lordbecerril · lordbecerril · Apr 29, 2020 · May 7, 2020
diff --git a/VGC_GAN.py b/VGC_GAN.py
@@ -3,7 +3,6 @@
         Eric Becerril-Blas    <Github: https://github.com/lordbecerril>
         Itzel Becerril        <Github: https://github.com/HadidBuilds>
         Erving Marure Sosa    <Github: https://eems20.github.io/>
-
  PURPOSE:
         We process the data and generate video game characters.
         Using a Deep Convolutional Generative Adversial Network.
@@ -95,23 +94,24 @@ def main():
         _________________________________________________________________
     '''
     generator_input = keras.Input(shape=(latent_dim,))
+    # foundations for 4x4 image
+    x = layers.Dense(256 * 4 * 4)(generator_input)
+    x = layers.LeakyReLU(0.2)(x)
+    x = layers.Reshape((4, 4, 256))(x)
 
-    x = layers.Dense(128 * 16 * 16)(generator_input)
-    x = layers.LeakyReLU()(x)
-    x = layers.Reshape((16, 16, 128))(x)
-
-    x = layers.Conv2D(256, 5, padding='same')(x)
-    x = layers.LeakyReLU()(x)
+    # upsample to 8x8
+    x = layers.Conv2DTranspose(128, 4, padding='same')(x)
+    x = layers.LeakyReLU(0.2)(x)
 
-    x = layers.Conv2DTranspose(256, 4, strides=2, padding='same')(x)
-    x = layers.LeakyReLU()(x)
+    # upsample to 16x16
+    x = layers.Conv2DTranspose(128, 4, strides=2, padding='same')(x)
+    x = layers.LeakyReLU(0.2)(x)
 
-    x = layers.Conv2D(256, 5, padding='same')(x)
-    x = layers.LeakyReLU()(x)
-    x = layers.Conv2D(256, 5, padding='same')(x)
-    x = layers.LeakyReLU()(x)
+    # upsample to 32 x32
+    x = layers.Conv2D(128, 4, padding='same')(x)
+    x = layers.LeakyReLU(0.2)(x)
 
-    x = layers.Conv2D(channels, 7, activation='tanh', padding='same')(x)
+    x = layers.Conv2D(channels, 3, activation='tanh', padding='same')(x)
     generator = keras.models.Model(generator_input, x)
     generator.summary()
     """
@@ -154,19 +154,21 @@ def main():
     print("Let us build our discriminator")
     print("\n")
     discriminator_input = layers.Input(shape = (height, width, channels))
-    x = layers.Conv2D(128, 3)(discriminator_input)
+    # normal
+    x = layers.Conv2D(64, 3)(discriminator_input)
     x = layers.LeakyReLU(0.2)(x)
-    x = layers.Conv2D(128, 4, strides = 2)(x)
-    x = layers.LeakyReLU()(x)
-    x = layers.Conv2D(128, 4, strides = 2)(x)
-    x = layers.LeakyReLU()(x)
-    x = layers.Conv2D(128, 4, strides = 2)(x)
-    x = layers.LeakyReLU()(x)
-
+    # downsample
+    x = layers.Conv2D(128, 3, strides = 2)(x)
+    x = layers.LeakyReLU(0.2)(x)
+    # downsample
+    x = layers.Conv2D(128, 3, strides = 2)(x)
+    x = layers.LeakyReLU(0.2)(x)
+    # downsample
+    x = layers.Conv2D(256, 3, strides = 2)(x)
+    x = layers.LeakyReLU(0.2)(x)
+    # classifier
     x = layers.Flatten()(x)
-
     x = layers.Dropout(0.4)(x)
-
     x = layers.Dense(1, activation = 'sigmoid')(x)
 
     discriminator = keras.models.Model(discriminator_input, x)
@@ -178,7 +180,6 @@ def main():
         To make the backpropagation possible for the Generator, we create new
         network in Keras, which is Generator followed by Discriminator. In this
         network, we freeze all the weights so that its weight do not changes.
-
         GAN Architecture:
         Model: "model_3"
         _________________________________________________________________
@@ -219,7 +220,7 @@ def main():
     x_train = data_train_gan
     # We will do 10000 iterations. Every iteration we process 40 batches
     iterations = 10000
-    batch_size = 20
+    batch_size = 128 #50?
     save_dir = './train_output/'
 
 
@@ -254,7 +255,7 @@ def main():
             start = 0
 
         # Print the loss and also save the faces generated by generator into train_output
-        if step % 10 == 0:
+        if step % 100 == 0:
             print('discriminator loss:', d_loss)
             print('advesarial loss:', a_loss)
             fig, axes = plt.subplots(2, 2)

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