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| author | nunzip <np.scarh@gmail.com> | 2019-02-27 23:10:04 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2019-02-27 23:10:04 +0000 |
| commit | 723ac9bb7094b482a2efc6bc97139409d2bde9c4 (patch) | |
| tree | 925e642e00affa87163a79ece23c1cbcddf6c111 | |
| parent | 6cf023576314b4aa39a8d5d5dc60e656958dde6d (diff) | |
| download | e4-gan-723ac9bb7094b482a2efc6bc97139409d2bde9c4.tar.gz e4-gan-723ac9bb7094b482a2efc6bc97139409d2bde9c4.tar.bz2 e4-gan-723ac9bb7094b482a2efc6bc97139409d2bde9c4.zip | |
Save images for all classes instead of zero only
| -rw-r--r-- | cgan.py | 6 |
1 files changed, 4 insertions, 2 deletions
@@ -175,9 +175,11 @@ class CGAN(): r, c = 2, 5 noise = np.random.normal(0, 1, (r * c, 100)) sampled_labels = np.arange(0, 10).reshape(-1, 1) - dummy_labels = np.zeros(32).reshape(-1, 1) + + #using dummy_labels would just print zeros to help identify image quality + #dummy_labels = np.zeros(32).reshape(-1, 1) - gen_imgs = self.generator.predict([noise, dummy_labels]) + gen_imgs = self.generator.predict([noise, sampled_labels]) # Rescale images 0 - 1 gen_imgs = 0.5 * gen_imgs + 0.5 |