models/dense_net.py
def batch_norm(self, _input, scope='BatchNorm'):
        """
        Performs batch normalisation on a given input (_input).

        Args:
            _input: tensor, the operation's input.
            scope: `str`, a variable scope for the operation.
        """
        output = tf.contrib.layers.batch_norm(
            _input, scale=True, is_training=self.is_training,
            updates_collections=None, scope=scope)
        return output

        Performs batch normalisation on a given input (_input).

        Args:
            _input: tensor, the operation's input.
            scope: `str`, a variable scope for the operation.
        
Performs batch normalisation on a given input (_input).
Performs batch normalisation on a given input ( _input ) .
def batch_norm(self, _input, scope='BatchNorm'):
        output = tf.contrib.layers.batch_norm(
            _input, scale=True, is_training=self.is_training,
            updates_collections=None, scope=scope)
        return output
def batch_norm ( self , _input , scope = 'BatchNorm' ) : output = tf . contrib . layers . batch_norm ( _input , scale = True , is_training = self . is_training , updates_collections = None , scope = scope ) return output
Performs batch normalisation on a given input (_input).
Performs batch normalisation on a given input ( _input ) .
"""
        Performs batch normalisation on a given input (_input).

        Args:
            _input: tensor, the operation's input.
            scope: `str`, a variable scope for the operation.
        """
self
_input
tensor, the operation's input.
tensor the operation ' s input .
scope
`str`, a variable scope for the operation.
` str ` a variable scope for the operation .