From 4aa41625bf0e32eecf96b825db2d8f273fa40183 Mon Sep 17 00:00:00 2001
From: Francois Chollet <francois.chollet@gmail.com>
Date: Tue, 11 Apr 2017 13:43:04 -0700
Subject: [PATCH] Switch variational examples to new API.

---
 examples/variational_autoencoder.py        | 17 ++++++-----------
 examples/variational_autoencoder_deconv.py | 18 ++++++------------
 2 files changed, 12 insertions(+), 23 deletions(-)

diff --git a/examples/variational_autoencoder.py b/examples/variational_autoencoder.py
index f499d966f..fd395b17f 100644
--- a/examples/variational_autoencoder.py
+++ b/examples/variational_autoencoder.py
@@ -42,11 +42,6 @@ h_decoded = decoder_h(z)
 x_decoded_mean = decoder_mean(h_decoded)
 
 
-# placeholder loss
-def zero_loss(y_true, y_pred):
-    return K.zeros_like(y_pred)
-
-
 # Custom loss layer
 class CustomVariationalLayer(Layer):
     def __init__(self, **kwargs):
@@ -63,12 +58,12 @@ class CustomVariationalLayer(Layer):
         x_decoded_mean = inputs[1]
         loss = self.vae_loss(x, x_decoded_mean)
         self.add_loss(loss, inputs=inputs)
-        # we don't use this output, but it has to have the correct shape:
-        return K.ones_like(x)
+        # We won't actually use the output.
+        return x
 
-loss_layer = CustomVariationalLayer()([x, x_decoded_mean])
-vae = Model(x, [loss_layer])
-vae.compile(optimizer='rmsprop', loss=[zero_loss])
+y = CustomVariationalLayer()([x, x_decoded_mean])
+vae = Model(x, y)
+vae.compile(optimizer='rmsprop', loss=None)
 
 
 # train the VAE on MNIST digits
@@ -79,7 +74,7 @@ x_test = x_test.astype('float32') / 255.
 x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
 x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
 
-vae.fit(x_train, x_train,
+vae.fit(x_train,
         shuffle=True,
         epochs=epochs,
         batch_size=batch_size,
diff --git a/examples/variational_autoencoder_deconv.py b/examples/variational_autoencoder_deconv.py
index 74426203d..f5aa1224d 100644
--- a/examples/variational_autoencoder_deconv.py
+++ b/examples/variational_autoencoder_deconv.py
@@ -106,11 +106,6 @@ x_decoded_relu = decoder_deconv_3_upsamp(deconv_2_decoded)
 x_decoded_mean_squash = decoder_mean_squash(x_decoded_relu)
 
 
-# placeholder loss
-def zero_loss(y_true, y_pred):
-    return K.zeros_like(y_pred)
-
-
 # Custom loss layer
 class CustomVariationalLayer(Layer):
     def __init__(self, **kwargs):
@@ -129,14 +124,13 @@ class CustomVariationalLayer(Layer):
         x_decoded_mean_squash = inputs[1]
         loss = self.vae_loss(x, x_decoded_mean_squash)
         self.add_loss(loss, inputs=inputs)
-        # we don't use this output, but it has to have the correct shape:
-        return K.ones_like(x)
+        # We don't use this output.
+        return x
 
 
-loss_layer = CustomVariationalLayer()([x, x_decoded_mean_squash])
-
-vae = Model(x, [loss_layer])
-vae.compile(optimizer='rmsprop', loss=zero_loss)
+y = CustomVariationalLayer()([x, x_decoded_mean_squash])
+vae = Model(x, y)
+vae.compile(optimizer='rmsprop', loss=None)
 vae.summary()
 
 # train the VAE on MNIST digits
@@ -149,7 +143,7 @@ x_test = x_test.reshape((x_test.shape[0],) + original_img_size)
 
 print('x_train.shape:', x_train.shape)
 
-vae.fit(x_train, x_train,
+vae.fit(x_train,
         shuffle=True,
         epochs=epochs,
         batch_size=batch_size,