1,22 @@
-from random import choices, seed
-import numpy as np
-
-import torch
 from functools import reduce
 from operator import mul
 
+from random import choices, choice
+
+import torch
+
 from torch import nn
 from torch.optim import Adam
+from torchvision.datasets import MNIST
 
+from datasets.mnist import MyMNIST
 from datasets.trajectory_dataset import TrajData
-from lib.evaluation.classification import ROCEvaluation
 from lib.modules.blocks import ConvModule, ResidualModule, DeConvModule
 from lib.modules.utils import LightningBaseModule, Flatten
 
 import matplotlib.pyplot as plt
+import lib.variables as V
+from lib.visualization.generator_eval import GeneratorVisualizer
 
 
 class CNNRouteGeneratorModel(LightningBaseModule):
@@ -24,48 +27,71 @@ class CNNRouteGeneratorModel(LightningBaseModule):
         return Adam(self.parameters(), lr=self.hparams.train_param.lr)
 
     def training_step(self, batch_xy, batch_nb, *args, **kwargs):
-        batch_x, alternative = batch_xy
+        batch_x, target = batch_xy
         generated_alternative, z, mu, logvar = self(batch_x)
-        element_wise_loss = self.criterion(generated_alternative, alternative)
-        # see Appendix B from VAE paper:
-        # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
-        # https://arxiv.org/abs/1312.6114
-        # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
+        target = batch_x if 'ae' in self.hparams.data_param.mode else target
+        element_wise_loss = self.criterion(generated_alternative, target)
 
-        kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
-        # Dimensional Resizing TODO: Does This make sense? Sanity Check it!
-        # kld_loss /= reduce(mul, self.in_shape)
-        # kld_loss *= self.hparams.data_param.dataset_length / self.hparams.train_param.batch_size * 100
+        if 'vae' in self.hparams.data_param.mode:
+            # see Appendix B from VAE paper:
+            # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
+            # https://arxiv.org/abs/1312.6114
+            # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
+            kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+            # Dimensional Resizing TODO: Does This make sense? Sanity Check it!
+            # kld_loss /= reduce(mul, self.in_shape)
+            # kld_loss *= self.hparams.data_param.dataset_length / self.hparams.train_param.batch_size
 
-        loss = kld_loss + element_wise_loss
+            loss = kld_loss + element_wise_loss
+        else:
+            loss = element_wise_loss
+            kld_loss = 0
         return dict(loss=loss, log=dict(element_wise_loss=element_wise_loss, loss=loss, kld_loss=kld_loss))
 
     def _test_val_step(self, batch_xy, batch_nb, *args):
         batch_x, _ = batch_xy
-        map_array = batch_x[:, 0].unsqueeze(1)
-        trajectory = batch_x[:, 1].unsqueeze(1)
-        labels = batch_x[:, 2].unsqueeze(1).max(dim=-1).values.max(-1).values
+        if 'vae' in self.hparams.data_param.mode:
+            z, mu, logvar = self.encode(batch_x)
+        else:
+            z = self.encode(batch_x)
+            mu, logvar = z, z
 
-        _, mu, _ = self.encode(batch_x)
         generated_alternative = self.generate(mu)
-        return dict(maps=map_array, trajectories=trajectory, batch_nb=batch_nb,  labels=labels,
-                    generated_alternative=generated_alternative, pred_label=-1)
+        return_dict = dict(input=batch_x, batch_nb=batch_nb, output=generated_alternative, z=z, mu=mu, logvar=logvar)
+
+        if 'hom' in self.hparams.data_param.mode:
+            labels = torch.full((batch_x.shape[0], 1), V.HOMOTOPIC)
+        elif 'alt' in self.hparams.data_param.mode:
+            labels = torch.full((batch_x.shape[0], 1), V.ALTERNATIVE)
+        elif 'vae' in self.hparams.data_param.mode:
+            labels = torch.full((batch_x.shape[0], 1), V.ANY)
+        elif 'ae' in self.hparams.data_param.mode:
+            labels = torch.full((batch_x.shape[0], 1), V.ANY)
+        else:
+            labels = batch_x[:, 2].unsqueeze(1).max(dim=-1).values.max(-1).values
+
+        return_dict.update(labels=self._move_to_model_device(labels))
+        return return_dict
 
     def _test_val_epoch_end(self, outputs, test=False):
-        val_restul_dict = self.generate_random()
+        plt.close('all')
 
-        from lib.visualization.generator_eval import GeneratorVisualizer
-        g = GeneratorVisualizer(**val_restul_dict)
-        fig = g.draw()
+        g = GeneratorVisualizer(choice(outputs))
+        fig = g.draw_io_bundle()
         self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
         plt.clf()
 
+        fig = g.draw_latent()
+        self.logger.log_image(f'{self.name}_Latent', fig, step=self.global_step)
+        plt.clf()
+
         return dict(epoch=self.current_epoch)
 
     def on_epoch_start(self):
-        self.dataset.seed(self.logger.version)
+        # self.dataset.seed(self.logger.version)
         # torch.random.manual_seed(self.logger.version)
         # np.random.seed(self.logger.version)
+        pass
 
     def validation_step(self, *args):
         return self._test_val_step(*args)
@@ -82,19 +108,23 @@ class CNNRouteGeneratorModel(LightningBaseModule):
     def __init__(self, *params, issubclassed=False):
         super(CNNRouteGeneratorModel, self).__init__(*params)
 
-        if not issubclassed:
+        if False:
             # Dataset
-            self.dataset = TrajData(self.hparams.data_param.map_root, mode='generator_all_in_map',
+            self.dataset = TrajData(self.hparams.data_param.map_root,
+                                    mode=self.hparams.data_param.mode,
                                     preprocessed=self.hparams.data_param.use_preprocessed,
                                     length=self.hparams.data_param.dataset_length, normalized=True)
-            self.criterion = nn.MSELoss()
+        self.criterion = nn.MSELoss()
+
+        self.dataset = MyMNIST()
 
         # Additional Attributes                               #
         #######################################################
         self.in_shape = self.dataset.map_shapes_max
         self.use_res_net = self.hparams.model_param.use_res_net
         self.lat_dim = self.hparams.model_param.lat_dim
-        self.feature_dim = self.lat_dim * 10
+        self.feature_dim = self.lat_dim
+        self.out_channels = 1 if 'generator' in self.hparams.data_param.mode else self.in_shape[0]
         ########################################################
 
         # NN Nodes
@@ -119,7 +149,7 @@ class CNNRouteGeneratorModel(LightningBaseModule):
                                       conv_filters=self.hparams.model_param.filters[1],
                                       use_norm=self.hparams.model_param.use_norm,
                                       use_bias=self.hparams.model_param.use_bias)
-        self.enc_conv_1b = ConvModule(self.enc_conv_1a.shape, conv_kernel=3, conv_stride=2, conv_padding=0,
+        self.enc_conv_1b = ConvModule(self.enc_conv_1a.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
                                       conv_filters=self.hparams.model_param.filters[1],
                                       use_norm=self.hparams.model_param.use_norm,
                                       use_bias=self.hparams.model_param.use_bias)
@@ -137,20 +167,8 @@ class CNNRouteGeneratorModel(LightningBaseModule):
                                       use_norm=self.hparams.model_param.use_norm,
                                       use_bias=self.hparams.model_param.use_bias)
 
-        self.enc_res_3 = ResidualModule(self.enc_conv_2b.shape, ConvModule, 2, conv_kernel=7, conv_stride=1,
-                                        conv_padding=3, conv_filters=self.hparams.model_param.filters[2],
-                                        use_norm=self.hparams.model_param.use_norm,
-                                        use_bias=self.hparams.model_param.use_bias)
-        self.enc_conv_3a = ConvModule(self.enc_res_3.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
-                                      conv_filters=self.hparams.model_param.filters[2],
-                                      use_norm=self.hparams.model_param.use_norm,
-                                      use_bias=self.hparams.model_param.use_bias)
-        self.enc_conv_3b = ConvModule(self.enc_conv_3a.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
-                                      conv_filters=self.hparams.model_param.filters[2],
-                                      use_norm=self.hparams.model_param.use_norm,
-                                      use_bias=self.hparams.model_param.use_bias)
-
-        self.enc_flat = Flatten(self.enc_conv_3b.shape)
+        last_conv_shape = self.enc_conv_2b.shape
+        self.enc_flat = Flatten(last_conv_shape)
         self.enc_lin_1 = nn.Linear(self.enc_flat.shape, self.feature_dim)
 
         #
@@ -160,46 +178,43 @@ class CNNRouteGeneratorModel(LightningBaseModule):
 
         #
         # Variational Bottleneck
-        self.mu = nn.Linear(self.feature_dim, self.lat_dim)
-        self.logvar = nn.Linear(self.feature_dim, self.lat_dim)
+        if 'vae' in self.hparams.data_param.mode:
+            self.mu = nn.Linear(self.feature_dim, self.lat_dim)
+            self.logvar = nn.Linear(self.feature_dim, self.lat_dim)
+
+        #
+        # Linear Bottleneck
+        else:
+            self.z = nn.Linear(self.feature_dim, self.lat_dim)
 
         #
         # Alternative Generator
-        self.gen_lin_1 = nn.Linear(self.hparams.model_param.lat_dim, self.feature_dim)
+        self.gen_lin_1 = nn.Linear(self.lat_dim, self.enc_flat.shape)
 
-        self.gen_lin_2 = nn.Linear(self.feature_dim, self.enc_flat.shape)
+        # self.gen_lin_2 = nn.Linear(self.feature_dim, self.enc_flat.shape)
 
-        self.reshape_to_last_conv = Flatten(self.enc_flat.shape, self.enc_conv_3b.shape)
+        self.reshape_to_last_conv = Flatten(self.enc_flat.shape, last_conv_shape)
 
-        self.gen_deconv_1a = DeConvModule(self.enc_conv_3b.shape, self.hparams.model_param.filters[2],
-                                          conv_padding=0, conv_kernel=11, conv_stride=1,
-                                          use_norm=self.hparams.model_param.use_norm)
-        self.gen_deconv_1b = DeConvModule(self.gen_deconv_1a.shape, self.hparams.model_param.filters[2],
-                                          conv_padding=0, conv_kernel=9, conv_stride=2,
+        self.gen_deconv_1a = DeConvModule(last_conv_shape, self.hparams.model_param.filters[2],
+                                          conv_padding=1, conv_kernel=9, conv_stride=1,
                                           use_norm=self.hparams.model_param.use_norm)
 
-        self.gen_deconv_2a = DeConvModule(self.gen_deconv_1b.shape, self.hparams.model_param.filters[1],
-                                          conv_padding=0, conv_kernel=7, conv_stride=1,
-                                          use_norm=self.hparams.model_param.use_norm)
-        self.gen_deconv_2b = DeConvModule(self.gen_deconv_2a.shape, self.hparams.model_param.filters[1],
-                                          conv_padding=0, conv_kernel=7, conv_stride=1,
+        self.gen_deconv_2a = DeConvModule(self.gen_deconv_1a.shape, self.hparams.model_param.filters[1],
+                                          conv_padding=1, conv_kernel=7, conv_stride=1,
                                           use_norm=self.hparams.model_param.use_norm)
 
-        self.gen_deconv_3a = DeConvModule(self.gen_deconv_2b.shape, self.hparams.model_param.filters[0],
-                                          conv_padding=1, conv_kernel=5, conv_stride=1,
-                                          use_norm=self.hparams.model_param.use_norm)
-        self.gen_deconv_3b = DeConvModule(self.gen_deconv_3a.shape, self.hparams.model_param.filters[0],
-                                          conv_padding=1, conv_kernel=4, conv_stride=1,
-                                          use_norm=self.hparams.model_param.use_norm)
-
-        self.gen_deconv_out = DeConvModule(self.gen_deconv_3b.shape, 1, activation=None,
+        self.gen_deconv_out = DeConvModule(self.gen_deconv_2a.shape, self.out_channels, activation=None,
                                            conv_padding=0, conv_kernel=3, conv_stride=1,
                                            use_norm=self.hparams.model_param.use_norm)
 
     def forward(self, batch_x):
         #
         # Encode
-        z, mu, logvar = self.encode(batch_x)
+        if 'vae' in self.hparams.data_param.mode:
+            z, mu, logvar = self.encode(batch_x)
+        else:
+            z = self.encode(batch_x)
+            mu, logvar = z, z
 
         #
         # Generate
@@ -220,148 +235,46 @@ class CNNRouteGeneratorModel(LightningBaseModule):
         combined_tensor = self.enc_res_2(combined_tensor) if self.use_res_net else combined_tensor
         combined_tensor = self.enc_conv_2a(combined_tensor)
         combined_tensor = self.enc_conv_2b(combined_tensor)
-        combined_tensor = self.enc_res_3(combined_tensor) if self.use_res_net else combined_tensor
-        combined_tensor = self.enc_conv_3a(combined_tensor)
-        combined_tensor = self.enc_conv_3b(combined_tensor)
+        # combined_tensor = self.enc_res_3(combined_tensor) if self.use_res_net else combined_tensor
+        # combined_tensor = self.enc_conv_3a(combined_tensor)
+        # combined_tensor = self.enc_conv_3b(combined_tensor)
 
         combined_tensor = self.enc_flat(combined_tensor)
         combined_tensor = self.enc_lin_1(combined_tensor)
-        combined_tensor = self.enc_lin_2(combined_tensor)
-
         combined_tensor = self.enc_norm(combined_tensor)
         combined_tensor = self.activation(combined_tensor)
+
         combined_tensor = self.enc_lin_2(combined_tensor)
         combined_tensor = self.enc_norm(combined_tensor)
         combined_tensor = self.activation(combined_tensor)
 
         #
+        # Variational
         # Parameter and Sampling
-        mu = self.mu(combined_tensor)
-        logvar = self.logvar(combined_tensor)
-        z = self.reparameterize(mu, logvar)
-        return z, mu, logvar
+        if 'vae' in self.hparams.data_param.mode:
+            mu = self.mu(combined_tensor)
+            logvar = self.logvar(combined_tensor)
+            z = self.reparameterize(mu, logvar)
+            return z, mu, logvar
+        else:
+            #
+            # Linear Bottleneck
+            z = self.z(combined_tensor)
+            return z
 
     def generate(self, z):
         alt_tensor = self.gen_lin_1(z)
         alt_tensor = self.activation(alt_tensor)
-        alt_tensor = self.gen_lin_2(alt_tensor)
-        alt_tensor = self.activation(alt_tensor)
+        # alt_tensor = self.gen_lin_2(alt_tensor)
+        # alt_tensor = self.activation(alt_tensor)
         alt_tensor = self.reshape_to_last_conv(alt_tensor)
         alt_tensor = self.gen_deconv_1a(alt_tensor)
-        alt_tensor = self.gen_deconv_1b(alt_tensor)
+
         alt_tensor = self.gen_deconv_2a(alt_tensor)
-        alt_tensor = self.gen_deconv_2b(alt_tensor)
-        alt_tensor = self.gen_deconv_3a(alt_tensor)
-        alt_tensor = self.gen_deconv_3b(alt_tensor)
+
+        # alt_tensor = self.gen_deconv_3a(alt_tensor)
+        # alt_tensor = self.gen_deconv_3b(alt_tensor)
         alt_tensor = self.gen_deconv_out(alt_tensor)
         # alt_tensor = self.activation(alt_tensor)
-        alt_tensor = self.sigmoid(alt_tensor)
+        # alt_tensor = self.sigmoid(alt_tensor)
         return alt_tensor
-
-    def generate_random(self, n=12):
-
-        samples, alternatives = zip(*[self.dataset.test_dataset[choice]
-                                      for choice in choices(range(self.dataset.length), k=n)])
-        samples = self._move_to_model_device(torch.stack([torch.as_tensor(x) for x in samples]))
-        alternatives = self._move_to_model_device(torch.stack([torch.as_tensor(x) for x in alternatives]))
-
-        return self._test_val_step((samples, alternatives), -9999)
-
-
-class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
-
-    name = 'CNNRouteGeneratorDiscriminated'
-
-    def training_step(self, batch_xy, batch_nb, *args, **kwargs):
-        batch_x, label = batch_xy
-
-        generated_alternative, z, mu, logvar = self(batch_x)
-        map_array, trajectory = batch_x
-
-        map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
-        pred_label = self.discriminator(map_stack)
-        discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
-
-        # see Appendix B from VAE paper:
-        # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
-        # https://arxiv.org/abs/1312.6114
-        # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
-        kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
-        # Dimensional Resizing
-        kld_loss /= reduce(mul, self.in_shape)
-
-        loss = (kld_loss + discriminated_bce_loss) / 2
-        return dict(loss=loss, log=dict(loss=loss,
-                                        discriminated_bce_loss=discriminated_bce_loss,
-                                        kld_loss=kld_loss)
-                    )
-
-    def _test_val_step(self, batch_xy, batch_nb, *args):
-        batch_x, label = batch_xy
-
-        generated_alternative, z, mu, logvar = self(batch_x)
-        map_array, trajectory = batch_x
-
-        map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
-        pred_label = self.discriminator(map_stack)
-
-        discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
-        return dict(discriminated_bce_loss=discriminated_bce_loss, batch_nb=batch_nb,
-                    pred_label=pred_label, label=label, generated_alternative=generated_alternative)
-
-    def validation_step(self, *args):
-        return self._test_val_step(*args)
-
-    def validation_epoch_end(self, outputs: list):
-        return self._test_val_epoch_end(outputs)
-
-    def _test_val_epoch_end(self, outputs, test=False):
-        evaluation = ROCEvaluation(plot_roc=True)
-        pred_label = torch.cat([x['pred_label'] for x in outputs])
-        labels = torch.cat([x['label'] for x in outputs]).unsqueeze(1)
-        mean_losses = torch.stack([x['discriminated_bce_loss'] for x in outputs]).mean()
-
-        # Sci-py call ROC eval call is eval(true_label, prediction)
-        roc_auc, tpr, fpr = evaluation(labels.cpu().numpy(), pred_label.cpu().numpy(), )
-        if test:
-            # self.logger.log_metrics(score_dict)
-            self.logger.log_image(f'{self.name}_ROC-Curve', plt.gcf(), step=self.global_step)
-        plt.clf()
-
-        maps, trajectories, labels, val_restul_dict = self.generate_random()
-
-        from lib.visualization.generator_eval import GeneratorVisualizer
-        g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
-        fig = g.draw()
-        self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
-        plt.clf()
-
-        return dict(mean_losses=mean_losses, roc_auc=roc_auc, epoch=self.current_epoch)
-
-    def test_step(self, *args):
-        return self._test_val_step(*args)
-
-    def test_epoch_end(self, outputs):
-        return self._test_val_epoch_end(outputs, test=True)
-
-    @property
-    def discriminator(self):
-        if self._disc is None:
-            raise RuntimeError('Set the Discriminator first; "set_discriminator(disc_model)')
-        return self._disc
-
-    def set_discriminator(self, disc_model):
-        if self._disc is not None:
-            raise RuntimeError('Discriminator has already been set... What are trying to do?')
-        self._disc = disc_model
-
-    def __init__(self, *params):
-        raise NotImplementedError
-        super(CNNRouteGeneratorDiscriminated, self).__init__(*params, issubclassed=True)
-
-        self._disc = None
-
-        self.criterion = nn.BCELoss()
-
-        self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route', preprocessed=True,
-                                length=self.hparams.data_param.dataset_length, normalized=True)
diff --git a/lib/models/generators/cnn_discriminated.py b/lib/models/generators/cnn_discriminated.py
new file mode 100644
index 0000000..9857a01
--- /dev/null
+++ b/lib/models/generators/cnn_discriminated.py
@@ -0,0 +1,116 @@
+from random import choices, seed
+import numpy as np
+
+import torch
+from functools import reduce
+from operator import mul
+
+from torch import nn
+from torch.optim import Adam
+
+from datasets.trajectory_dataset import TrajData
+from lib.evaluation.classification import ROCEvaluation
+from lib.models.generators.cnn import CNNRouteGeneratorModel
+from lib.modules.blocks import ConvModule, ResidualModule, DeConvModule
+from lib.modules.utils import LightningBaseModule, Flatten
+
+import matplotlib.pyplot as plt
+
+
+class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
+
+    name = 'CNNRouteGeneratorDiscriminated'
+
+    def training_step(self, batch_xy, batch_nb, *args, **kwargs):
+        batch_x, label = batch_xy
+
+        generated_alternative, z, mu, logvar = self(batch_x)
+        map_array, trajectory = batch_x
+
+        map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
+        pred_label = self.discriminator(map_stack)
+        discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
+
+        # see Appendix B from VAE paper:
+        # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
+        # https://arxiv.org/abs/1312.6114
+        # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
+        kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+        # Dimensional Resizing
+        kld_loss /= reduce(mul, self.in_shape)
+
+        loss = (kld_loss + discriminated_bce_loss) / 2
+        return dict(loss=loss, log=dict(loss=loss,
+                                        discriminated_bce_loss=discriminated_bce_loss,
+                                        kld_loss=kld_loss)
+                    )
+
+    def _test_val_step(self, batch_xy, batch_nb, *args):
+        batch_x, label = batch_xy
+
+        generated_alternative, z, mu, logvar = self(batch_x)
+        map_array, trajectory = batch_x
+
+        map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
+        pred_label = self.discriminator(map_stack)
+
+        discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
+        return dict(discriminated_bce_loss=discriminated_bce_loss, batch_nb=batch_nb,
+                    pred_label=pred_label, label=label, generated_alternative=generated_alternative)
+
+    def validation_step(self, *args):
+        return self._test_val_step(*args)
+
+    def validation_epoch_end(self, outputs: list):
+        return self._test_val_epoch_end(outputs)
+
+    def _test_val_epoch_end(self, outputs, test=False):
+        evaluation = ROCEvaluation(plot_roc=True)
+        pred_label = torch.cat([x['pred_label'] for x in outputs])
+        labels = torch.cat([x['label'] for x in outputs]).unsqueeze(1)
+        mean_losses = torch.stack([x['discriminated_bce_loss'] for x in outputs]).mean()
+
+        # Sci-py call ROC eval call is eval(true_label, prediction)
+        roc_auc, tpr, fpr = evaluation(labels.cpu().numpy(), pred_label.cpu().numpy(), )
+        if test:
+            # self.logger.log_metrics(score_dict)
+            self.logger.log_image(f'{self.name}_ROC-Curve', plt.gcf(), step=self.global_step)
+        plt.clf()
+
+        maps, trajectories, labels, val_restul_dict = self.generate_random()
+
+        from lib.visualization.generator_eval import GeneratorVisualizer
+        g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
+        fig = g.draw()
+        self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
+        plt.clf()
+
+        return dict(mean_losses=mean_losses, roc_auc=roc_auc, epoch=self.current_epoch)
+
+    def test_step(self, *args):
+        return self._test_val_step(*args)
+
+    def test_epoch_end(self, outputs):
+        return self._test_val_epoch_end(outputs, test=True)
+
+    @property
+    def discriminator(self):
+        if self._disc is None:
+            raise RuntimeError('Set the Discriminator first; "set_discriminator(disc_model)')
+        return self._disc
+
+    def set_discriminator(self, disc_model):
+        if self._disc is not None:
+            raise RuntimeError('Discriminator has already been set... What are trying to do?')
+        self._disc = disc_model
+
+    def __init__(self, *params):
+        raise NotImplementedError
+        super(CNNRouteGeneratorDiscriminated, self).__init__(*params, issubclassed=True)
+
+        self._disc = None
+
+        self.criterion = nn.BCELoss()
+
+        self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route', preprocessed=True,
+                                length=self.hparams.data_param.dataset_length, normalized=True)
diff --git a/lib/objects/map.py b/lib/objects/map.py
index 46a0423..726d292 100644
--- a/lib/objects/map.py
+++ b/lib/objects/map.py
@@ -189,5 +189,5 @@ class MapStorage(UserDict):
         )
 
         for map_file in map_files:
-            current_map = Map().from_image(map_file, embedding_size=self.max_map_size)
+            current_map = Map.from_image(map_file, embedding_size=self.max_map_size)
             self.__setitem__(map_file.name, current_map)
diff --git a/lib/utils/config.py b/lib/utils/config.py
index ee790e7..673bcfb 100644
--- a/lib/utils/config.py
+++ b/lib/utils/config.py
@@ -5,7 +5,9 @@ from collections import defaultdict
 from configparser import ConfigParser
 from pathlib import Path
 
-from lib.models.generators.cnn import CNNRouteGeneratorModel, CNNRouteGeneratorDiscriminated
+from lib.models.generators.cnn import CNNRouteGeneratorModel
+from lib.models.generators.cnn_discriminated import CNNRouteGeneratorDiscriminated
+
 from lib.models.homotopy_classification.cnn_based import ConvHomDetector
 from lib.utils.model_io import ModelParameters
 from lib.utils.transforms import AsArray
diff --git a/lib/utils/logging.py b/lib/utils/logging.py
index deb3d38..8050687 100644
--- a/lib/utils/logging.py
+++ b/lib/utils/logging.py
@@ -37,7 +37,7 @@ class Logger(LightningLoggerBase):
     @property
     def outpath(self):
         # ToDo: Add further path modification such as dataset config etc.
-        return Path(self.config.train.outpath)
+        return Path(self.config.train.outpath) / self.config.data.mode
 
     def __init__(self, config: Config):
         """
diff --git a/lib/utils/tools.py b/lib/utils/tools.py
index 6516bd8..594fff4 100644
--- a/lib/utils/tools.py
+++ b/lib/utils/tools.py
@@ -9,6 +9,7 @@ def write_to_shelve(file_path, value):
     with shelve.open(str(file_path), protocol=pickle.HIGHEST_PROTOCOL) as f:
         new_key = str(len(f))
         f[new_key] = value
+    f.close()
 
 
 def load_from_shelve(file_path, key):
diff --git a/lib/variables.py b/lib/variables.py
index ab97da8..9b27a6c 100644
--- a/lib/variables.py
+++ b/lib/variables.py
@@ -1,9 +1,15 @@
 from pathlib import Path
 _ROOT = Path('..')
 
+# Labels for classes
 HOMOTOPIC = 1
 ALTERNATIVE = 0
+ANY = -1
+
+# Colors for img files
 WHITE = 255
 BLACK = 0
 
-DPI = 100
+# Variables for plotting
+PADDING = 0.25
+DPI = 50
diff --git a/lib/visualization/generator_eval.py b/lib/visualization/generator_eval.py
index 1241717..b895816 100644
--- a/lib/visualization/generator_eval.py
+++ b/lib/visualization/generator_eval.py
@@ -1,53 +1,106 @@
+from collections import defaultdict
+
 import matplotlib.pyplot as plt
+import matplotlib.cm as cmaps
 from mpl_toolkits.axisartist.axes_grid import ImageGrid
+from sklearn.cluster import Birch, DBSCAN, KMeans
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
+
 import lib.variables as V
+import numpy as np
 
 
 class GeneratorVisualizer(object):
 
-    def __init__(self, **kwargs):
-        # val_results = dict(discriminated_bce_loss, batch_nb, pred_label, label, generated_alternative)
-        self.alternatives = kwargs.get('generated_alternative')
-        self.labels = kwargs.get('labels')
-        self.trajectories = kwargs.get('trajectories')
-        self.maps = kwargs.get('maps')
+    def __init__(self, outputs, k=8):
+        d = defaultdict(list)
+        for key in outputs.keys():
+            try:
+                d[key] = outputs[key][:k].cpu().numpy()
+            except AttributeError:
+                d[key] = outputs[key][:k]
+            except TypeError:
+                self.batch_nb = outputs[key]
+        for key in d.keys():
+            self.__setattr__(key, d[key])
 
-        self._map_width, self._map_height = self.maps[0].squeeze().shape
+        # val_results = dict(discriminated_bce_loss, batch_nb, pred_label, label, generated_alternative)
+        self._map_width, self._map_height = self.input.shape[1], self.input.shape[2]
         self.column_dict_list = self._build_column_dict_list()
         self._cols = len(self.column_dict_list)
         self._rows = len(self.column_dict_list[0])
 
+        self.colormap = cmaps.tab20
+
     def _build_column_dict_list(self):
         trajectories = []
-        non_hom_alternatives = []
-        hom_alternatives = []
+        alternatives = []
 
-        for idx in range(self.alternatives.shape[0]):
-            image = (self.alternatives[idx]).cpu().numpy().squeeze()
-            label = self.labels[idx].item()
-            # Dirty and Quick hack incomming.
-            if label == V.HOMOTOPIC:
-                hom_alternatives.append(dict(image=image, label='Homotopic'))
-                non_hom_alternatives.append(None)
-            else:
-                non_hom_alternatives.append(dict(image=image, label='NonHomotopic'))
-                hom_alternatives.append(None)
-        for idx in range(max(len(hom_alternatives), len(non_hom_alternatives))):
-            image = (self.maps[idx] + self.trajectories[idx]).cpu().numpy().squeeze()
+        for idx in range(self.output.shape[0]):
+            image = (self.output[idx]).squeeze()
+            label = 'Homotopic' if self.labels[idx].item() == V.HOMOTOPIC else 'Alternative'
+            alternatives.append(dict(image=image, label=label))
+
+        for idx in range(len(alternatives)):
+            image = (self.input[idx]).squeeze()
             label = 'original'
             trajectories.append(dict(image=image, label=label))
 
-        return trajectories, hom_alternatives, non_hom_alternatives
+        return trajectories, alternatives
 
-    def draw(self):
-        padding = 0.25
-        additional_size = self._cols * padding + 3 * padding
-        width = (self._map_width * self._cols) / V.DPI + additional_size
-        height = (self._map_height * self._rows) / V.DPI + additional_size
+    @staticmethod
+    def cluster_data(data):
+
+        cluster = Birch()
+
+        labels = cluster.fit_predict(data)
+        return labels
+
+    def draw_latent(self):
+        plt.close('all')
+        clusterer = KMeans(10)
+        try:
+            labels = clusterer.fit_predict(self.logvar)
+        except ValueError:
+            fig = plt.figure()
+            return fig
+        if self.z.shape[-1] > 2:
+            fig, axs = plt.subplots(ncols=2, nrows=1)
+            transformers = [TSNE(2), PCA(2)]
+            for idx, transformer in enumerate(transformers):
+                transformed = transformer.fit_transform(self.z)
+
+                colored = self.colormap(labels)
+                ax = axs[idx]
+                ax.scatter(x=transformed[:, 0], y=transformed[:, 1], c=colored)
+                ax.set_title(transformer.__class__.__name__)
+                ax.set_xlim(np.min(transformed[:, 0])*1.1, np.max(transformed[:, 0]*1.1))
+                ax.set_ylim(np.min(transformed[:, 1]*1.1), np.max(transformed[:, 1]*1.1))
+        elif self.z.shape[-1] == 2:
+            fig, axs = plt.subplots()
+
+            # TODO: Build transformation for lat_dim_size >= 3
+            print('All Predictions sucesfully Gathered and Shaped ')
+            axs.set_xlim(np.min(self.z[:, 0]), np.max(self.z[:, 0]))
+            axs.set_ylim(np.min(self.z[:, 1]), np.max(self.z[:, 1]))
+            # ToDo: Insert Normalization
+            colored = self.colormap(labels)
+            plt.scatter(self.z[:, 0], self.z[:, 1], c=colored)
+        else:
+            raise NotImplementedError("Latent Dimensions can not be one-dimensional (yet).")
+
+        return fig
+
+    def draw_io_bundle(self):
+        width, height = self._cols * 5, self._rows * 5
+        additional_size = self._cols * V.PADDING + 3 * V.PADDING
+        # width = (self._map_width * self._cols) / V.DPI + additional_size
+        # height = (self._map_height * self._rows) / V.DPI + additional_size
         fig = plt.figure(figsize=(width, height), dpi=V.DPI)
         grid = ImageGrid(fig, 111,  # similar to subplot(111)
                          nrows_ncols=(self._rows, self._cols),
-                         axes_pad=padding,  # pad between axes in inch.
+                         axes_pad=V.PADDING,  # pad between axes in inch.
                          )
 
         for idx in range(len(grid.axes_all)):
diff --git a/main.py b/main.py
index 84fcbde..5b20fe4 100644
--- a/main.py
+++ b/main.py
@@ -33,12 +33,13 @@ main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")
 
 # Data Parameters
 main_arg_parser.add_argument("--data_worker", type=int, default=10, help="")
-main_arg_parser.add_argument("--data_dataset_length", type=int, default=100000, help="")
+main_arg_parser.add_argument("--data_dataset_length", type=int, default=10000, help="")
 main_arg_parser.add_argument("--data_root", type=str, default='data', help="")
 main_arg_parser.add_argument("--data_map_root", type=str, default='res/shapes', help="")
 main_arg_parser.add_argument("--data_normalized", type=strtobool, default=True, help="")
 main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=True, help="")
 
+main_arg_parser.add_argument("--data_mode", type=str, default='ae_no_label_in_map', help="")
 
 # Transformations
 main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
@@ -46,7 +47,7 @@ main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, defa
 # Transformations
 main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
 main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
-main_arg_parser.add_argument("--train_epochs", type=int, default=20, help="")
+main_arg_parser.add_argument("--train_epochs", type=int, default=200, help="")
 main_arg_parser.add_argument("--train_batch_size", type=int, default=164, help="")
 main_arg_parser.add_argument("--train_lr", type=float, default=0.002, help="")
 main_arg_parser.add_argument("--train_num_sanity_val_steps", type=int, default=0, help="")
@@ -54,9 +55,9 @@ main_arg_parser.add_argument("--train_num_sanity_val_steps", type=int, default=0
 # Model
 main_arg_parser.add_argument("--model_type", type=str, default="CNNRouteGenerator", help="")
 main_arg_parser.add_argument("--model_activation", type=str, default="elu", help="")
-main_arg_parser.add_argument("--model_filters", type=str, default="[16, 32, 64]", help="")
+main_arg_parser.add_argument("--model_filters", type=str, default="[16, 32, 32]", help="")
 main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
-main_arg_parser.add_argument("--model_lat_dim", type=int, default=8, help="")
+main_arg_parser.add_argument("--model_lat_dim", type=int, default=4, help="")
 main_arg_parser.add_argument("--model_use_bias", type=strtobool, default=True, help="")
 main_arg_parser.add_argument("--model_use_norm", type=strtobool, default=True, help="")
 main_arg_parser.add_argument("--model_use_res_net", type=strtobool, default=False, help="")
@@ -101,7 +102,7 @@ def run_lightning_loop(config_obj):
         model.init_weights(torch.nn.init.xavier_normal_)
         if model.name == 'CNNRouteGeneratorDiscriminated':
             # ToDo: Make this dependent on the used seed
-            path = Path(Path(config_obj.train.outpath) / 'classifier_cnn' / 'version_0')
+            path = logger.outpath / 'classifier_cnn' / 'version_0'
             disc_model = SavedLightningModels.load_checkpoint(path).restore()
             model.set_discriminator(disc_model)
 
@@ -111,13 +112,12 @@ def run_lightning_loop(config_obj):
                           show_progress_bar=True,
                           weights_save_path=logger.log_dir,
                           gpus=[0] if torch.cuda.is_available() else None,
-                          check_val_every_n_epoch=1,
-                          num_sanity_val_steps=config_obj.train.num_sanity_val_steps,
+                          check_val_every_n_epoch=10,
+                          # num_sanity_val_steps=config_obj.train.num_sanity_val_steps,
                           # row_log_interval=(model.n_train_batches * 0.1),  # TODO: Better Value / Setting
                           # log_save_interval=(model.n_train_batches * 0.2),  # TODO: Better Value / Setting
                           checkpoint_callback=checkpoint_callback,
                           logger=logger,
-                          val_percent_check=0.025,
                           fast_dev_run=config_obj.main.debug,
                           early_stop_callback=None
                           )
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