--- a
+++ b/trainers/ConstrainedAAE.py
@@ -0,0 +1,203 @@
+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 AEMODEL, Phase, indicate_early_stopping, update_log_dicts
+from trainers.DLMODEL import *
+
+
+class ConstrainedAAE(AEMODEL):
+    class Config(AEMODEL.Config):
+        def __init__(self):
+            super().__init__('ConstrainedAAE')
+            self.rho = 1
+            self.scale = 10.0
+
+    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.z = tf.placeholder(tf.float32, [None, self.config.zDim], name='z')
+
+        self.outputs = self.network(self.z, self.x, dropout_rate=self.dropout_rate, dropout=self.dropout, config=self.config)
+        self.reconstruction = self.x_hat = self.outputs['x_hat']
+        self.generated = self.z_ = self.outputs['z_']
+        self.d_ = self.outputs['d_']
+        self.d = self.outputs['d']
+        self.z_hat = self.outputs['z_hat']
+        self.d_hat = self.outputs['d_hat']
+        self.z_rec = self.outputs['z_rec']
+
+        self.scale = self.config.scale
+        self.rho = self.config.rho
+
+        # 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['gen_loss'] = gen_loss = -tf.reduce_mean(self.d_)
+        self.losses['disc_loss_without_grad'] = disc_loss = tf.reduce_mean(self.d_) - tf.reduce_mean(self.d)
+        self.losses['disc_loss_real'] = tf.reduce_mean(self.d)
+        self.losses['disc_loss_fake'] = tf.reduce_mean(self.d_)
+
+        ddx = tf.gradients(self.d_hat, self.z_hat)[0]
+        ddx = tf.sqrt(tf.reduce_sum(tf.square(ddx), axis=1))
+        ddx = tf.reduce_mean(tf.square(ddx - 1.0) * self.scale)
+        self.losses['disc_loss'] = disc_loss = disc_loss + ddx
+
+        self.losses['L1'] = tf.losses.absolute_difference(self.x, self.reconstruction, reduction=Reduction.NONE)
+        self.losses['reconstructionLoss'] = tf.reduce_mean(tf.reduce_sum(self.losses['L1'], axis=[1, 2, 3]))
+
+        self.losses['L2'] = l2 = tf.reduce_mean(tf.losses.mean_squared_error(self.x, self.reconstruction, reduction=Reduction.NONE), axis=[1, 2, 3])
+        self.losses['Rec_z'] = rec_z = tf.reduce_mean(tf.losses.mean_squared_error(self.z_rec, self.z_, reduction=Reduction.NONE), axis=[1])
+
+        self.losses['loss'] = ae_loss = tf.reduce_mean(l2 + self.rho * rec_z)
+
+        with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
+            # Set the optimizer
+            t_vars = tf.trainable_variables()
+            dis_vars = [var for var in t_vars if 'Discriminator' in var.name]
+            gen_vars = [var for var in t_vars if 'Encoder' in var.name]
+            ae_vars = t_vars
+
+            optim_dis = tf.train.AdamOptimizer(learning_rate=self.config.learningrate, beta1=0.5, beta2=0.9).minimize(disc_loss, var_list=dis_vars)
+            optim_gen = tf.train.AdamOptimizer(learning_rate=self.config.learningrate, beta1=0.5, beta2=0.9).minimize(gen_loss, var_list=gen_vars)
+            optim_ae = tf.train.AdamOptimizer(learning_rate=self.config.learningrate, beta1=0.5, beta2=0.9).minimize(ae_loss, var_list=ae_vars)
+
+        # 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 #
+            ############
+            phase = Phase.TRAIN
+            scalars = defaultdict(list)
+            visuals = []
+            d_iters = 20
+            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)
+
+                run = {}
+                for _ in range(d_iters if epoch <= 5 else 1):
+                    # AE optimization
+                    fetches = {
+                        'reconstruction': self.reconstruction,
+                        'rec_z': self.losses['Rec_z'],
+                        'L1': self.losses['L1'],
+                        'loss': self.losses['loss'],
+                        'reconstructionLoss': self.losses['reconstructionLoss'],
+                        'z_': self.z_,
+                        'z_rec': self.z_rec,
+                        'optimizer_ae': optim_ae
+                    }
+
+                    feed_dict = self.get_feed_dict(batch, phase)
+
+                    run = self.sess.run(fetches, feed_dict=feed_dict)
+
+                for _ in range(d_iters):
+                    # Discriminator optimization
+                    fetches = {
+                        'disc_loss': self.losses['disc_loss'],
+                        'optimizer_d': optim_dis,
+                    }
+
+                    feed_dict = self.get_feed_dict(batch, phase)
+
+                    run = {**run, **self.sess.run(fetches, feed_dict=feed_dict)}
+
+                # Generator optimization
+                fetches = {
+                    'gen_loss': self.losses['gen_loss'],
+                    'optimizer_g': optim_gen,
+                }
+
+                feed_dict = self.get_feed_dict(batch, phase)
+
+                run = {**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["reconstructionLoss"]:.8f},'
+                      f' gen_loss: {run["gen_loss"]:.8f}, disc_loss: {run["disc_loss"]:.8f}')
+                update_log_dicts(*trainer_utils.get_summary_dict(batch, run), scalars, visuals)
+
+            self.log_to_tensorboard(epoch, scalars, visuals, phase)
+
+            # Increment last_epoch counter and save model
+            last_epoch += 1
+            self.save(self.checkpointDir, last_epoch)
+
+            ##############
+            # VALIDATION #
+            ##############
+            scalars = defaultdict(list)
+            visuals = []
+            phase = Phase.VAL
+            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
+                }
+
+                feed_dict = self.get_feed_dict(batch, phase)
+                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)
+
+            best_cost, last_improvement, stop = indicate_early_stopping(scalars['reconstructionLoss'], best_cost, last_improvement)
+            if stop:
+                print('Early stopping was triggered due to no improvement over the last 5 epochs')
+                break
+
+    def get_feed_dict(self, batch, phase):
+        return {
+            self.x: batch,
+            self.z: self.sample_z(),
+            self.dropout: phase == Phase.TRAIN,
+            self.dropout_rate: self.config.dropout_rate
+        }
+
+    def reconstruct(self, x, dropout=False):
+        if x.ndim < 4:
+            x = np.expand_dims(x, 0)
+
+        fetches = {
+            'reconstruction': self.reconstruction
+        }
+
+        feed_dict = {
+            self.x: x,
+            self.z: self.sample_z(),
+            self.dropout: dropout,  # apply only during MC sampling.
+            self.dropout_rate: self.config.dropout_rate
+        }
+        results = self.sess.run(fetches, feed_dict=feed_dict)
+
+        results['l1err'] = np.sum(np.abs(x - results['reconstruction']))
+        results['l2err'] = np.sum(np.sqrt((x - results['reconstruction']) ** 2))
+
+        return results
+
+    def sample_z(self):
+        return np.random.normal(size=[self.config.batchsize, self.config.zDim])