Change the loss of c_gan and dc_gan. (#1283)

* Change the loss of c_gan and dc_gan.

* Remove unused comment.

fluid/gan/c_gan/c_gan.py

@@ -40,7 +40,9 @@ add_arg('use_gpu',           bool,  True,       "Whether to use GPU to train.")
 
 
 def loss(x, label):
-    return fluid.layers.mean(x * (label - 0.5))
+    return fluid.layers.mean(
+        fluid.layers.sigmoid_cross_entropy_with_logits(
+            x=x, label=label))
 
 
 def train(args):
@@ -67,7 +69,10 @@ def train(args):
         g_program_test = dg_program.clone(for_test=True)
 
         dg_logit = D_cond(g_img, conditions)
-        dg_loss = loss(dg_logit, 1)
+        dg_loss = loss(
+            dg_logit,
+            fluid.layers.fill_constant_batch_size_like(
+                input=noise, dtype='float32', shape=[-1, 1], value=1.0))
 
     opt = fluid.optimizer.Adam(learning_rate=LEARNING_RATE)
 

fluid/gan/c_gan/dc_gan.py

@@ -32,15 +32,17 @@ LEARNING_RATE = 2e-4
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
-add_arg('batch_size',        int,   121,          "Minibatch size.")
+add_arg('batch_size',        int,   128,          "Minibatch size.")
 add_arg('epoch',             int,   20,        "The number of epoched to be trained.")
-add_arg('output',            str,   "./output", "The directory the model and the test result to be saved to.")
+add_arg('output',            str,   "./output_dcgan", "The directory the model and the test result to be saved to.")
 add_arg('use_gpu',           bool,  True,       "Whether to use GPU to train.")
 # yapf: enable
 
 
 def loss(x, label):
-    return fluid.layers.mean(x * (label - 0.5))
+    return fluid.layers.mean(
+        fluid.layers.sigmoid_cross_entropy_with_logits(
+            x=x, label=label))
 
 
 def train(args):
@@ -63,7 +65,10 @@ def train(args):
         g_program_test = dg_program.clone(for_test=True)
 
         dg_logit = D(g_img)
-        dg_loss = loss(dg_logit, 1)
+        dg_loss = loss(
+            dg_logit,
+            fluid.layers.fill_constant_batch_size_like(
+                input=noise, dtype='float32', shape=[-1, 1], value=1.0))
 
     opt = fluid.optimizer.Adam(learning_rate=LEARNING_RATE)
 
@@ -139,9 +144,9 @@ def train(args):
                                            fetch_list={g_img})[0]
                 total_images = np.concatenate([real_image, generated_images])
                 fig = plot(total_images)
-                msg = "Epoch ID={0}\n Batch ID={1}\n D-Loss={2}\n DG-Loss={3}\n gen={4}".format(
-                    pass_id, batch_id, d_loss_np, dg_loss_np,
-                    check(generated_images))
+                msg = "Epoch ID={0} Batch ID={1} D-Loss={2} DG-Loss={3}\n gen={4}".format(
+                    pass_id, batch_id,
+                    np.sum(d_loss_np), dg_loss_np, check(generated_images))
                 print(msg)
                 plt.title(msg)
                 plt.savefig(