Merge remote-tracking branch 'origin/master'

Author: dribnet

README.rst

@@ -64,7 +64,7 @@ Pre-trained models are provided in the GitHub releases.  Training your own is a
     
     # Train the model using an adversarial setup based on [4] below.
     python3.4 enhance.py --train "data/*.jpg" --model custom --scales=2 --epochs=250 \
-             --perceptual-layer=conv5_2 --smoothness-weight=2e4 --adversary-weight=2e5 \
+             --perceptual-layer=conv5_2 --smoothness-weight=2e4 --adversary-weight=1e3 \
              --generator-start=5 --discriminator-start=0 --adversarial-start=5 \
              --discriminator-size=64
 

enhance.py

@@ -97,7 +97,7 @@ def extend(lst): return itertools.chain(lst, itertools.repeat(lst[-1]))
 # Load the underlying deep learning libraries based on the device specified.  If you specify THEANO_FLAGS manually,
 # the code assumes you know what you are doing and they are not overriden!
 os.environ.setdefault('THEANO_FLAGS', 'floatX=float32,device={},force_device=True,allow_gc=True,'\
-                                      'print_active_device=False,lib.cnmem=1.0'.format(args.device))
+                                      'print_active_device=False'.format(args.device))
 
 # Scientific & Imaging Libraries
 import numpy as np
@@ -436,7 +436,7 @@ def loss_total_variation(self, x):
         return T.mean(((x[:,:,:-1,:-1] - x[:,:,1:,:-1])**2 + (x[:,:,:-1,:-1] - x[:,:,:-1,1:])**2)**1.25)
 
     def loss_adversarial(self, d):
-        return T.mean(1.0 - T.nnet.softplus(d[args.batch_size:]))
+        return T.mean(1.0 - T.nnet.softminus(d[args.batch_size:]))
 
     def loss_discriminator(self, d):
         return T.mean(T.nnet.softminus(d[args.batch_size:]) - T.nnet.softplus(d[:args.batch_size]))