Comments (5)
No description provided.
change x = tf.layers.dense(x, units=inputs_decoder * 2 + 1, activation=lrelu)
to x = tf.layers.dense(x, units=inputs_decoder * 2, activation=lrelu)
in decoder()
will solve this problem.
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I am also getting the same issue
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@AdityaAmrutiya @alla15747 you solve it?
Iam trying to run face creation, its prepared to generate 40x40 pixel images.. I want to generate 200x200 pixel so I change 40,40,3 values to 200,200,3 to get 200pixel images but Iam facing this error :
InvalidArgumentError: Input to reshape is a tensor with 307200 values, but the requested shape requires a multiple of 120000
[[Node: discriminator_1/Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](generator/conv2d_transpose_3/Sigmoid, discriminator/Reshape/shape)]]
any suggestions,In which line I need to make correction?
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Hi @u112358 ,
i can't find the line x = tf.layers.dense(x, units=inputs_decoder * 2 + 1, activation=lrelu)
in script (DCGAN-face-creation.ipynb), the code you mentioned is in VAE.ipynb.
i only need to change the size of the generated images from default size 40x40 to any other size example between: 100x100 or 200x200 pixels. When i tried to change the sizes i'm getting the error above. Do you have any idea how to change the sizes of images ?
thanks
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No description provided.
change
x = tf.layers.dense(x, units=inputs_decoder * 2 + 1, activation=lrelu)
tox = tf.layers.dense(x, units=inputs_decoder * 2, activation=lrelu)
indecoder()
will solve this problem.
This is a full year later so sorry for reviving this but I was wondering if you could provide any insight on why this fix works?
Thanks!
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Related Issues (10)
- Updating the README
- How to generate 200*200 pxl face images?
- Additional Deep Learning Notebooks or Scripts with Python
- Find bugs and improve the existing code HOT 1
- dst_loss not defined HOT 1
- "Ambiguous dimension: 24.5" in decoder call HOT 5
- Error while reshaping the tensor in one of CNN layers.
- How did you choose these values??
- Cannot feed value of shape (0,) for Tensor u'X:0', which has shape '(?, 40, 40, 3)'
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