diff --git a/keras_contrib/backend/tensorflow_backend.py b/keras_contrib/backend/tensorflow_backend.py
index 008e2e3..6b13704 100644
--- a/keras_contrib/backend/tensorflow_backend.py
+++ b/keras_contrib/backend/tensorflow_backend.py
@@ -58,7 +58,7 @@ def deconv3d(x, kernel, output_shape, strides=(1, 1, 1),
     x = _preprocess_conv3d_input(x, dim_ordering)
     output_shape = _preprocess_deconv_output_shape(x, output_shape, dim_ordering)
     kernel = _preprocess_conv3d_kernel(kernel, dim_ordering)
-    kernel = tf.transpose(kernel, (0, 1, 3, 4, 2))
+    kernel = tf.transpose(kernel, (0, 1, 2, 4, 3))
     padding = _preprocess_border_mode(border_mode)
     strides = (1,) + strides + (1,)
 
diff --git a/tests/keras_contrib/layers/test_convolutional.py b/tests/keras_contrib/layers/test_convolutional.py
index 380a207..7b11fc7 100644
--- a/tests/keras_contrib/layers/test_convolutional.py
+++ b/tests/keras_contrib/layers/test_convolutional.py
@@ -18,12 +18,12 @@ else:
 
 @keras_test
 def test_deconvolution_3d():
-    nb_samples = 2
-    nb_filter = 2
-    stack_size = 3
-    kernel_dim1 = 10
-    kernel_dim2 = 6
-    kernel_dim3 = 5
+    nb_samples = 6
+    nb_filter = 4
+    stack_size = 2
+    kernel_dim1 = 12
+    kernel_dim2 = 10
+    kernel_dim3 = 8
 
     for batch_size in [None, nb_samples]:
         for border_mode in _convolution_border_modes:
@@ -31,13 +31,13 @@ def test_deconvolution_3d():
                 if border_mode == 'same' and subsample != (1, 1, 1):
                     continue
 
-                dim1 = conv_input_length(kernel_dim1, 3, border_mode, subsample[0])
-                dim2 = conv_input_length(kernel_dim2, 3, border_mode, subsample[1])
+                dim1 = conv_input_length(kernel_dim1, 7, border_mode, subsample[0])
+                dim2 = conv_input_length(kernel_dim2, 5, border_mode, subsample[1])
                 dim3 = conv_input_length(kernel_dim3, 3, border_mode, subsample[2])
                 layer_test(convolutional.Deconvolution3D,
                            kwargs={'nb_filter': nb_filter,
-                                   'kernel_dim1': 3,
-                                   'kernel_dim2': 3,
+                                   'kernel_dim1': 7,
+                                   'kernel_dim2': 5,
                                    'kernel_dim3': 3,
                                    'output_shape': (batch_size, nb_filter, dim1, dim2, dim3),
                                    'border_mode': border_mode,
@@ -48,8 +48,8 @@ def test_deconvolution_3d():
 
                 layer_test(convolutional.Deconvolution3D,
                            kwargs={'nb_filter': nb_filter,
-                                   'kernel_dim1': 3,
-                                   'kernel_dim2': 3,
+                                   'kernel_dim1': 7,
+                                   'kernel_dim2': 5,
                                    'kernel_dim3': 3,
                                    'output_shape': (batch_size, nb_filter, dim1, dim2, dim3),
                                    'border_mode': border_mode,
@@ -63,8 +63,8 @@ def test_deconvolution_3d():
 
                 layer_test(convolutional.Deconvolution3D,
                            kwargs={'nb_filter': nb_filter,
-                                   'kernel_dim1': 3,
-                                   'kernel_dim2': 3,
+                                   'kernel_dim1': 7,
+                                   'kernel_dim2': 5,
                                    'kernel_dim3': 3,
                                    'output_shape': (nb_filter, dim1, dim2, dim3),
                                    'border_mode': border_mode,