diff --git a/discriminator.py b/discriminator.py
index 691fd18..4506b5d 100644
--- a/discriminator.py
+++ b/discriminator.py
@@ -31,9 +31,14 @@ def disc_block(block_idx, input_, kwidth, nfmaps, bnorm, activation,
                         print('D block {} input shape: {}'
                               ''.format(block_idx, input_.get_shape()),
                               end=' *** ')
+                    bias_init = None
+                    if self.bias_D_conv:
+                        if not reuse:
+                            print('biasing D conv', end=' *** ')
+                        bias_init = tf.constant_initializer(0.)
                     downconv_init = tf.truncated_normal_initializer(stddev=0.02)
                     hi_a = downconv(input_, nfmaps, kwidth=kwidth, pool=pooling,
-                                    init=downconv_init)
+                                    init=downconv_init, bias_init=bias_init)
                     if not reuse:
                         print('downconved shape: {} '
                               ''.format(hi_a.get_shape()), end=' *** ')
diff --git a/generator.py b/generator.py
index 377724f..b2cef4f 100644
--- a/generator.py
+++ b/generator.py
@@ -198,14 +198,14 @@ def make_z(shape, mean=0., std=1., name='z'):
                 h_i_dim = h_i.get_shape().as_list()
                 out_shape = [h_i_dim[0], h_i_dim[1] * 2, layer_depth]
                 bias_init = None
-                if segan.bias_deconv:
-                    if is_ref:
-                        print('Biasing deconv in G')
-                    bias_init = tf.constant_initializer(0.)
                 # deconv
                 if segan.deconv_type == 'deconv':
                     if is_ref:
                         print('-- Transposed deconvolution type --')
+                        if segan.bias_deconv:
+                            print('Biasing deconv in G')
+                    if segan.bias_deconv:
+                        bias_init = tf.constant_initializer(0.)
                     h_i_dcv = deconv(h_i, out_shape, kwidth=kwidth, dilation=2,
                                      init=tf.truncated_normal_initializer(stddev=0.02),
                                      bias_init=bias_init,
@@ -213,7 +213,11 @@ def make_z(shape, mean=0., std=1., name='z'):
                 elif segan.deconv_type == 'nn_deconv':
                     if is_ref:
                         print('-- NN interpolated deconvolution type --')
-                    h_i_dcv = nn_deconv(h_i, kwdith=kwidth, dilation=2,
+                        if segan.bias_deconv:
+                            print('Biasing deconv in G')
+                    if segan.bias_deconv:
+                        bias_init = 0.
+                    h_i_dcv = nn_deconv(h_i, kwidth=kwidth, dilation=2,
                                         init=tf.truncated_normal_initializer(stddev=0.02),
                                         bias_init=bias_init,
                                         name='dec_{}'.format(layer_idx))
diff --git a/ops.py b/ops.py
index d36e62e..a970985 100644
--- a/ops.py
+++ b/ops.py
@@ -243,10 +243,10 @@ def repeat_elements(x, rep, axis):
                          'Typically you need to pass a fully-defined '
                          '`input_shape` argument to your first layer.')
     # slices along the repeat axis
-    splits = tf.split(value=x, num_or_size_splits=x_shape[axis], axis=axis)
+    splits = tf.split(split_dim=axis, num_split=x_shape[axis], value=x)
     # repeat each slice the given number of reps
     x_rep = [s for s in splits for _ in range(rep)]
-    return concatenate(x_rep, axis)
+    return tf.concat(axis, x_rep)
 
 def nn_deconv(x, kwidth=5, dilation=2, init=None, uniform=False,
               bias_init=None, name='nn_deconv1d'):