Update final_vae.py

final_vae.py

@@ -57,7 +57,7 @@
 #%%
 
 K.clear_session()
-tf.set_random_seed(0)
+#tf.set_random_seed(0)
 np.random.seed(0)
 
 #from keras.mode import Model
@@ -83,7 +83,7 @@
 #reparameterization trick
 def sampling(args):
     z_mean, z_log_var = args
-    epsilon = K.random_normal(shape=(K.shape(z_mean)[0],K.shape(z_mean)[1],K.shape(z_mean)[2],K.shape(z_mean)[3]), mean=0.,
+    epsilon = tf.random.normal(shape=(K.shape(z_mean)[0],K.shape(z_mean)[1],K.shape(z_mean)[2],K.shape(z_mean)[3]), mean=0.,
                               stddev=1.0)
     return z_mean + K.exp(z_log_var / 2) * epsilon
 
@@ -180,4 +180,4 @@ def schedule(epoch, lr):
 
 # vae.load_weights()
 vae.fit(X_train_norm, X_train_norm, shuffle=True, epochs=1000, batch_size=64,
-        validation_data=(X_validation_norm, X_validation_norm), callbacks=[lrate, chckpnt, csv])
+        validation_data=(X_validation_norm, X_validation_norm), callbacks=[lrate, chckpnt, csv])