models/dense_net.py
def bias_variable(self, shape, name='bias'):
        """
        Creates bias terms for a fully-connected layer, initialized to 0.0.

        Args:
            shape: `list` of `int`, shape of the bias matrix;
            name: `str`, a name for identifying the bias matrix.
        """
        initial = tf.constant(0.0, shape=shape)
        return tf.get_variable(name, initializer=initial)

        Creates bias terms for a fully-connected layer, initialized to 0.0.

        Args:
            shape: `list` of `int`, shape of the bias matrix;
            name: `str`, a name for identifying the bias matrix.
        
Creates bias terms for a fully-connected layer, initialized to 0.0.
Creates bias terms for a fully - connected layer initialized to 0 . 0 .
def bias_variable(self, shape, name='bias'):
        initial = tf.constant(0.0, shape=shape)
        return tf.get_variable(name, initializer=initial)
def bias_variable ( self , shape , name = 'bias' ) : initial = tf . constant ( 0.0 , shape = shape ) return tf . get_variable ( name , initializer = initial )
Creates bias terms for a fully-connected layer, initialized to 0.0.
Creates bias terms for a fully - connected layer initialized to 0 . 0 .
"""
        Creates bias terms for a fully-connected layer, initialized to 0.0.

        Args:
            shape: `list` of `int`, shape of the bias matrix;
            name: `str`, a name for identifying the bias matrix.
        """
self
shape
`list` of `int`, shape of the bias matrix.
` list ` of ` int ` shape of the bias matrix .
name
`str`, a name for identifying the bias matrix.
` str ` a name for identifying the bias matrix .