Added readme

Tickloop · Jan 15, 2022 · c98fd58 · c98fd58
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+# Generative Adverserial Network - MNIST
+
+Generative Adverserial Networks are powerful tools for generative modeling. The new technique of training deep neural networks using adverserial networks has given generative modeling new avenues of research.
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+To display the power of GAN's this repository hosts the code to load and train a GAN on the MNIST dataset. The results of generation can be viewed here.
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+<img width="100" src="./gifs/0.gif"><img width="100" src="./gifs/1.gif"><img width="100" src="./gifs/2.gif"><img width="100" src="./gifs/3.gif"><img width="100" src="./gifs/4.gif">
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+<img width="100" src="./gifs/5.gif"><img width="100" src="./gifs/6.gif"><img width="100" src="./gifs/7.gif"><img width="100" src="./gifs/8.gif"><img width="100" src="./gifs/9.gif">
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+The dataset that we used is the popular MNIST dataset in the form of images: https://www.kaggle.com/scolianni/mnistasjpg
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+Here is the link to the original paper: https://arxiv.org/abs/1406.2661
diff --git a/gif.py b/gif.py
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+import imageio
+
+for i in range(10):
+    gif = []
+    path = f"samples/{i}"
+
+    for filename in range(100):
+            gif.append(imageio.imread(f"{path}/sample_epoch_{filename}.jpg"))
+
+    imageio.mimsave(f"gifs/{i}.gif", gif, fps=24)
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