diff --git a/generate.py b/generate.py
new file mode 100644
index 0000000..0c93261
--- /dev/null
+++ b/generate.py
@@ -0,0 +1,63 @@
+from __future__ import print_function
+import argparse
+import random
+import torch
+import torch.nn as nn
+import torch.nn.parallel
+import torch.backends.cudnn as cudnn
+import torch.optim as optim
+import torch.utils.data
+import torchvision.datasets as dset
+import torchvision.transforms as transforms
+import torchvision.utils as vutils
+from torch.autograd import Variable
+import os
+import json
+
+import models.dcgan as dcgan
+import models.mlp as mlp
+
+if __name__=="__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-c', '--config', required=True, type=str, help='path to generator config .json file')
+    parser.add_argument('-w', '--weights', required=True, type=str, help='path to generator weights .pth file')
+    parser.add_argument('-o', '--output_dir', required=True, type=str, help="path to to output directory")
+    parser.add_argument('-n', '--nimages', required=True, type=int, help="number of images to generate", default=1)
+    parser.add_argument('--cuda', action='store_true', help='enables cuda')
+    opt = parser.parse_args()
+
+    with open(opt.config, 'r') as gencfg:
+        generator_config = json.loads(gencfg.read())
+    
+    imageSize = generator_config["imageSize"]
+    nz = generator_config["nz"]
+    nc = generator_config["nc"]
+    ngf = generator_config["ngf"]
+    noBN = generator_config["noBN"]
+    ngpu = generator_config["ngpu"]
+    mlp_G = generator_config["mlp_G"]
+    n_extra_layers = generator_config["n_extra_layers"]
+
+    if noBN:
+        netG = dcgan.DCGAN_G_nobn(imageSize, nz, nc, ngf, ngpu, n_extra_layers)
+    elif mlp_G:
+        netG = mlp.MLP_G(imageSize, nz, nc, ngf, ngpu)
+    else:
+        netG = dcgan.DCGAN_G(imageSize, nz, nc, ngf, ngpu, n_extra_layers)
+
+    # load weights
+    netG.load_state_dict(torch.load(opt.weights))
+
+    # initialize noise
+    fixed_noise = torch.FloatTensor(opt.nimages, nz, 1, 1).normal_(0, 1)
+
+    if opt.cuda:
+        netG.cuda()
+        fixed_noise = fixed_noise.cuda()
+
+    fake = netG(fixed_noise)
+    fake.data = fake.data.mul(0.5).add(0.5)
+
+    for i in range(opt.nimages):
+        vutils.save_image(fake.data[i, ...].reshape((1, nc, imageSize, imageSize)), os.path.join(opt.output_dir, "generated_%02d.png"%i))
diff --git a/main.py b/main.py
index a9cf68f..36fc2d3 100644
--- a/main.py
+++ b/main.py
@@ -12,6 +12,7 @@ import torchvision.transforms as transforms
 import torchvision.utils as vutils
 from torch.autograd import Variable
 import os
+import json
 
 import models.dcgan as dcgan
 import models.mlp as mlp
@@ -98,6 +99,11 @@ if __name__=="__main__":
     nc = int(opt.nc)
     n_extra_layers = int(opt.n_extra_layers)
 
+    # write out generator config to generate images together wth training checkpoints (.pth)
+    generator_config = {"imageSize": opt.imageSize, "nz": nz, "nc": nc, "ngf": ngf, "ngpu": ngpu, "n_extra_layers": n_extra_layers, "noBN": opt.noBN, "mlp_G": opt.mlp_G}
+    with open(os.path.join(opt.experiment, "generator_config.json"), 'w') as gcfg:
+        gcfg.write(json.dumps(generator_config)+"\n")
+
     # custom weights initialization called on netG and netD
     def weights_init(m):
         classname = m.__class__.__name__
@@ -114,6 +120,11 @@ if __name__=="__main__":
     else:
         netG = dcgan.DCGAN_G(opt.imageSize, nz, nc, ngf, ngpu, n_extra_layers)
 
+    # write out generator config to generate images together wth training checkpoints (.pth)
+    generator_config = {"imageSize": opt.imageSize, "nz": nz, "nc": nc, "ngf": ngf, "ngpu": ngpu, "n_extra_layers": n_extra_layers, "noBN": opt.noBN, "mlp_G": opt.mlp_G}
+    with open(os.path.join(opt.experiment, "generator_config.json"), 'w') as gcfg:
+        gcfg.write(json.dumps(generator_config)+"\n")
+
     netG.apply(weights_init)
     if opt.netG != '': # load checkpoint if needed
         netG.load_state_dict(torch.load(opt.netG))