buriburisuri / ac-gan Goto Github PK

Hi @buriburisuri , your code ac-gan loss, Here the loss which need to be optimized seems not be a 0d tensor, didn't it need to sg_mean() with the loss_c_r and loss_c_d?

thanks

Traceback (most recent call last):
File "generate.py", line 98, in
fig_name='fake.png')
File "generate.py", line 73, in run_generator
saver.restore(sess, tf.train.latest_checkpoint('asset/train'))
File "/home/xujian/.local/lib/python2.7/site-packages/tensorflow/python/training/saver.py", line 1439, in restore
{self.saver_def.filename_tensor_name: save_path})
File "/home/xujian/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 767, in run
run_metadata_ptr)
File "/home/xujian/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 965, in _run
feed_dict_string, options, run_metadata)
File "/home/xujian/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1015, in _do_run
target_list, options, run_metadata)
File "/home/xujian/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1035, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InternalError: Unable to get element from the feed as bytes.

Hello,
If I want to use this code for 128x128 mages, all I have to do is adding more sg_upconv layers to the generator in order to achieve the desired output? And sg_conv for the discriminator of course.
Any thought?
Thanks.

Thank for sharing this code, but when I trying to train this model with python train.py, the following error occurs:

Traceback (most recent call last):
File "train.py", line 86, in
y_disc = tf.concat(0, [y, y * 0])
File "/home/hao/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 1062, in concat
).assert_is_compatible_with(tensor_shape.scalar())
File "/home/hao/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/tensor_shape.py", line 737, in assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (2, 32) and () are incompatible

As is known, only generated samples are used to calculate adversarial loss for updating G net in classic gan. Here, I see that real image data was also used to calculate classification loss to update G net? Is it ok to do this?

when i run trian.py, I don't find log, and when I run the tensorboard --logdir asset/train/log, the web don't display the result. Can you tell me how to use the tensorboard?

Can you explain for me "what is continuous factor loss"? i read the paper about AC-Gan and can not find anything that related to "continuous factor loss".

Extracting ./asset/data/mnist/train-images-idx3-ubyte.gz
Traceback (most recent call last):
File "train.py", line 75, in
data = tf.sg_data.Mnist(batch_size=batch_size)
File "/home/xujian/ac-gan-master/ac-gan-master/sugartensor/sg_data.py", line 40, in init
data_set = input_data.read_data_sets(Mnist._data_dir, reshape=reshape, one_hot=one_hot)
File "/home/xujian/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py", line 213, in read_data_sets
train_images = extract_images(f)
File "/home/xujian/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py", line 53, in extract_images
magic = _read32(bytestream)
File "/home/xujian/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py", line 35, in _read32
return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
File "/usr/lib/python2.7/gzip.py", line 268, in read
self._read(readsize)
File "/usr/lib/python2.7/gzip.py", line 303, in _read
self._read_gzip_header()
File "/usr/lib/python2.7/gzip.py", line 197, in _read_gzip_header
raise IOError, 'Not a gzipped file'
IOError: Not a gzipped file

1. the "y_disc = tf.concat(0, [y, y * 0])" in 82 lines seems unused?

2.You divide the class info into categorical latent code and conditional latent code, Is this thought from InfoGan? I don't find the paper do it.

The picture in Readme contains conditional gan, semi-supervised gan, info gan and acgan. But it seems that the paper of acgan does not have this picture. So which paper contains this picture?