diff --git a/keras/layers/convolutional.py b/keras/layers/convolutional.py
index 019b66d14..55b0e3ec6 100644
--- a/keras/layers/convolutional.py
+++ b/keras/layers/convolutional.py
@@ -1045,7 +1045,8 @@ class UpSampling1D(Layer):
         super(UpSampling1D, self).__init__(**kwargs)
 
     def get_output_shape_for(self, input_shape):
-        return (input_shape[0], self.length * input_shape[1], input_shape[2])
+        length = self.length * input_shape[1] if input_shape[1] is not None else None
+        return (input_shape[0], length, input_shape[2])
 
     def call(self, x, mask=None):
         output = K.repeat_elements(x, self.length, axis=1)
@@ -1094,14 +1095,18 @@ class UpSampling2D(Layer):
 
     def get_output_shape_for(self, input_shape):
         if self.dim_ordering == 'th':
+            width = self.size[0] * input_shape[2] if input_shape[2] is not None else None
+            height = self.size[1] * input_shape[3] if input_shape[3] is not None else None
             return (input_shape[0],
                     input_shape[1],
-                    self.size[0] * input_shape[2],
-                    self.size[1] * input_shape[3])
+                    width,
+                    height)
         elif self.dim_ordering == 'tf':
+            width = self.size[0] * input_shape[1] if input_shape[1] is not None else None
+            height = self.size[1] * input_shape[2] if input_shape[2] is not None else None
             return (input_shape[0],
-                    self.size[0] * input_shape[1],
-                    self.size[1] * input_shape[2],
+                    width,
+                    height,
                     input_shape[3])
         else:
             raise Exception('Invalid dim_ordering: ' + self.dim_ordering)
@@ -1153,16 +1158,22 @@ class UpSampling3D(Layer):
 
     def get_output_shape_for(self, input_shape):
         if self.dim_ordering == 'th':
+            dim1 = self.size[0] * input_shape[2] if input_shape[2] is not None else None
+            dim2 = self.size[1] * input_shape[3] if input_shape[3] is not None else None
+            dim3 = self.size[2] * input_shape[4] if input_shape[4] is not None else None
             return (input_shape[0],
                     input_shape[1],
-                    self.size[0] * input_shape[2],
-                    self.size[1] * input_shape[3],
-                    self.size[2] * input_shape[4])
+                    dim1,
+                    dim2,
+                    dim3)
         elif self.dim_ordering == 'tf':
+            dim1 = self.size[0] * input_shape[1] if input_shape[1] is not None else None
+            dim2 = self.size[1] * input_shape[2] if input_shape[2] is not None else None
+            dim3 = self.size[2] * input_shape[3] if input_shape[3] is not None else None
             return (input_shape[0],
-                    self.size[0] * input_shape[1],
-                    self.size[1] * input_shape[2],
-                    self.size[2] * input_shape[3],
+                    dim1,
+                    dim2,
+                    dim3,
                     input_shape[4])
         else:
             raise Exception('Invalid dim_ordering: ' + self.dim_ordering)