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View Code? Open in Web Editor NEWSync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures
Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures
Thanks for your released code and I have a question about this source code:
In this code, there is parameter "x_prev", but I didn't find any definition and declaration about it. I was really confused about this.
Hi, Singularity42
I have an error when I generated the dataset. The error is:
/home/chenqi/anaconda2/lib/python2.7/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float
to np.floating
is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type
.
from ._conv import register_converters as _register_converters
Traceback (most recent call last):
File "mnist_caption_single.py", line 211, in
train_data = f['train'].value.reshape(-1, 28, 28)
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "/home/chenqi/anaconda2/lib/python2.7/site-packages/h5py/_hl/group.py", line 167, in getitem
oid = h5o.open(self.id, self._e(name), lapl=self._lapl)
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "h5py/h5o.pyx", line 190, in h5py.h5o.open
KeyError: "Unable to open object (object 'train' doesn't exist)"
Could you tell me how to solve the problem?
Thank you very much.
Thanks for your released code and I have a question about this source code:
In this code, there is parameter "x_prev", but I didn't find any definition and declaration about it. I was really confused about this.
Thanks for your released code and I have a question about this source code:
In this code, there is parameter "x_prev", but I didn't find any definition and declaration about it. I was really confused about this.
error image:
Dimensions must be equal, but are 100 and 10 for 'sub' (op: 'Sub') with input shapes: [100,4096], [100,10,4096].
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