models/dense_net.py
def conv2d(self, _input, out_features, kernel_size,
               strides=[1, 1, 1, 1], padding='SAME'):
        """
        Creates a 2d convolutional filter layer (applies a certain number of
        kernels on some input features to obtain output features).
        Returns the output of the layer and a reference to its filter.

        Args:
            _input: tensor, the operation's input;
            out_features: `int`, number of feature maps at the output;
            kernel_size: `int`, size of the square kernels (their side);
            strides: `list` of `int`, strides in each direction for kernels;
            padding: `str`, should we use padding ('SAME') or not ('VALID').
        """
        in_features = int(_input.get_shape()[-1])
        filter_ref = self.weight_variable_msra(
            [kernel_size, kernel_size, in_features, out_features],
            name='filter')
        output = tf.nn.conv2d(_input, filter_ref, strides, padding)
        return output, filter_ref

        Creates a 2d convolutional filter layer (applies a certain number of
        kernels on some input features to obtain output features).
        Returns the output of the layer and a reference to its filter.

        Args:
            _input: tensor, the operation's input;
            out_features: `int`, number of feature maps at the output;
            kernel_size: `int`, size of the square kernels (their side);
            strides: `list` of `int`, strides in each direction for kernels;
            padding: `str`, should we use padding ('SAME') or not ('VALID').
        
Creates a 2d convolutional filter layer (applies a certain number of
kernels on some input features to obtain output features).
Returns the output of the layer and a reference to its filter.
Creates a 2d convolutional filter layer ( applies a certain number of kernels on some input features to obtain output features ) . Returns the output of the layer and a reference to its filter .
def conv2d(self, _input, out_features, kernel_size,
               strides=[1, 1, 1, 1], padding='SAME'):
        in_features = int(_input.get_shape()[-1])
        filter_ref = self.weight_variable_msra(
            [kernel_size, kernel_size, in_features, out_features],
            name='filter')
        output = tf.nn.conv2d(_input, filter_ref, strides, padding)
        return output, filter_ref
def conv2d ( self , _input , out_features , kernel_size , strides = [ 1 , 1 , 1 , 1 ] , padding = 'SAME' ) : in_features = int ( _input . get_shape ( ) [ - 1 ] ) filter_ref = self . weight_variable_msra ( [ kernel_size , kernel_size , in_features , out_features ] , name = 'filter' ) output = tf . nn . conv2d ( _input , filter_ref , strides , padding ) return output , filter_ref
Creates a 2d convolutional filter layer (applies a certain number of
kernels on some input features to obtain output features).
Creates a 2d convolutional filter layer ( applies a certain number of kernels on some input features to obtain output features ) .
"""
        Creates a 2d convolutional filter layer (applies a certain number of
        kernels on some input features to obtain output features).
        Returns the output of the layer and a reference to its filter.

        Args:
            _input: tensor, the operation's input;
            out_features: `int`, number of feature maps at the output;
            kernel_size: `int`, size of the square kernels (their side);
            strides: `list` of `int`, strides in each direction for kernels;
            padding: `str`, should we use padding ('SAME') or not ('VALID').
        """
self
_input
tensor, the operation's input.
tensor the operation ' s input .
out_features
`int`, number of feature maps at the output.
` int ` number of feature maps at the output .
kernel_size
`int`, size of the square kernels (their side).
` int ` size of the square kernels ( their side ) .
strides
`list` of `int`, strides in each direction for kernels.
` list ` of ` int ` strides in each direction for kernels .
padding
`str`, should we use padding ('SAME') or not ('VALID').
` str ` should we use padding ( ' SAME ' ) or not ( ' VALID ' ) .