import tensorflow as tf

    def conv_layer(self, bottom, kernal_size, in_channels, out_channels, stride, name):
    	with tf.variable_scope(name):
    		filt, conv_biases = self.get_conv_var(kernal_size, in_channels, out_channels, name)
    		conv = tf.nn.conv2d(bottom, filt, [1,stride,stride,1], padding='SAME')
    		bias = tf.nn.bias_add(conv, conv_biases)

    		tf.summary.histogram('weight', filt)
    		tf.summary.histogram('bias', conv_biases)

    		return bias

    def conv_bn_relu(self, bottom,name, kernel_size, output_channels, initializer,stride=1, bn=False,training=False,relu=True):
