diff options
| -rwxr-xr-x | ncdcgan.py | 22 |
1 files changed, 12 insertions, 10 deletions
@@ -234,19 +234,21 @@ class nCDCGAN(): fig.savefig("images/%d.png" % epoch) plt.close() - def generate_data(self): - noise_train = np.random.normal(0, 1, (55000, 100)) - noise_test = np.random.normal(0, 1, (10000, 100)) - noise_val = np.random.normal(0, 1, (5000, 100)) + def generate_data(self, out=55000): + v_out = int(out/11) + te_out = v_out*2 + noise_train = np.random.normal(0, 1, (out, 100)) + noise_test = np.random.normal(0, 1, (te_out, 100)) + noise_val = np.random.normal(0, 1, (v_out, 100)) - labels_train = np.zeros(55000).reshape(-1, 1) - labels_test = np.zeros(10000).reshape(-1, 1) - labels_val = np.zeros(5000).reshape(-1, 1) + labels_train = np.zeros(out).reshape(-1, 1) + labels_test = np.zeros(te_out).reshape(-1, 1) + labels_val = np.zeros(v_out).reshape(-1, 1) for i in range(10): - labels_train[i*5500:-1] = i - labels_test[i*1000:-1] = i - labels_val[i*500:-1] = i + labels_train[i*int(out/10):-1] = i + labels_test[i*int(te_out/10):-1] = i + labels_val[i*int(v_out/10):-1] = i train_data = self.generator.predict([noise_train, labels_train]) test_data = self.generator.predict([noise_test, labels_test]) |