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

1 files changed, 11 insertions, 17 deletions

diff --git a/cgan.py b/cgan.py
index 45b9bb9..6406244 100644..100755
--- a/cgan.py
+++ b/cgan.py
@@ -1,23 +1,21 @@
 from __future__ import print_function, division
 import tensorflow.keras as keras
 import tensorflow as tf
-from tensorflow.keras.datasets import mnist
-from tensorflow.keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply
-from tensorflow.keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D
-from tensorflow.keras.layers import LeakyReLU
-from tensorflow.keras.layers import UpSampling2D, Conv2D
-from tensorflow.keras.models import Sequential, Model
-from tensorflow.keras.optimizers import Adam
+from keras.datasets import mnist
+from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply
+from keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D
+from keras.layers.advanced_activations import LeakyReLU
+from keras.layers.convolutional import UpSampling2D, Conv2D
+from keras.models import Sequential, Model
+from keras.optimizers import Adam
 import matplotlib.pyplot as plt
 from IPython.display import clear_output
 from tqdm import tqdm
 
-from lib.virtual_batch import VirtualBatchNormalization
-
 import numpy as np
 
 class CGAN():
-    def __init__(self, dense_layers = 3, virtual_batch_normalization=False):
+    def __init__(self, dense_layers = 3):
         # Input shape
         self.img_rows = 28
         self.img_cols = 28
@@ -26,7 +24,6 @@ class CGAN():
         self.num_classes = 10
         self.latent_dim = 100
         self.dense_layers = dense_layers
-        self.virtual_batch_normalization = virtual_batch_normalization
 
         optimizer = Adam(0.0002, 0.5)
 
@@ -66,10 +63,7 @@ class CGAN():
             output_size = 2**(8+i)
             model.add(Dense(output_size, input_dim=self.latent_dim))
             model.add(LeakyReLU(alpha=0.2))
-            if self.virtual_batch_normalization:
-                model.add(VirtualBatchNormalization(momentum=0.8))
-            else:
-                model.add(BatchNormalization(momentum=0.8))
+            model.add(BatchNormalization(momentum=0.8))
 
         model.add(Dense(np.prod(self.img_shape), activation='tanh'))
         model.add(Reshape(self.img_shape))
@@ -142,7 +136,6 @@ class CGAN():
             # Sample noise as generator input
             noise = np.random.normal(0, 1, (batch_size, 100))
 
-            tf.keras.backend.get_session().run(tf.global_variables_initializer()) 
             # Generate a half batch of new images
             gen_imgs = self.generator.predict([noise, labels])
 
@@ -224,9 +217,10 @@ class CGAN():
 
       return train_data, test_data, val_data, labels_train, labels_test, labels_val
 
+
 '''
 if __name__ == '__main__':
-    cgan = CGAN(dense_layers=1, virtual_batch_normalization=True)
+    cgan = CGAN(dense_layers=1)
     cgan.train(epochs=7000, batch_size=32, sample_interval=200)
     train, test, tr_labels, te_labels = cgan.generate_data()
     print(train.shape, test.shape)