Merge branch 'master' of skozl.com:e4-gan

1 files changed, 34 insertions, 32 deletions

diff --git a/cdcgan.py b/cdcgan.py
index effc89b..a69dbc8 100755
--- a/cdcgan.py
+++ b/cdcgan.py
@@ -1,8 +1,5 @@
 from __future__ import print_function, division
-import tensorflow as keras
-
-import tensorflow as tf
-import tensorflow.keras as keras
+import keras
 from keras.datasets import mnist
 from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply
 from keras.layers import BatchNormalization, Embedding, Activation, ZeroPadding2D
@@ -39,11 +36,12 @@ class CDCGAN():
             optimizer=optimizer,
             metrics=['accuracy'])
 
-        # Build the generator
-        self.generator = self.build_generator()
-
         noise = Input(shape=(self.latent_dim,))
         label = Input(shape=(1,))
+
+        # Build the generator
+        self.generator = self.build_generator(noise, label)
+
         img = self.generator([noise, label])
 
         # For the combined model we will only train the generator
@@ -59,38 +57,44 @@ class CDCGAN():
         self.combined.compile(loss=['binary_crossentropy'],
             optimizer=optimizer)
 
-    def build_generator(self):
+    def build_generator(self, noise_in, label_in):
+        noise = Dense(7 * 7 * 256)(noise_in)
+        noise = Reshape(target_shape=(7, 7, 256))(noise)
+        noise = Conv2DTranspose(256, kernel_size=3, padding="same")(noise)
+        noise = BatchNormalization()(noise)
+        noise = Activation("relu")(noise)
 
-        model = Sequential()
+        label = Flatten()(Embedding(self.num_classes, self.latent_dim)(label_in))
+        label = Dense(7 * 7 * 256)(label)
+        label = Reshape(target_shape=(7, 7, 256))(label)
+        label = Conv2DTranspose(256, kernel_size=3, padding="same")(label)
+        label = BatchNormalization()(label)
+        label = Activation("relu")(label)
 
-        model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
-        model.add(Reshape((7, 7, 128)))
+        # Combine the two
 
-        model.add(Conv2DTranspose(256, kernel_size=3, padding="same", strides=(2,2)))
-        model.add(BatchNormalization())
-        model.add(Activation("relu"))
+        x = keras.layers.Concatenate()([noise, label])
 
-        model.add(Conv2DTranspose(128, kernel_size=3, padding="same", strides=(2,2)))
-        model.add(BatchNormalization())
-        model.add(Activation("relu"))
+        x = Conv2DTranspose(256, kernel_size=3, padding="same")(x)
+        x = BatchNormalization()(x)
+        x = Activation("relu")(x)
 
-        model.add(Conv2DTranspose(64, kernel_size=3, padding="same"))
-        model.add(BatchNormalization())
-        model.add(Activation("relu"))
+        x = Conv2DTranspose(128, kernel_size=3, padding="same", strides=(2,2))(x)
+        x = BatchNormalization()(x)
+        x = Activation("relu")(x)
+        
+        x = Conv2DTranspose(64, kernel_size=3, padding="same", strides=(2,2))(x)
+        x = BatchNormalization()(x)
+        x = Activation("relu")(x)
 
-        model.add(Conv2DTranspose(1, kernel_size=3, padding="same"))
-        model.add(Activation("tanh"))
+        x = (Conv2DTranspose(1, kernel_size=3, padding="same"))(x)
+        x = Activation("tanh")(x)
 
+        model = Model([noise_in, label_in], outputs=x)
 
-        noise = Input(shape=(self.latent_dim,))
-        label = Input(shape=(1,), dtype='int32')
-        label_embedding = Flatten()(Embedding(self.num_classes, self.latent_dim)(label))
-        model_input = multiply([noise, label_embedding])
-        img = model(model_input)
-
-        #model.summary()
+        model.summary()
 
-        return Model([noise, label], img)
+        return model
 
     def build_discriminator(self):
 
@@ -242,8 +246,6 @@ class CDCGAN():
 
       return train_data, test_data, val_data, labels_train, labels_test, labels_val
 
-'''
 if __name__ == '__main__':
     cdcgan = CDCGAN()
     cdcgan.train(epochs=4000, batch_size=32)
-'''