fixed type error, division returns ints now, not floats

Author: FullPint

gan.py

@@ -64,7 +64,7 @@ def __discriminator(self):
         model.add(Flatten(input_shape=self.shape))
         model.add(Dense((self.width * self.height * self.channels), input_shape=self.shape))
         model.add(LeakyReLU(alpha=0.2))
-        model.add(Dense((self.width * self.height * self.channels)/2))
+        model.add(Dense(np.int64((self.width * self.height * self.channels)/2)))
         model.add(LeakyReLU(alpha=0.2))
         model.add(Dense(1, activation='sigmoid'))
         model.summary()
@@ -86,21 +86,21 @@ def train(self, X_train, epochs=20000, batch = 32, save_interval = 100):
         for cnt in range(epochs):
 
             ## train discriminator
-            random_index = np.random.randint(0, len(X_train) - batch/2)
-            legit_images = X_train[random_index : random_index + batch/2].reshape(batch/2, self.width, self.height, self.channels)
+            random_index = np.random.randint(0, len(X_train) - np.int64(batch/2))
+            legit_images = X_train[random_index : random_index + np.int64(batch/2)].reshape(np.int64(batch/2), self.width, self.height, self.channels)
 
-            gen_noise = np.random.normal(0, 1, (batch/2, 100))
+            gen_noise = np.random.normal(0, 1, (np.int64(batch/2), 100))
             syntetic_images = self.G.predict(gen_noise)
 
             x_combined_batch = np.concatenate((legit_images, syntetic_images))
-            y_combined_batch = np.concatenate((np.ones((batch/2, 1)), np.zeros((batch/2, 1))))
+            y_combined_batch = np.concatenate((np.ones((np.int64(batch/2), 1)), np.zeros((np.int64(batch/2), 1))))
 
             d_loss = self.D.train_on_batch(x_combined_batch, y_combined_batch)
 
 
             # train generator
 
-            noise = np.random.normal(0, 1, (batch, 100))
+            noise = np.random.normal(0, 1, (batch, 10git@github.com:daymos/simple_keras_GAN.git0))
             y_mislabled = np.ones((batch, 1))
 
             g_loss = self.stacked_generator_discriminator.train_on_batch(noise, y_mislabled)