--- a
+++ b/trainers/AEMODEL.py
@@ -0,0 +1,79 @@
+import os
+from abc import ABC
+from datetime import datetime
+
+import numpy as np
+import tensorflow as tf
+
+from trainers.DLMODEL import DLMODEL
+from utils.logger import Logger, Phase
+
+
+class AEMODEL(DLMODEL, ABC):
+    class Config(DLMODEL.Config):
+        def __init__(self, modelname='AE'):
+            super().__init__()
+            self.modelname = modelname
+            self.intermediateResolutions = [8, 8]
+            self.outputWidth = 256
+            self.outputHeight = 256
+            self.numChannels = 3
+            self.dropout = False
+            self.dropout_rate = 0.2
+            self.zDim = 128
+
+    def __init__(self, sess, config=Config(), network=None):
+        super().__init__(sess, config)
+        self.losses = {}
+        self.dropout = tf.placeholder(tf.bool, name='dropout')
+        self.dropout_rate = tf.placeholder(tf.float32, name='dropout_rate')
+
+        self.network = network
+        self.checkpointDir = os.path.join(self.config.checkpointDir, self.network.__name__)
+        self.logDir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'logs', self.network.__name__, self.model_dir,
+                                   datetime.now().strftime('%Y%m%d_%H%M%S'))
+        self.logger = Logger(self.sess, self.logDir)
+
+    def log_to_tensorboard(self, epoch, scalars, visuals, phase: Phase, name='x'):
+        for key in scalars.keys():
+            scalars[key] = np.mean(scalars[key])
+
+        if visuals:
+            self.logger.summarize(epoch, phase=phase, summaries_dict={**scalars, **{name: np.vstack(visuals)[:50]}})
+
+    def load_checkpoint(self):
+        could_load, checkpoint_counter = self.load(self.checkpointDir)
+        if could_load:
+            last_epoch = checkpoint_counter
+            print(" [*] Load SUCCESS")
+        else:
+            last_epoch = 0
+            print(" [!] Load failed...")
+        return last_epoch
+
+    @property
+    def model_dir(self):
+        return "{}_d{}_s{}x{}_{}_b{}_z{}_{}".format(self.config.modelname, self.config.dataset,
+                                                    self.config.outputWidth,
+                                                    self.config.outputHeight,
+                                                    self.network.__name__,
+                                                    self.config.batchsize, self.config.zDim,
+                                                    self.config.description)
+
+
+def update_log_dicts(scalars, visuals, train_scalars, train_visuals):
+    for k, v in list(scalars.items()):
+        train_scalars[k].append(v)
+    train_visuals.append(visuals)
+
+
+def indicate_early_stopping(current_cost, best_cost, last_improvement):
+    if current_cost < best_cost:
+        best_cost = current_cost
+        last_improvement = 0
+        return best_cost, last_improvement, False
+    else:
+        last_improvement += 1
+        if last_improvement >= 5:
+            return best_cost, last_improvement, True
+        return best_cost, last_improvement, False