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Hantian Zhang
Hantian Zhang committed Jul 25, 2017
1 parent f1cca46 commit db75abf718b763bd72d1fe5d100c5084d67a96c4
Showing with 10 additions and 14 deletions.
  1. +1 −5 README.md
  2. +2 −2 config.py
  3. +7 −7 model.py
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@@ -1,5 +1,5 @@
# GalaxyGAN_python
This project is the implementation of the Paper "Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit" on python.
This project is the implementation of the Paper "Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit" on python. It is the python version of https://github.com/SpaceML/GalaxyGAN. This python version doesn't include deconvolution part of the paper.
## Amazon EC2 Setup
@@ -78,7 +78,3 @@ Before you try to test your model, you should modify the model path in the confi
python test.py gpu=1
```
The results can be seen in the folder "test".
##Acknowledge
This project is the python version of https://github.com/SpaceML/GalaxyGAN. Thanks for his work!
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@@ -1,7 +1,7 @@
class Config:
data_path = "/figures/train"
data_path = "figures"
model_path = ""
output_path = "/results/train"
output_path = "results"
img_size = 424
adjust_size = 500
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@@ -54,19 +54,19 @@ def generator(self, cond):
size =(size+1)/2
d1 = deconv2d(tf.nn.relu(e8), [1,num[1],num[1],feature*8], name="d1")
d1 = tf.concat(3, [tf.nn.dropout(batch_norm(d1, "d1"), 0.5), e7])
d1 = tf.concat([tf.nn.dropout(batch_norm(d1, "d1"), 0.5), e7], 3)
d2 = deconv2d(tf.nn.relu(d1), [1,num[2],num[2],feature*8], name="d2")
d2 = tf.concat(3, [tf.nn.dropout(batch_norm(d2, "d2"), 0.5), e6])
d2 = tf.concat([tf.nn.dropout(batch_norm(d2, "d2"), 0.5), e6], 3)
d3 = deconv2d(tf.nn.relu(d2), [1,num[3],num[3],feature*8], name="d3")
d3 = tf.concat(3, [tf.nn.dropout(batch_norm(d3, "d3"), 0.5), e5])
d3 = tf.concat([tf.nn.dropout(batch_norm(d3, "d3"), 0.5), e5], 3)
d4 = deconv2d(tf.nn.relu(d3), [1,num[4],num[4],feature*8], name="d4")
d4 = tf.concat(3, [batch_norm(d4, "d4"), e4])
d4 = tf.concat([batch_norm(d4, "d4"), e4], 3)
d5 = deconv2d(tf.nn.relu(d4), [1,num[5],num[5],feature*4], name="d5")
d5 = tf.concat(3, [batch_norm(d5, "d5"), e3])
d5 = tf.concat([batch_norm(d5, "d5"), e3], 3)
d6 = deconv2d(tf.nn.relu(d5), [1,num[6],num[6],feature*2], name="d6")
d6 = tf.concat(3, [batch_norm(d6, "d6"), e2])
d6 = tf.concat([batch_norm(d6, "d6"), e2], 3)
d7 = deconv2d(tf.nn.relu(d6), [1,num[7],num[7],feature], name="d7")
d7 = tf.concat(3, [batch_norm(d7, "d7"), e1])
d7 = tf.concat([batch_norm(d7, "d7"), e1], 3)
d8 = deconv2d(tf.nn.relu(d7), [1,num[8],num[8],conf.img_channel], name="d8")
return tf.nn.tanh(d8)

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