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| author | nunzip <np.scarh@gmail.com> | 2019-02-27 22:43:40 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2019-02-27 22:43:40 +0000 |
| commit | 35216b30bdff05c04fd4846cfc8433b97218139f (patch) | |
| tree | 447e029c1083154d65a9591ca79c3db41aefa02f | |
| parent | 765a74824171f9c3d1ab80d561debf2053c3dc06 (diff) | |
| download | e4-gan-35216b30bdff05c04fd4846cfc8433b97218139f.tar.gz e4-gan-35216b30bdff05c04fd4846cfc8433b97218139f.tar.bz2 e4-gan-35216b30bdff05c04fd4846cfc8433b97218139f.zip | |
Reformat labels output
| -rw-r--r-- | cgan.py | 4 |
1 files changed, 4 insertions, 0 deletions
@@ -206,6 +206,10 @@ class CGAN(): labels_test[i*1000:] = i labels_val[i*500:] = i + labels_train = keras.utils.to_categorical(labels_train, 10) + labels_test = keras.utils.to_categorical(labels_test, 10) + labels_val = keras.utils.to_categorical(labels_val, 10) + train_data = self.generator.predict([noise_train, labels_train]) test_data = self.generator.predict([noise_test, labels_test]) val_data = self.generator.predict([noise_val, labels_val]) |