models/dense_net.py
def avg_pool(self, _input, k):
        """
        Performs average pooling on a given input (_input),
        within square kernels of side k and stride k.

        Args:
            _input: tensor, the operation's input;
            k: `int`, the size and stride for the kernels.
        """
        ksize = [1, k, k, 1]
        strides = [1, k, k, 1]
        padding = 'VALID'
        output = tf.nn.avg_pool(_input, ksize, strides, padding)
        return output

        Performs average pooling on a given input (_input),
        within square kernels of side k and stride k.

        Args:
            _input: tensor, the operation's input;
            k: `int`, the size and stride for the kernels.
        
Performs average pooling on a given input (_input),
within square kernels of side k and stride k.
Performs average pooling on a given input ( _input ) within square kernels of side k and stride k .
def avg_pool(self, _input, k):
        ksize = [1, k, k, 1]
        strides = [1, k, k, 1]
        padding = 'VALID'
        output = tf.nn.avg_pool(_input, ksize, strides, padding)
        return output
def avg_pool ( self , _input , k ) : ksize = [ 1 , k , k , 1 ] strides = [ 1 , k , k , 1 ] padding = 'VALID' output = tf . nn . avg_pool ( _input , ksize , strides , padding ) return output
Performs average pooling on a given input (_input),
within square kernels of side k and stride k.
Performs average pooling on a given input ( _input ) within square kernels of side k and stride k .
"""
        Performs average pooling on a given input (_input),
        within square kernels of side k and stride k.

        Args:
            _input: tensor, the operation's input;
            k: `int`, the size and stride for the kernels.
        """
self
_input
tensor, the operation's input.
tensor the operation ' s input .
k
`int`, the size and stride for the kernels.
` int ` the size and stride for the kernels .