Can not use both bias and batch normalization in convolution layers : <p>I use slim framework for tensorflow, because of its simplicity. 
But I want to have convolutional layer with both biases and batch normalization. 
In vanilla tensorflow, I have:</p>

<pre><code>def conv2d(input_, output_dim, k_h=5, k_w=5, d_h=2, d_w=2, name="conv2d"):
    with tf.variable_scope(name):
        w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],

    initializer=tf.contrib.layers.xavier_initializer(uniform=False))
    conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME')

    biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))
    conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())

    tf.summary.histogram("weights", w)
    tf.summary.histogram("biases", biases)

    return conv

d_bn1 = BatchNorm(name='d_bn1')
h1 = lrelu(d_bn1(conv2d(h0, df_dim + y_dim, name='d_h1_conv')))
</code></pre>

<p>and I rewrote it to slim by this:</p>

<pre><code>h1 = slim.conv2d(h0,
                 num_outputs=self.df_dim + self.y_dim,
                 scope='d_h1_conv',
                 kernel_size=[5, 5],
                 stride=[2, 2],
                 activation_fn=lrelu,
                 normalizer_fn=layers.batch_norm,
                 normalizer_params=batch_norm_params,                           
                 weights_initializer=layers.xavier_initializer(uniform=False),
                 biases_initializer=tf.constant_initializer(0.0)
                 )
</code></pre>

<p>But this code does not add bias to conv layer. 
That is because of <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/layers.py#L1025" rel="noreferrer">https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/layers.py#L1025</a> where is </p>

<pre><code>    layer = layer_class(filters=num_outputs,
                    kernel_size=kernel_size,
                    strides=stride,
                    padding=padding,
                    data_format=df,
                    dilation_rate=rate,
                    activation=None,
                    use_bias=not normalizer_fn and biases_initializer,
                    kernel_initializer=weights_initializer,
                    bias_initializer=biases_initializer,
                    kernel_regularizer=weights_regularizer,
                    bias_regularizer=biases_regularizer,
                    activity_regularizer=None,
                    trainable=trainable,
                    name=sc.name,
                    dtype=inputs.dtype.base_dtype,
                    _scope=sc,
                    _reuse=reuse)
    outputs = layer.apply(inputs)
</code></pre>

<p>in the construction of layer, which results in not having bias when using batch normalization.
Does that mean that I can not have both biases and batch normalization using slim and layers library? Or is there another way to achieve having both bias and batch normalization in layer when using slim?</p>
