old_contents: import tensorflow as tf
  import tensorlayer as tl
  
  sess = tf.InteractiveSession()
  
  ## vector
  x = tf.placeholder(tf.float32, shape=[None, 784])
  inputs = tl.layers.InputLayer(x, name='input_layer')
  net1 = tl.layers.DenseLayer(inputs, 100, act=tf.nn.relu, name='relu1_1')
  net2 = tl.layers.DenseLayer(inputs, 100, act=tf.nn.relu, name='relu2_1')
  net = tl.layers.ConcatLayer([net1, net2], 1, name='concat_layer')
  
  net.print_params(False)
  net.print_layers()
  
  if len(net.all_layers) != 3:
      raise Exception("layers dont match")
  
  if len(net.all_params) != 4:
      raise Exception("params dont match")
  
  if net.count_params() != 157000:
      raise Exception("params dont match")
  
  net_0 = tl.layers.DenseLayer(inputs, n_units=100, act=tf.nn.relu, name='net_0')
  net_1 = tl.layers.DenseLayer(inputs, n_units=100, act=tf.nn.relu, name='net_1')
  net = tl.layers.ElementwiseLayer([net_0, net_1], combine_fn=tf.minimum, name='minimum')
  
  net.print_params(False)
  net.print_layers()
  
  if len(net.all_layers) != 3:
      raise Exception("layers dont match")
  
  if len(net.all_params) != 4:
      raise Exception("params dont match")
  
  if net.count_params() != 157000:
      raise Exception("params dont match")
  
  ### image
  x = tf.placeholder(tf.float32, shape=[None, 100, 100, 3])
  inputs = tl.layers.InputLayer(x, name='input')
  net1 = tl.layers.Conv2d(inputs, 32, (3, 3), (2, 2), act=tf.nn.relu, name='c1')
  net2 = tl.layers.Conv2d(inputs, 32, (3, 3), (2, 2), act=tf.nn.relu, name='c2')
  net = tl.layers.ConcatLayer([net1, net2], -1, name='concat')
  
  net.print_params(False)
  net.print_layers()
  
  shape = net.outputs.get_shape().as_list()
  if shape[1:] != [50, 50, 64]:
      raise Exception("shape dont match")
  
  if len(net.all_layers) != 3:
      raise Exception("layers dont match")
  
  if len(net.all_params) != 4:
      raise Exception("params dont match")
  
  if net.count_params() != 1792:
      raise Exception("params dont match")
  
  net = tl.layers.ElementwiseLayer([net1, net2], combine_fn=tf.minimum, name='minimum2')
  net.print_params(False)
  net.print_layers()
  
  shape = net.outputs.get_shape().as_list()
  if shape[1:] != [50, 50, 32]:
      raise Exception("shape dont match")
  
  if len(net.all_layers) != 3:
      raise Exception("layers dont match")
  
  if len(net.all_params) != 4:
      raise Exception("params dont match")
  
  if net.count_params() != 1792:
      raise Exception("params dont match")
  
new_contents: import tensorflow as tf
  import tensorlayer as tl
  
  sess = tf.InteractiveSession()
  
  ## vector
  x = tf.placeholder(tf.float32, shape=[None, 784])
  inputs = tl.layers.InputLayer(x, name='input_layer')
  net1 = tl.layers.DenseLayer(inputs, 100, act=tf.nn.relu, name='relu1_1')
  net2 = tl.layers.DenseLayer(inputs, 100, act=tf.nn.relu, name='relu2_1')
  net = tl.layers.ConcatLayer([net1, net2], 1, name='concat_layer')
  
  net.print_params(False)
  net.print_layers()
  
  if len(net.all_layers) != 3:
      raise Exception("layers dont match")
  
  if len(net.all_params) != 4:
      raise Exception("params dont match")
  
  if net.count_params() != 157000:
      raise Exception("params dont match")
  
  net_0 = tl.layers.DenseLayer(inputs, n_units=100, act=tf.nn.relu, name='net_0')
  net_1 = tl.layers.DenseLayer(inputs, n_units=100, act=tf.nn.relu, name='net_1')
  net = tl.layers.ElementwiseLayer([net_0, net_1], combine_fn=tf.minimum, name='minimum')
  
  net.print_params(False)
  net.print_layers()
  
  if len(net.all_layers) != 3:
      raise Exception("layers dont match")
  
  if len(net.all_params) != 4:
      raise Exception("params dont match")
  
  if net.count_params() != 157000:
      raise Exception("params dont match")
  
  ## image
  x = tf.placeholder(tf.float32, shape=[None, 100, 100, 3])
  inputs = tl.layers.InputLayer(x, name='input')
  net1 = tl.layers.Conv2d(inputs, 32, (3, 3), (2, 2), act=tf.nn.relu, name='c1')
  net2 = tl.layers.Conv2d(inputs, 32, (3, 3), (2, 2), act=tf.nn.relu, name='c2')
  net = tl.layers.ConcatLayer([net1, net2], -1, name='concat')
  
  net.print_params(False)
  net.print_layers()
  
  shape = net.outputs.get_shape().as_list()
  if shape[1:] != [50, 50, 64]:
      raise Exception("shape dont match")
  
  if len(net.all_layers) != 3:
      raise Exception("layers dont match")
  
  if len(net.all_params) != 4:
      raise Exception("params dont match")
  
  if net.count_params() != 1792:
      raise Exception("params dont match")
  
  net = tl.layers.ElementwiseLayer([net1, net2], combine_fn=tf.minimum, name='minimum2')
  net.print_params(False)
  net.print_layers()
  
  shape = net.outputs.get_shape().as_list()
  if shape[1:] != [50, 50, 32]:
      raise Exception("shape dont match")
  
  if len(net.all_layers) != 3:
      raise Exception("layers dont match")
  
  if len(net.all_params) != 4:
      raise Exception("params dont match")
  
  if net.count_params() != 1792:
      raise Exception("params dont match")
  
subject: Update test_layers_merge.py