--- a
+++ b/trainers/VAE_You.py
@@ -0,0 +1,173 @@
+from collections import defaultdict
+from math import inf
+
+from tensorflow.python.ops.losses.losses_impl import Reduction
+
+from trainers import trainer_utils
+from trainers.AEMODEL import Phase, update_log_dicts, indicate_early_stopping, AEMODEL
+from trainers.DLMODEL import *
+
+
+class VAE_You(AEMODEL):
+    class Config(AEMODEL.Config):
+        def __init__(self):
+            super().__init__('VAE_You')
+            self.restore_lr = 1e-3
+            self.restore_steps = 150
+            self.tv_lambda = 1.8
+
+    def __init__(self, sess, config, network=None):
+        super().__init__(sess, config, network)
+        self.x = tf.placeholder(tf.float32, [None, self.config.outputHeight, self.config.outputWidth, self.config.numChannels], name='x')
+        self.tv_lambda = tf.placeholder(tf.float32, shape=())
+
+        # Additional Parameters
+        self.restore_lr = self.config.restore_lr
+        self.restore_steps = self.config.restore_steps
+        self.tv_lambda_value = self.config.tv_lambda
+
+        self.outputs = self.network(self.x, dropout_rate=self.dropout_rate, dropout=self.dropout, config=self.config)
+        self.reconstruction = self.outputs['x_hat']
+        self.z_mu = self.outputs['z_mu']
+        self.z_sigma = self.outputs['z_sigma']
+
+        # Print Stats
+        self.get_number_of_trainable_params()
+        # Instantiate Saver
+        self.saver = tf.train.Saver()
+
+    def train(self, dataset):
+        # Determine trainable variables
+        self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
+
+        # Build losses
+        self.losses['L1'] = tf.losses.absolute_difference(self.x, self.reconstruction, reduction=Reduction.NONE)
+        rec = tf.reduce_sum(self.losses['L1'], axis=[1, 2, 3])
+        kl = 0.5 * tf.reduce_sum(tf.square(self.z_mu) + tf.square(self.z_sigma) - tf.log(tf.square(self.z_sigma)) - 1, axis=1)
+        self.losses['pixel_loss'] = rec + kl
+        self.losses['reconstructionLoss'] = tf.reduce_mean(rec)
+        self.losses['kl'] = tf.reduce_mean(kl)
+        self.losses['loss'] = tf.reduce_mean(rec + kl)
+
+        # for restoration
+        self.losses['restore'] = self.tv_lambda * tf.image.total_variation(tf.subtract(self.x, self.reconstruction))
+        self.losses['grads'] = tf.gradients(self.losses['pixel_loss'] + self.losses['restore'], self.x)[0]
+
+        # Set the optimizer
+        optim = self.create_optimizer(self.losses['loss'], var_list=self.variables, learningrate=self.config.learningrate,
+                                      beta1=self.config.beta1, type=self.config.optimizer)
+
+        # initialize all variables
+        tf.global_variables_initializer().run(session=self.sess)
+
+        best_cost = inf
+        last_improvement = 0
+        last_epoch = self.load_checkpoint()
+
+        # Go go go!
+        for epoch in range(last_epoch, self.config.numEpochs):
+            ############
+            # TRAINING #
+            ############
+            self.process(dataset, epoch, Phase.TRAIN, optim)
+
+            # Increment last_epoch counter and save model
+            last_epoch += 1
+            self.save(self.checkpointDir, last_epoch)
+
+            ##############
+            # VALIDATION #
+            ##############
+            val_scalars = self.process(dataset, epoch, Phase.VAL)
+
+            best_cost, last_improvement, stop = indicate_early_stopping(val_scalars['loss'], best_cost, last_improvement)
+            if stop:
+                print('Early stopping was triggered due to no improvement over the last 5 epochs')
+                break
+
+        if self.tv_lambda_value == -1 and self.restore_steps > 0:
+            ##############
+            # Determine lambda #
+            ##############
+            print('Determining best lambda')
+            self.determine_best_lambda(dataset)
+
+    def process(self, dataset, epoch, phase: Phase, optim=None):
+        scalars = defaultdict(list)
+        visuals = []
+        num_batches = dataset.num_batches(self.config.batchsize, set=phase.value)
+        for idx in range(0, num_batches):
+            batch, _, _ = dataset.next_batch(self.config.batchsize, set=phase.value)
+
+            fetches = {
+                'reconstruction': self.reconstruction,
+                **self.losses
+            }
+            if phase == Phase.TRAIN:
+                fetches['optimizer'] = optim
+
+            feed_dict = {
+                self.x: batch,
+                self.tv_lambda: self.tv_lambda_value,
+                self.dropout: phase == Phase.TRAIN,
+                self.dropout_rate: self.config.dropout_rate
+            }
+
+            run = self.sess.run(fetches, feed_dict=feed_dict)
+
+            # Print to console
+            print(f'Epoch ({phase.value}): [{epoch:2d}] [{idx:4d}/{num_batches:4d}] loss: {run["loss"]:.8f}')
+            update_log_dicts(*trainer_utils.get_summary_dict(batch, run), scalars, visuals)
+
+        self.log_to_tensorboard(epoch, scalars, visuals, phase)
+        return scalars
+
+    def reconstruct(self, x, dropout=False):
+        if x.ndim < 4:
+            x = np.expand_dims(x, 0)
+
+        restored = x.copy()
+        for step in range(self.restore_steps):
+            feed_dict = {
+                self.x: restored,
+                self.tv_lambda: self.tv_lambda_value,
+                self.dropout: dropout,  # apply only during MC sampling.
+                self.dropout_rate: self.config.dropout_rate
+            }
+            run = self.sess.run({'grads': self.losses['grads']}, feed_dict=feed_dict)
+            gradients = run['grads']
+            restored -= self.restore_lr * gradients
+
+        results = {
+            'reconstruction': restored
+        }
+        results['l1err'] = np.sum(np.abs(x - results['reconstruction']))
+        results['l2err'] = np.sum(np.sqrt((x - results['reconstruction']) ** 2))
+
+        return results
+
+    def determine_best_lambda(self, dataset):
+        lambdas = np.arange(20) / 10.0
+        mean_errors = []
+        fetches = self.losses
+
+        for tv_lambda in lambdas:
+            errors = []
+            for idx in range(int(dataset.num_batches(self.config.batchsize, set=Phase.VAL.value) * 0.2)):
+                batch, _, _ = dataset.next_batch(self.config.batchsize, set=Phase.VAL.value)
+                restored = batch.copy()
+                for step in range(self.restore_steps):
+                    feed_dict = {
+                        self.x: restored,
+                        self.tv_lambda: tv_lambda,
+                        self.dropout: False,
+                        self.dropout_rate: self.config.dropout_rate
+                    }
+                    run = self.sess.run(fetches, feed_dict=feed_dict)
+                    restored -= self.restore_lr * run['grads']
+                errors.append(np.sum(np.abs(batch - restored)))
+            mean_error = np.mean(errors)
+            mean_errors.append(mean_error)
+            print(f'mean_error for lambda {tv_lambda}: {mean_error}')
+        self.tv_lambda_value = lambdas[mean_errors.index(min(mean_errors))]
+        print(f'Best lambda: {self.tv_lambda_value}')