text: import tensorflow as tf
  
  def architecture(x_in,drop_out):
  
  	xp = tf.layers.conv2d(x_in,filters=12,kernel_size=5,strides=(1, 1),padding='same',
  			activation=tf.nn.relu)
  	x1 = tf.layers.conv2d(xp,filters=12,kernel_size=5,strides=(1, 1),padding='same',
  			activation=tf.nn.relu)
  	x2 = tf.layers.conv2d(x1,filters=12,kernel_size=5,strides=(1, 1),padding='same',
  			activation=tf.nn.relu)
  	x3 = x2+xp
  	x3 = tf.layers.batch_normalization(x3)
  	x4 = tf.layers.conv2d(x3,filters=12,kernel_size=5,strides=(1, 1),padding='same',
  			activation=tf.nn.relu)
  
  	x5 = tf.layers.dropout(x4, drop_out)
  	x_out = tf.layers.conv2d(x5,filters=3,kernel_size=5,strides=(1, 1),padding='same',
  			activation=tf.nn.relu)
  
  	return [x_out,x5,x4,x3,x2,x1,xp]
  