import keras
import cv2
import numpy as np

from keras.models import Sequential
from keras.layers import Dense, Activation

def generater_model():
	model = Sequential()
	model.add(Dense(32, input_dim = 32 * 32))
	return model

def discriminator_model():
	model = Sequential()
	return model


@@ -115,16 +115,62 @@
 tivation
+%0Afrom keras.layers.convolutional import Conv2D
 %0A%0Adef ge
@@ -174,17 +174,17 @@
  generat
-e
+o
 r_model(
@@ -245,80 +245,296 @@
 m = 
-32 * 32))%0A%09return model%0A%0Adef discriminator_model():%0A%09model = Sequential(
+100))%0A%09model.add(Activation('sigmoid'))%0A%09model.add(Dense(64))%0A%09model.add(Activation('relu'))%0A%0A%09return model%0A%0Adef discriminator_model():%0A%09model = Sequential()%0A%09return model%0A%0Adef adversial_model(generator, discriminator):%0A%09model = Sequential()%0A%09model.add(generator)%0A%09model.add(discriminator
 )%0A%09r

update model