diff --git a/keras_contrib/applications/densenet.py b/keras_contrib/applications/densenet.py
index e03c84c..ff72ceb 100644
--- a/keras_contrib/applications/densenet.py
+++ b/keras_contrib/applications/densenet.py
@@ -178,7 +178,7 @@ def DenseNet(input_shape=None, depth=40, nb_dense_block=3, growth_rate=12, nb_fi
 def DenseNetFCN(input_shape, nb_dense_block=5, growth_rate=16, nb_layers_per_block=4,
                 reduction=0.0, dropout_rate=0.0, weight_decay=1E-4, init_conv_filters=48,
                 include_top=True, weights=None, input_tensor=None, classes=1, activation='softmax',
-                upsampling_conv=128, upsampling_type='upsampling'):
+                upsampling_conv=128, upsampling_type='deconv'):
     '''Instantiate the DenseNet FCN architecture.
         Note that when using TensorFlow,
         for best performance you should set
@@ -216,7 +216,7 @@ def DenseNetFCN(input_shape, nb_dense_block=5, growth_rate=16, nb_layers_per_blo
             activation: Type of activation at the top layer. Can be one of 'softmax' or 'sigmoid'.
                 Note that if sigmoid is used, classes must be 1.
             upsampling_conv: number of convolutional layers in upsampling via subpixel convolution
-            upsampling_type: Can be one of 'upsampling', 'deconv' and
+            upsampling_type: Can be one of 'deconv', 'upsampling' and
                 'subpixel'. Defines type of upsampling algorithm used.
             batchsize: Fixed batch size. This is a temporary requirement for
                 computation of output shape in the case of Deconvolution2D layers.
@@ -382,10 +382,10 @@ def __dense_block(x, nb_layers, nb_filter, growth_rate, bottleneck=False, dropou
     x_list = [x]
 
     for i in range(nb_layers):
-        x = __conv_block(x, growth_rate, bottleneck, dropout_rate, weight_decay)
-        x_list.append(x)
+        conv_block = __conv_block(x, growth_rate, bottleneck, dropout_rate, weight_decay)
+        x_list.append(conv_block)
 
-        x = concatenate(x_list, axis=concat_axis)
+        x = concatenate([x, conv_block], axis=concat_axis)
 
         if grow_nb_filters:
             nb_filter += growth_rate
@@ -511,7 +511,7 @@ def __create_dense_net(nb_classes, img_input, include_top, depth=40, nb_dense_bl
 
 def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5, growth_rate=12,
                            reduction=0.0, dropout_rate=None, weight_decay=1E-4,
-                           nb_layers_per_block=4, nb_upsampling_conv=128, upsampling_type='upsampling',
+                           nb_layers_per_block=4, nb_upsampling_conv=128, upsampling_type='deconv',
                            init_conv_filters=48, input_shape=None, activation='softmax'):
     ''' Build the DenseNet model
     Args:
@@ -614,14 +614,14 @@ def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5,
         x = concatenate([t, skip_list[block_idx]], axis=concat_axis)
 
         # Dont allow the feature map size to grow in upsampling dense blocks
-        _, nb_filter, concat_list = __dense_block(x, nb_layers[nb_dense_block + block_idx + 1], nb_filter=growth_rate,
-                                                  growth_rate=growth_rate, dropout_rate=dropout_rate,
-                                                  weight_decay=weight_decay,
-                                                  return_concat_list=True, grow_nb_filters=False)
+        x_up, nb_filter, concat_list = __dense_block(x, nb_layers[nb_dense_block + block_idx + 1], nb_filter=growth_rate,
+                                                     growth_rate=growth_rate, dropout_rate=dropout_rate,
+                                                     weight_decay=weight_decay,
+                                                     return_concat_list=True, grow_nb_filters=False)
 
     if include_top:
         x = Conv2D(nb_classes, (1, 1), activation='linear', padding='same', kernel_regularizer=l2(weight_decay),
-                   use_bias=False)(x)
+                   use_bias=False)(x_up)
 
         if K.image_data_format() == 'channels_first':
             channel, row, col = input_shape
@@ -631,5 +631,7 @@ def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5,
         x = Reshape((row * col, nb_classes))(x)
         x = Activation(activation)(x)
         x = Reshape((row, col, nb_classes))(x)
+    else:
+        x = x_up
 
     return x