Fix Python 3 compatibility

MNIST_GAN/MNIST_GAN.py

@@ -72,14 +72,14 @@ y[ntrain:, 0] = 1             # class 0 for generated images
 discriminator.fit(X, y, epochs=1, batch_size=128)
 
 # - Create a dataset of 5000 real test images and 5000 fake images.
-no = np.random.choice(10000, size=ntrain/2, replace='False')
+no = np.random.choice(10000, size=ntrain//2, replace='False')
 real_test = X_test[no, :, :, :]   # sample real images from test set
-noise_gen = np.random.uniform(0, 1, size=[ntrain/2, latent_dim])
+noise_gen = np.random.uniform(0, 1, size=[ntrain//2, latent_dim])
 generated_images = generator.predict(noise_gen)    # generate fake images with untrained generator
 Xt = np.concatenate((real_test, generated_images))
 yt = np.zeros([ntrain, 2])   # class vector: one-hot encoding
-yt[:ntrain/2, 1] = 1         # class 1 for real images
-yt[ntrain/2:, 0] = 1         # class 0 for generated images
+yt[:ntrain//2, 1] = 1         # class 1 for real images
+yt[ntrain//2:, 0] = 1         # class 0 for generated images
 
 # - Evaluate the test accuracy of your network.
 pretrain_loss, pretrain_acc = discriminator.evaluate(Xt, yt, verbose=0, batch_size=128)
@@ -99,7 +99,7 @@ def train_for_n(epochs=1, batch_size=32):
         generated_images = generator.predict(noise)
         plot_images(generated_images, fname=log_dir + '/generated_images_' + str(epoch))
 
-        iterations_per_epoch = 60000/batch_size    # number of training steps per epoch
+        iterations_per_epoch = 60000//batch_size    # number of training steps per epoch
         perm = np.random.choice(60000, size=60000, replace='False')
 
         for i in range(iterations_per_epoch):