pred_dict = {"input_generator": [np.random.uniform(0., 1., size=[100])]}
g = gen_net.predict(pred_dict)[0]
temp = [[ii, ii, ii] for ii in list(g)]
a[0][i].imshow(np.reshape(temp, (28, 28, 3)))
temp = [[ii, ii, ii] for ii in list(g)]
a[1][i].imshow(np.reshape(temp, (28, 28, 3)))
f.show()
plt.draw()
After Change
loss_gan = gan_net.fit_batch({"input_stacked": noise},
{"target_stacked": Y_batch})
if i % 10 == 0:
print("Loss Disc", loss_disc)
print("Loss GAN", loss_gan)
f, a = plt.subplots(1, 10)
for i in range(10):
pred_dict = {"input_generator": [np.random.uniform(-1., 1., size=[100])]}
g = gen_net.predict(pred_dict)[0]
