multicnet.py
def conv2d(self, x, W, stride = 1):
        """
        We use a stride size of 1 and zero padded convolutions
        to ensure we get the same output size as it was our input
        """
        return tf.nn.conv2d(x, W, strides = [1, stride, stride, 1], padding = "SAME")

        We use a stride size of 1 and zero padded convolutions
        to ensure we get the same output size as it was our input
        
We use a stride size of 1 and zero padded convolutions
to ensure we get the same output size as it was our input
We use a stride size of 1 and zero padded convolutions to ensure we get the same output size as it was our input
def conv2d(self, x, W, stride = 1):
        return tf.nn.conv2d(x, W, strides = [1, stride, stride, 1], padding = "SAME")
def conv2d ( self , x , W , stride = 1 ) : return tf . nn . conv2d ( x , W , strides = [ 1 , stride , stride , 1 ] , padding = "SAME" )
We use a stride size of 1 and zero padded convolutions
to ensure we get the same output size as it was our input
We use a stride size of 1 and zero padded convolutions to ensure we get the same output size as it was our input
"""
        We use a stride size of 1 and zero padded convolutions
        to ensure we get the same output size as it was our input
        """
self
x
W
stride