text: #! /usr/bin/python
  # -*- coding: utf8 -*-
  
  
  
  import tensorflow.compat.v1 as tf
  import time
  from . import visualize
  from . import utils
  from . import files
  from . import cost
  from . import iterate
  from . import ops
  import numpy as np
  from six.moves import xrange
  import random, warnings
  import copy
  
  # __all__ = [
  #     "Layer",
  #     "DenseLayer",
  # ]
  
  
  # set_keep = locals()
  set_keep = globals()
  set_keep['_layers_name_list'] =[]
  set_keep['name_reuse'] = False
  
  try:  # For TF12 and later
      TF_GRAPHKEYS_VARIABLES = tf.GraphKeys.GLOBAL_VARIABLES
  except:  # For TF11 and before
      TF_GRAPHKEYS_VARIABLES = tf.GraphKeys.VARIABLES
  
  ## Variable Operation
  def flatten_reshape(variable, name=''):
      """Reshapes high-dimension input to a vector.
      [batch_size, mask_row, mask_col, n_mask] ---> [batch_size, mask_row * mask_col * n_mask]
  
      Parameters
      ----------
      variable : a tensorflow variable
      name : a string or None
          An optional name to attach to this layer.
  
      Examples
      --------
      >>> W_conv2 = weight_variable([5, 5, 100, 32])   # 64 features for each 5x5 patch
      >>> b_conv2 = bias_variable([32])
      >>> W_fc1 = weight_variable([7 * 7 * 32, 256])
  
      >>> h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
      >>> h_pool2 = max_pool_2x2(h_conv2)
      >>> h_pool2.get_shape()[:].as_list() = [batch_size, 7, 7, 32]
      ...         [batch_size, mask_row, mask_col, n_mask]
      >>> h_pool2_flat = tl.layers.flatten_reshape(h_pool2)
      ...         [batch_size, mask_row * mask_col * n_mask]
      >>> h_pool2_flat_drop = tf.nn.dropout(h_pool2_flat, keep_prob)
      ...
      """
      dim = 1
      for d in variable.get_shape()[1:].as_list():
          dim *= d
      return tf.reshape(variable, shape=[-1, dim], name=name)
  
  def clear_layers_name():
      """Clear all layer names in set_keep['_layers_name_list'],
      enable layer name reuse.
  
      Examples
      ---------
      >>> network = tl.layers.InputLayer(x, name='input_layer')
      >>> network = tl.layers.DenseLayer(network, n_units=800, name='relu1')
      ...
      >>> tl.layers.clear_layers_name()
      >>> network2 = tl.layers.InputLayer(x, name='input_layer')
      >>> network2 = tl.layers.DenseLayer(network2, n_units=800, name='relu1')
      ...
      """
      set_keep['_layers_name_list'] =[]
  
  def set_name_reuse(enable=True):
      """Enable or disable reuse layer name. By default, each layer must has unique
      name. When you want two or more input placeholder (inference) share the same
      model parameters, you need to enable layer name reuse, then allow the
      parameters have same name scope.
  
      Parameters
      ------------
      enable : boolean, enable name reuse.
  
      Examples
      ------------
      >>> def embed_seq(input_seqs, is_train, reuse):
      >>>    with tf.variable_scope("model", reuse=reuse):
      >>>         tl.layers.set_name_reuse(reuse)
      >>>         network = tl.layers.EmbeddingInputlayer(
      ...                     inputs = input_seqs,
      ...                     vocabulary_size = vocab_size,
      ...                     embedding_size = embedding_size,
      ...                     name = 'e_embedding')
      >>>        network = tl.layers.DynamicRNNLayer(network,
      ...                     cell_fn = tf.nn.rnn_cell.BasicLSTMCell,
      ...                     n_hidden = embedding_size,
      ...                     dropout = (0.7 if is_train else None),
      ...                     initializer = w_init,
      ...                     sequence_length = tl.layers.retrieve_seq_length_op2(input_seqs),
      ...                     return_last = True,
      ...                     name = 'e_dynamicrnn',)
      >>>    return network
      >>>
      >>> net_train = embed_seq(t_caption, is_train=True, reuse=False)
      >>> net_test = embed_seq(t_caption, is_train=False, reuse=True)
  
      - see ``tutorial_ptb_lstm.py`` for example.
      """
      set_keep['name_reuse'] = enable
  
  def initialize_rnn_state(state):
      """Return the initialized RNN state.
      The input is LSTMStateTuple or State of RNNCells.
  
      Parameters
      -----------
      state : a RNN state.
      """
      try: # TF1.0
          LSTMStateTuple = tf.contrib.rnn.LSTMStateTuple
      except:
          LSTMStateTuple = tf.nn.rnn_cell.LSTMStateTuple
  
      if isinstance(state, LSTMStateTuple):
          c = state.c.eval()
          h = state.h.eval()
          return (c, h)
      else:
          new_state = state.eval()
          return new_state
  
  def print_all_variables(train_only=False):
      """Print all trainable and non-trainable variables
      without tl.layers.initialize_global_variables(sess)
  
      Parameters
      ----------
      train_only : boolean
          If True, only print the trainable variables, otherwise, print all variables.
      """
      # tvar = tf.trainable_variables() if train_only else tf.all_variables()
      if train_only:
          t_vars = tf.trainable_variables()
          print("  [*] printing trainable variables")
      else:
          try: # TF1.0
              t_vars = tf.global_variables()
          except: # TF0.12
              t_vars = tf.all_variables()
          print("  [*] printing global variables")
      for idx, v in enumerate(t_vars):
          print("  var {:3}: {:15}   {}".format(idx, str(v.get_shape()), v.name))
  
  def get_variables_with_name(name, train_only=True, printable=False):
      """Get variable list by a given name scope.
  
      Examples
      ---------
      >>> dense_vars = tl.layers.get_variable_with_name('dense', True, True)
      """
      print("  [*] geting variables with %s" % name)
      # tvar = tf.trainable_variables() if train_only else tf.all_variables()
      if train_only:
          t_vars = tf.trainable_variables()
      else:
          try: # TF1.0
              t_vars = tf.global_variables()
          except: # TF0.12
              t_vars = tf.all_variables()
  
      d_vars = [var for var in t_vars if name in var.name]
      if printable:
          for idx, v in enumerate(d_vars):
              print("  got {:3}: {:15}   {}".format(idx, v.name, str(v.get_shape())))
      return d_vars
  
  def get_layers_with_name(network=None, name="", printable=False):
      """Get layer list in a network by a given name scope.
  
      Examples
      ---------
      >>> layers = tl.layers.get_layers_with_name(network, "CNN", True)
      """
      assert network is not None
      print("  [*] geting layers with %s" % name)
  
      layers = []
      i = 0
      for layer in network.all_layers:
          # print(type(layer.name))
          if name in layer.name:
              layers.append(layer)
              if printable:
                  # print(layer.name)
                  print("  got {:3}: {:15}   {}".format(i, layer.name, str(layer.get_shape())))
                  i = i + 1
      return layers
  
  def list_remove_repeat(l=None):
      """Remove the repeated items in a list, and return the processed list.
      You may need it to create merged layer like Concat, Elementwise and etc.
  
      Parameters
      ----------
      l : a list
  
      Examples
      ---------
      >>> l = [2, 3, 4, 2, 3]
      >>> l = list_remove_repeat(l)
      ... [2, 3, 4]
      """
      l2 = []
      [l2.append(i) for i in l if not i in l2]
      return l2
  
  def initialize_global_variables(sess=None):
      """Excute ``sess.run(tf.global_variables_initializer())`` for TF12+ or
      sess.run(tf.initialize_all_variables()) for TF11.
  
      Parameters
      ----------
      sess : a Session
      """
      assert sess is not None
      try:    # TF12
          sess.run(tf.global_variables_initializer())
      except: # TF11
          sess.run(tf.initialize_all_variables())
  
  
  ## Basic layer
  class Layer(object):
      """
      The :class:`Layer` class represents a single layer of a neural network. It
      should be subclassed when implementing new types of layers.
      Because each layer can keep track of the layer(s) feeding into it, a
      network's output :class:`Layer` instance can double as a handle to the full
      network.
  
      Parameters
      ----------
      inputs : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      name : a string or None
          An optional name to attach to this layer.
      """
      def __init__(
          self,
          inputs = None,
          name ='layer'
      ):
          self.inputs = inputs
          scope_name=tf.get_variable_scope().name
          if scope_name:
              name = scope_name + '/' + name
          if (name in set_keep['_layers_name_list']) and name_reuse == False:
              raise Exception("Layer '%s' already exists, please choice other 'name' or reuse this layer\
              \nHint : Use different name for different 'Layer' (The name is used to control parameter sharing)" % name)
          else:
              self.name = name
              if name not in ['', None, False]:
                  set_keep['_layers_name_list'].append(name)
  
  
      def print_params(self, details=True):
          ''' Print all info of parameters in the network'''
          for i, p in enumerate(self.all_params):
              if details:
                  try:
                      print("  param {:3}: {:15} (mean: {:<18}, median: {:<18}, std: {:<18})   {}".format(i, str(p.eval().shape), p.eval().mean(), np.median(p.eval()), p.eval().std(), p.name))
                  except Exception as e:
                      print(str(e))
                      raise Exception("Hint: print params details after tl.layers.initialize_global_variables(sess) or use network.print_params(False).")
              else:
                  print("  param {:3}: {:15}    {}".format(i, str(p.get_shape()), p.name))
          print("  num of params: %d" % self.count_params())
  
      def print_layers(self):
          ''' Print all info of layers in the network '''
          for i, p in enumerate(self.all_layers):
              print("  layer %d: %s" % (i, str(p)))
  
      def count_params(self):
          ''' Return the number of parameters in the network '''
          n_params = 0
          for i, p in enumerate(self.all_params):
              n = 1
              # for s in p.eval().shape:
              for s in p.get_shape():
                  try:
                      s = int(s)
                  except:
                      s = 1
                  if s:
                      n = n * s
              n_params = n_params + n
          return n_params
  
      def __str__(self):
          # print("\nIt is a Layer class")
          # self.print_params(False)
          # self.print_layers()
          return "  Last layer is: %s" % self.__class__.__name__
  
  ## Input layer
  class InputLayer(Layer):
      """
      The :class:`InputLayer` class is the starting layer of a neural network.
  
      Parameters
      ----------
      inputs : a placeholder or tensor
          The input tensor data.
      name : a string or None
          An optional name to attach to this layer.
      """
      def __init__(
          self,
          inputs = None,
          name ='input_layer'
      ):
          Layer.__init__(self, inputs=inputs, name=name)
          print("  [TL] InputLayer  %s: %s" % (self.name, inputs.get_shape()))
          self.outputs = inputs
          self.all_layers = []
          self.all_params = []
          self.all_drop = {}
  
  ## OneHot layer
  class OneHotInputLayer(Layer):
      """
      The :class:`OneHotInputLayer` class is the starting layer of a neural network, see ``tf.one_hot``.
  
      Parameters
      ----------
      inputs : a placeholder or tensor
          The input tensor data.
      name : a string or None
          An optional name to attach to this layer.
      depth : If the input indices is rank N, the output will have rank N+1. The new axis is created at dimension axis (default: the new axis is appended at the end).
      on_value : If on_value is not provided, it will default to the value 1 with type dtype.
          default, None
      off_value : If off_value is not provided, it will default to the value 0 with type dtype.
          default, None
      axis : default, None
      dtype : default, None
      """
      def __init__(
          self,
          inputs = None,
          depth = None,
          on_value = None,
          off_value = None,
          axis = None,
          dtype=None,
          name ='input_layer'
      ):
          Layer.__init__(self, inputs=inputs, name=name)
          assert depth != None, "depth is not given"
          print("  [TL]:Instantiate OneHotInputLayer  %s: %s" % (self.name, inputs.get_shape()))
          self.outputs = tf.one_hot(inputs, depth, on_value=on_value, off_value=off_value, axis=axis, dtype=dtype)
          self.all_layers = []
          self.all_params = []
          self.all_drop = {}
  
  ## Word Embedding Input layer
  class Word2vecEmbeddingInputlayer(Layer):
      """
      The :class:`Word2vecEmbeddingInputlayer` class is a fully connected layer,
      for Word Embedding. Words are input as integer index.
      The output is the embedded word vector.
  
      Parameters
      ----------
      inputs : placeholder
          For word inputs. integer index format.
      train_labels : placeholder
          For word labels. integer index format.
      vocabulary_size : int
          The size of vocabulary, number of words.
      embedding_size : int
          The number of embedding dimensions.
      num_sampled : int
          The Number of negative examples for NCE loss.
      nce_loss_args : a dictionary
          The arguments for tf.nn.nce_loss()
      E_init : embedding initializer
          The initializer for initializing the embedding matrix.
      E_init_args : a dictionary
          The arguments for embedding initializer
      nce_W_init : NCE decoder biases initializer
          The initializer for initializing the nce decoder weight matrix.
      nce_W_init_args : a dictionary
          The arguments for initializing the nce decoder weight matrix.
      nce_b_init : NCE decoder biases initializer
          The initializer for tf.get_variable() of the nce decoder bias vector.
      nce_b_init_args : a dictionary
          The arguments for tf.get_variable() of the nce decoder bias vector.
      name : a string or None
          An optional name to attach to this layer.
  
      Variables
      --------------
      nce_cost : a tensor
          The NCE loss.
      outputs : a tensor
          The outputs of embedding layer.
      normalized_embeddings : tensor
          Normalized embedding matrix
  
      Examples
      --------
      - Without TensorLayer : see tensorflow/examples/tutorials/word2vec/word2vec_basic.py
      >>> train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
      >>> train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
      >>> embeddings = tf.Variable(
      ...     tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
      >>> embed = tf.nn.embedding_lookup(embeddings, train_inputs)
      >>> nce_weights = tf.Variable(
      ...     tf.truncated_normal([vocabulary_size, embedding_size],
      ...                    stddev=1.0 / math.sqrt(embedding_size)))
      >>> nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
      >>> cost = tf.reduce_mean(
      ...    tf.nn.nce_loss(weights=nce_weights, biases=nce_biases,
      ...               inputs=embed, labels=train_labels,
      ...               num_sampled=num_sampled, num_classes=vocabulary_size,
      ...               num_true=1))
  
      - With TensorLayer : see tutorial_word2vec_basic.py
      >>> train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
      >>> train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
      >>> emb_net = tl.layers.Word2vecEmbeddingInputlayer(
      ...         inputs = train_inputs,
      ...         train_labels = train_labels,
      ...         vocabulary_size = vocabulary_size,
      ...         embedding_size = embedding_size,
      ...         num_sampled = num_sampled,
      ...        name ='word2vec_layer',
      ...    )
      >>> cost = emb_net.nce_cost
      >>> train_params = emb_net.all_params
      >>> train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(
      ...                                             cost, var_list=train_params)
      >>> normalized_embeddings = emb_net.normalized_embeddings
  
      References
      ----------
      - `tensorflow/examples/tutorials/word2vec/word2vec_basic.py <https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/examples/tutorials/word2vec/word2vec_basic.py>`_
      """
      def __init__(
          self,
          inputs = None,
          train_labels = None,
          vocabulary_size = 80000,
          embedding_size = 200,
          num_sampled = 64,
          nce_loss_args = {},
          E_init = tf.random_uniform_initializer(minval=-1.0, maxval=1.0),
          E_init_args = {},
          nce_W_init = tf.truncated_normal_initializer(stddev=0.03),
          nce_W_init_args = {},
          nce_b_init = tf.constant_initializer(value=0.0),
          nce_b_init_args = {},
          name ='word2vec_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = inputs
          print("  [TL] Word2vecEmbeddingInputlayer %s: (%d, %d)" % (self.name, vocabulary_size, embedding_size))
          # Look up embeddings for inputs.
          # Note: a row of 'embeddings' is the vector representation of a word.
          # for the sake of speed, it is better to slice the embedding matrix
          # instead of transfering a word id to one-hot-format vector and then
          # multiply by the embedding matrix.
          # embed is the outputs of the hidden layer (embedding layer), it is a
          # row vector with 'embedding_size' values.
          with tf.variable_scope(name) as vs:
              embeddings = tf.get_variable(name='embeddings',
                                      shape=(vocabulary_size, embedding_size),
                                      initializer=E_init,
                                      **E_init_args)
              embed = tf.nn.embedding_lookup(embeddings, self.inputs)
              # Construct the variables for the NCE loss (i.e. negative sampling)
              nce_weights = tf.get_variable(name='nce_weights',
                                      shape=(vocabulary_size, embedding_size),
                                      initializer=nce_W_init,
                                      **nce_W_init_args)
              nce_biases = tf.get_variable(name='nce_biases',
                                      shape=(vocabulary_size),
                                      initializer=nce_b_init,
                                      **nce_b_init_args)
  
          # Compute the average NCE loss for the batch.
          # tf.nce_loss automatically draws a new sample of the negative labels
          # each time we evaluate the loss.
          self.nce_cost = tf.reduce_mean(
              tf.nn.nce_loss(weights=nce_weights, biases=nce_biases,
                             inputs=embed, labels=train_labels,
                             num_sampled=num_sampled, num_classes=vocabulary_size,
                             **nce_loss_args))
  
          self.outputs = embed
          self.normalized_embeddings = tf.nn.l2_normalize(embeddings, 1)
  
          self.all_layers = [self.outputs]
          self.all_params = [embeddings, nce_weights, nce_biases]
          self.all_drop = {}
  
  class EmbeddingInputlayer(Layer):
      """
      The :class:`EmbeddingInputlayer` class is a fully connected layer,
      for Word Embedding. Words are input as integer index.
      The output is the embedded word vector.
  
      If you have a pre-train matrix, you can assign the matrix into it.
      To train a word embedding matrix, you can used class:`Word2vecEmbeddingInputlayer`.
  
      Note that, do not update this embedding matrix.
  
      Parameters
      ----------
      inputs : placeholder
          For word inputs. integer index format.
          a 2D tensor : [batch_size, num_steps(num_words)]
      vocabulary_size : int
          The size of vocabulary, number of words.
      embedding_size : int
          The number of embedding dimensions.
      E_init : embedding initializer
          The initializer for initializing the embedding matrix.
      E_init_args : a dictionary
          The arguments for embedding initializer
      name : a string or None
          An optional name to attach to this layer.
  
      Variables
      ------------
      outputs : a tensor
          The outputs of embedding layer.
          the outputs 3D tensor : [batch_size, num_steps(num_words), embedding_size]
  
      Examples
      --------
      >>> vocabulary_size = 50000
      >>> embedding_size = 200
      >>> model_file_name = "model_word2vec_50k_200"
      >>> batch_size = None
      ...
      >>> all_var = tl.files.load_npy_to_any(name=model_file_name+'.npy')
      >>> data = all_var['data']; count = all_var['count']
      >>> dictionary = all_var['dictionary']
      >>> reverse_dictionary = all_var['reverse_dictionary']
      >>> tl.files.save_vocab(count, name='vocab_'+model_file_name+'.txt')
      >>> del all_var, data, count
      ...
      >>> load_params = tl.files.load_npz(name=model_file_name+'.npz')
      >>> x = tf.placeholder(tf.int32, shape=[batch_size])
      >>> y_ = tf.placeholder(tf.int32, shape=[batch_size, 1])
      >>> emb_net = tl.layers.EmbeddingInputlayer(
      ...                inputs = x,
      ...                vocabulary_size = vocabulary_size,
      ...                embedding_size = embedding_size,
      ...                name ='embedding_layer')
      >>> tl.layers.initialize_global_variables(sess)
      >>> tl.files.assign_params(sess, [load_params[0]], emb_net)
      >>> word = b'hello'
      >>> word_id = dictionary[word]
      >>> print('word_id:', word_id)
      ... 6428
      ...
      >>> words = [b'i', b'am', b'hao', b'dong']
      >>> word_ids = tl.files.words_to_word_ids(words, dictionary)
      >>> context = tl.files.word_ids_to_words(word_ids, reverse_dictionary)
      >>> print('word_ids:', word_ids)
      ... [72, 1226, 46744, 20048]
      >>> print('context:', context)
      ... [b'i', b'am', b'hao', b'dong']
      ...
      >>> vector = sess.run(emb_net.outputs, feed_dict={x : [word_id]})
      >>> print('vector:', vector.shape)
      ... (1, 200)
      >>> vectors = sess.run(emb_net.outputs, feed_dict={x : word_ids})
      >>> print('vectors:', vectors.shape)
      ... (4, 200)
  
      """
      def __init__(
          self,
          inputs = None,
          vocabulary_size = 80000,
          embedding_size = 200,
          E_init = tf.random_uniform_initializer(-0.1, 0.1),
          E_init_args = {},
          name ='embedding_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = inputs
          print("  [TL] EmbeddingInputlayer %s: (%d, %d)" % (self.name, vocabulary_size, embedding_size))
  
          with tf.variable_scope(name) as vs:
              embeddings = tf.get_variable(name='embeddings',
                                      shape=(vocabulary_size, embedding_size),
                                      initializer=E_init,
                                      **E_init_args)
              embed = tf.nn.embedding_lookup(embeddings, self.inputs)
  
          self.outputs = embed
  
          self.all_layers = [self.outputs]
          self.all_params = [embeddings]
          self.all_drop = {}
  
  ## Dense layer
  class DenseLayer(Layer):
      """
      The :class:`DenseLayer` class is a fully connected layer.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      n_units : int
          The number of units of the layer.
      act : activation function
          The function that is applied to the layer activations.
      W_init : weights initializer
          The initializer for initializing the weight matrix.
      b_init : biases initializer or None
          The initializer for initializing the bias vector. If None, skip biases.
      W_init_args : dictionary
          The arguments for the weights tf.get_variable.
      b_init_args : dictionary
          The arguments for the biases tf.get_variable.
      name : a string or None
          An optional name to attach to this layer.
  
      Examples
      --------
      >>> network = tl.layers.InputLayer(x, name='input_layer')
      >>> network = tl.layers.DenseLayer(
      ...                 network,
      ...                 n_units=800,
      ...                 act = tf.nn.relu,
      ...                 W_init=tf.truncated_normal_initializer(stddev=0.1),
      ...                 name ='relu_layer'
      ...                 )
  
      >>> Without TensorLayer, you can do as follow.
      >>> W = tf.Variable(
      ...     tf.random_uniform([n_in, n_units], -1.0, 1.0), name='W')
      >>> b = tf.Variable(tf.zeros(shape=[n_units]), name='b')
      >>> y = tf.nn.relu(tf.matmul(inputs, W) + b)
  
      Notes
      -----
      If the input to this layer has more than two axes, it need to flatten the
      input by using :class:`FlattenLayer` in this case.
      """
      def __init__(
          self,
          layer = None,
          n_units = 100,
          act = tf.identity,
          W_init = tf.truncated_normal_initializer(stddev=0.1),
          b_init = tf.constant_initializer(value=0.0),
          W_init_args = {},
          b_init_args = {},
          name ='dense_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          if self.inputs.get_shape().ndims != 2:
              raise Exception("The input dimension must be rank 2, please reshape or flatten it")
  
          n_in = int(self.inputs.get_shape()[-1])
          self.n_units = n_units
          print("  [TL] DenseLayer  %s: %d %s" % (self.name, self.n_units, act.__name__))
          with tf.variable_scope(name) as vs:
              W = tf.get_variable(name='W', shape=(n_in, n_units), initializer=W_init, **W_init_args )
              if b_init:
                  b = tf.get_variable(name='b', shape=(n_units), initializer=b_init, **b_init_args )
                  self.outputs = act(tf.matmul(self.inputs, W) + b)
              else:
                  self.outputs = act(tf.matmul(self.inputs, W))
  
          # Hint : list(), dict() is pass by value (shallow), without them, it is
          # pass by reference.
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          if b_init:
              self.all_params.extend( [W, b] )
          else:
              self.all_params.extend( [W] )
  
  class ReconLayer(DenseLayer):
      """
      The :class:`ReconLayer` class is a reconstruction layer `DenseLayer` which
      use to pre-train a `DenseLayer`.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      x_recon : tensorflow variable
          The variables used for reconstruction.
      name : a string or None
          An optional name to attach to this layer.
      n_units : int
          The number of units of the layer, should be equal to x_recon
      act : activation function
          The activation function that is applied to the reconstruction layer.
          Normally, for sigmoid layer, the reconstruction activation is sigmoid;
          for rectifying layer, the reconstruction activation is softplus.
  
      Examples
      --------
      >>> network = tl.layers.InputLayer(x, name='input_layer')
      >>> network = tl.layers.DenseLayer(network, n_units=196,
      ...                                 act=tf.nn.sigmoid, name='sigmoid1')
      >>> recon_layer1 = tl.layers.ReconLayer(network, x_recon=x, n_units=784,
      ...                                 act=tf.nn.sigmoid, name='recon_layer1')
      >>> recon_layer1.pretrain(sess, x=x, X_train=X_train, X_val=X_val,
      ...                         denoise_name=None, n_epoch=1200, batch_size=128,
      ...                         print_freq=10, save=True, save_name='w1pre_')
  
      Methods
      -------
      pretrain(self, sess, x, X_train, X_val, denoise_name=None, n_epoch=100, batch_size=128, print_freq=10, save=True, save_name='w1pre_')
          Start to pre-train the parameters of previous DenseLayer.
  
      Notes
      -----
      The input layer should be `DenseLayer` or a layer has only one axes.
      You may need to modify this part to define your own cost function.
      By default, the cost is implemented as follow:
      - For sigmoid layer, the implementation can be `UFLDL <http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial>`_
      - For rectifying layer, the implementation can be `Glorot (2011). Deep Sparse Rectifier Neural Networks <http://doi.org/10.1.1.208.6449>`_
      """
      def __init__(
          self,
          layer = None,
          x_recon = None,
          name = 'recon_layer',
          n_units = 784,
          act = tf.nn.softplus,
      ):
          DenseLayer.__init__(self, layer=layer, n_units=n_units, act=act, name=name)
          print("     [TL] %s is a ReconLayer" % self.name)
  
          # y : reconstruction outputs; train_params : parameters to train
          # Note that: train_params = [W_encoder, b_encoder, W_decoder, b_encoder]
          y = self.outputs
          self.train_params = self.all_params[-4:]
  
          # =====================================================================
          #
          # You need to modify the below cost function and optimizer so as to
          # implement your own pre-train method.
          #
          # =====================================================================
          lambda_l2_w = 0.004
          learning_rate = 0.0001
          print("     lambda_l2_w: %f" % lambda_l2_w)
          print("     learning_rate: %f" % learning_rate)
  
          # Mean-squre-error i.e. quadratic-cost
          mse = tf.reduce_sum(tf.squared_difference(y, x_recon),  1)
          mse = tf.reduce_mean(mse)            # in theano: mse = ((y - x) ** 2 ).sum(axis=1).mean()
              # mse = tf.reduce_mean(tf.reduce_sum(tf.square(tf.sub(y, x_recon)),  1))
              # mse = tf.reduce_mean(tf.squared_difference(y, x_recon)) # <haodong>: Error
              # mse = tf.sqrt(tf.reduce_mean(tf.square(y - x_recon)))   # <haodong>: Error
          # Cross-entropy
              # ce = cost.cross_entropy(y, x_recon)                                               # <haodong>: list , list , Error (only be used for softmax output)
              # ce = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, x_recon))          # <haodong>: list , list , Error (only be used for softmax output)
              # ce = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(y, x_recon))   # <haodong>: list , index , Error (only be used for softmax output)
          L2_w = tf.contrib.layers.l2_regularizer(lambda_l2_w)(self.train_params[0]) \
                  + tf.contrib.layers.l2_regularizer(lambda_l2_w)(self.train_params[2])           # faster than the code below
              # L2_w = lambda_l2_w * tf.reduce_mean(tf.square(self.train_params[0])) + lambda_l2_w * tf.reduce_mean( tf.square(self.train_params[2]))
          # DropNeuro
          P_o = cost.lo_regularizer(0.03)(self.train_params[0])   # + cost.lo_regularizer(0.5)(self.train_params[2])    # <haodong>: if add lo on decoder, no neuron will be broken
          P_i = cost.li_regularizer(0.03)(self.train_params[0])  # + cost.li_regularizer(0.001)(self.train_params[2])
  
          # L1 of activation outputs
          activation_out = self.all_layers[-2]
          L1_a = 0.001 * tf.reduce_mean(activation_out)   # <haodong>:  theano: T.mean( self.a[i] )         # some neuron are broken, white and black
              # L1_a = 0.001 * tf.reduce_mean( tf.reduce_sum(activation_out, 0) )         # <haodong>: some neuron are broken, white and black
              # L1_a = 0.001 * 100 * tf.reduce_mean( tf.reduce_sum(activation_out, 1) )   # <haodong>: some neuron are broken, white and black
          # KL Divergence
          beta = 4
          rho = 0.15
          p_hat = tf.reduce_mean(activation_out, 0)   # theano: p_hat = T.mean( self.a[i], axis=0 )
          try: ## TF1.0
              KLD = beta * tf.reduce_sum( rho * tf.log(tf.divide(rho, p_hat)) + (1- rho) * tf.log((1- rho)/ (tf.subtract(float(1), p_hat))) )
          except: ## TF0.12
              KLD = beta * tf.reduce_sum( rho * tf.log(tf.div(rho, p_hat)) + (1- rho) * tf.log((1- rho)/ (tf.sub(float(1), p_hat))) )
              # KLD = beta * tf.reduce_sum( rho * tf.log(rho/ p_hat) + (1- rho) * tf.log((1- rho)/(1- p_hat)) )
              # theano: L1_a = l1_a[i] * T.sum( rho[i] * T.log(rho[i]/ p_hat) + (1- rho[i]) * T.log((1- rho[i])/(1- p_hat)) )
          # Total cost
          if act == tf.nn.softplus:
              print('     use: mse, L2_w, L1_a')
              self.cost = mse + L1_a + L2_w
          elif act == tf.nn.sigmoid:
              # ----------------------------------------------------
              # Cross-entropy was used in Denoising AE
              # print('     use: ce, L2_w, KLD')
              # self.cost = ce + L2_w + KLD
              # ----------------------------------------------------
              # Mean-squared-error was used in Vanilla AE
              print('     use: mse, L2_w, KLD')
              self.cost = mse + L2_w + KLD
              # ----------------------------------------------------
              # Add DropNeuro penalty (P_o) can remove neurons of AE
              # print('     use: mse, L2_w, KLD, P_o')
              # self.cost = mse + L2_w + KLD + P_o
              # ----------------------------------------------------
              # Add DropNeuro penalty (P_i) can remove neurons of previous layer
              #   If previous layer is InputLayer, it means remove useless features
              # print('     use: mse, L2_w, KLD, P_i')
              # self.cost = mse + L2_w + KLD + P_i
          else:
              raise Exception("Don't support the given reconstruct activation function")
  
          self.train_op = tf.train.AdamOptimizer(learning_rate, beta1=0.5, beta2=0.999,
                                          epsilon=1e-08, use_locking=False).minimize(self.cost, var_list=self.train_params)
                  # self.train_op = tf.train.GradientDescentOptimizer(1.0).minimize(self.cost, var_list=self.train_params)
  
      def pretrain(self, sess, x, X_train, X_val, denoise_name=None, n_epoch=100, batch_size=128, print_freq=10,
                    save=True, save_name='w1pre_'):
          # ====================================================
          #
          # You need to modify the cost function in __init__() so as to
          # get your own pre-train method.
          #
          # ====================================================
          print("     [*] %s start pretrain" % self.name)
          print("     batch_size: %d" % batch_size)
          if denoise_name:
              print("     denoising layer keep: %f" % self.all_drop[set_keep[denoise_name]])
              dp_denoise = self.all_drop[set_keep[denoise_name]]
          else:
              print("     no denoising layer")
  
          for epoch in range(n_epoch):
              start_time = time.time()
              for X_train_a, _ in iterate.minibatches(X_train, X_train, batch_size, shuffle=True):
                  dp_dict = utils.dict_to_one( self.all_drop )
                  if denoise_name:
                      dp_dict[set_keep[denoise_name]] = dp_denoise
                  feed_dict = {x: X_train_a}
                  feed_dict.update(dp_dict)
                  sess.run(self.train_op, feed_dict=feed_dict)
  
              if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
                  print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))
                  train_loss, n_batch = 0, 0
                  for X_train_a, _ in iterate.minibatches(X_train, X_train, batch_size, shuffle=True):
                      dp_dict = utils.dict_to_one( self.all_drop )
                      feed_dict = {x: X_train_a}
                      feed_dict.update(dp_dict)
                      err = sess.run(self.cost, feed_dict=feed_dict)
                      train_loss += err
                      n_batch += 1
                  print("   train loss: %f" % (train_loss/ n_batch))
                  val_loss, n_batch = 0, 0
                  for X_val_a, _ in iterate.minibatches(X_val, X_val, batch_size, shuffle=True):
                      dp_dict = utils.dict_to_one( self.all_drop )
                      feed_dict = {x: X_val_a}
                      feed_dict.update(dp_dict)
                      err = sess.run(self.cost, feed_dict=feed_dict)
                      val_loss += err
                      n_batch += 1
                  print("   val loss: %f" % (val_loss/ n_batch))
                  if save:
                      try:
                          visualize.W(self.train_params[0].eval(), second=10, saveable=True, shape=[28,28], name=save_name+str(epoch+1), fig_idx=2012)
                          files.save_npz([self.all_params[0]] , name=save_name+str(epoch+1)+'.npz')
                      except:
                          raise Exception("You should change the visualize.W() in ReconLayer.pretrain(), if you want to save the feature images for different dataset")
  
  ## Noise layer
  class DropoutLayer(Layer):
      """
      The :class:`DropoutLayer` class is a noise layer which randomly set some
      values to zero by a given keeping probability.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      keep : float
          The keeping probability, the lower more values will be set to zero.
      is_fix : boolean
          Default False, if True, the keeping probability is fixed and cannot be changed via feed_dict.
      is_train : boolean
          If False, skip this layer, default is True.
      name : a string or None
          An optional name to attach to this layer.
  
      Examples
      --------
      - Define network
      >>> network = tl.layers.InputLayer(x, name='input_layer')
      >>> network = tl.layers.DropoutLayer(network, keep=0.8, name='drop1')
      >>> network = tl.layers.DenseLayer(network, n_units=800, act = tf.nn.relu, name='relu1')
      >>> ...
  
      - For training, enable dropout as follow.
      >>> feed_dict = {x: X_train_a, y_: y_train_a}
      >>> feed_dict.update( network.all_drop )     # enable noise layers
      >>> sess.run(train_op, feed_dict=feed_dict)
      >>> ...
  
      - For testing, disable dropout as follow.
      >>> dp_dict = tl.utils.dict_to_one( network.all_drop ) # disable noise layers
      >>> feed_dict = {x: X_val_a, y_: y_val_a}
      >>> feed_dict.update(dp_dict)
      >>> err, ac = sess.run([cost, acc], feed_dict=feed_dict)
      >>> ...
  
      Notes
      -------
      - A frequent question regarding :class:`DropoutLayer` is that why it donot have `is_train` like :class:`BatchNormLayer`.
      In many simple cases, user may find it is better to use one inference instead of two inferences for training and testing seperately, :class:`DropoutLayer`
      allows you to control the dropout rate via `feed_dict`. However, you can fix the keeping probability by setting `is_fix` to True.
      """
      def __init__(
          self,
          layer = None,
          keep = 0.5,
          is_fix = False,
          is_train = True,
          name = 'dropout_layer',
      ):
          Layer.__init__(self, name=name)
          if is_train is False:
              print("  [TL] skip DropoutLayer")
              self.outputs = layer.outputs
              self.all_layers = list(layer.all_layers)
              self.all_params = list(layer.all_params)
              self.all_drop = dict(layer.all_drop)
          else:
              self.inputs = layer.outputs
              print("  [TL] DropoutLayer %s: keep:%f is_fix:%s" % (self.name, keep, is_fix))
  
              # The name of placeholder for keep_prob is the same with the name
              # of the Layer.
              if is_fix:
                  self.outputs = tf.nn.dropout(self.inputs, keep, name=name)
              else:
                  set_keep[name] = tf.placeholder(tf.float32)
                  self.outputs = tf.nn.dropout(self.inputs, set_keep[name], name=name) # 1.2
  
              self.all_layers = list(layer.all_layers)
              self.all_params = list(layer.all_params)
              self.all_drop = dict(layer.all_drop)
              if is_fix is False:
                  self.all_drop.update( {set_keep[name]: keep} )
              self.all_layers.extend( [self.outputs] )
  
          # print(set_keep[name])
          #   Tensor("Placeholder_2:0", dtype=float32)
          # print(denoising1)
          #   Tensor("Placeholder_2:0", dtype=float32)
          # print(self.all_drop[denoising1])
          #   0.8
          #
          # https://www.tensorflow.org/versions/r0.8/tutorials/mnist/tf/index.html
          # The optional feed_dict argument allows the caller to override the
          # value of tensors in the graph. Each key in feed_dict can be one of
          # the following types:
          # If the key is a Tensor, the value may be a Python scalar, string,
          # list, or numpy ndarray that can be converted to the same dtype as that
          # tensor. Additionally, if the key is a placeholder, the shape of the
          # value will be checked for compatibility with the placeholder.
          # If the key is a SparseTensor, the value should be a SparseTensorValue.
  
  class GaussianNoiseLayer(Layer):
      """
      The :class:`GaussianNoiseLayer` class is noise layer that adding noise with
      normal distribution to the activation.
  
      Parameters
      ------------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      mean : float
      stddev : float
      is_train : boolean
          If False, skip this layer, default is True.
      name : a string or None
          An optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          mean = 0.0,
          stddev = 1.0,
          is_train = True,
          name = 'gaussian_noise_layer',
      ):
          Layer.__init__(self, name=name)
          if is_train is False:
              print("  [TL] skip GaussianNoiseLayer")
              self.outputs = layer.outputs
              self.all_layers = list(layer.all_layers)
              self.all_params = list(layer.all_params)
              self.all_drop = dict(layer.all_drop)
          else:
              self.inputs = layer.outputs
              print("  [TL] GaussianNoiseLayer %s: mean:%f stddev:%f" % (self.name, mean, stddev))
              with tf.variable_scope(name) as vs:
                  # noise = np.random.normal(0.0 , sigma , tf.to_int64(self.inputs).get_shape())
                  noise = tf.random_normal(shape = self.inputs.get_shape(), mean=mean, stddev=stddev)
                  self.outputs = self.inputs + noise
              self.all_layers = list(layer.all_layers)
              self.all_params = list(layer.all_params)
              self.all_drop = dict(layer.all_drop)
  
  class DropconnectDenseLayer(Layer):
      """
      The :class:`DropconnectDenseLayer` class is ``DenseLayer`` with DropConnect
      behaviour which randomly remove connection between this layer to previous
      layer by a given keeping probability.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      keep : float
          The keeping probability, the lower more values will be set to zero.
      n_units : int
          The number of units of the layer.
      act : activation function
          The function that is applied to the layer activations.
      W_init : weights initializer
          The initializer for initializing the weight matrix.
      b_init : biases initializer
          The initializer for initializing the bias vector.
      W_init_args : dictionary
          The arguments for the weights tf.get_variable().
      b_init_args : dictionary
          The arguments for the biases tf.get_variable().
      name : a string or None
          An optional name to attach to this layer.
  
      Examples
      --------
      >>> network = tl.layers.InputLayer(x, name='input_layer')
      >>> network = tl.layers.DropconnectDenseLayer(network, keep = 0.8,
      ...         n_units=800, act = tf.nn.relu, name='dropconnect_relu1')
      >>> network = tl.layers.DropconnectDenseLayer(network, keep = 0.5,
      ...         n_units=800, act = tf.nn.relu, name='dropconnect_relu2')
      >>> network = tl.layers.DropconnectDenseLayer(network, keep = 0.5,
      ...         n_units=10, act = tl.activation.identity, name='output_layer')
  
      References
      ----------
      - `Wan, L. (2013). Regularization of neural networks using dropconnect <http://machinelearning.wustl.edu/mlpapers/papers/icml2013_wan13>`_
      """
      def __init__(
          self,
          layer = None,
          keep = 0.5,
          n_units = 100,
          act = tf.identity,
          W_init = tf.truncated_normal_initializer(stddev=0.1),
          b_init = tf.constant_initializer(value=0.0),
          W_init_args = {},
          b_init_args = {},
          name ='dropconnect_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          if self.inputs.get_shape().ndims != 2:
              raise Exception("The input dimension must be rank 2")
          n_in = int(self.inputs.get_shape()[-1])
          self.n_units = n_units
          print("  [TL] DropconnectDenseLayer %s: %d %s" % (self.name, self.n_units, act.__name__))
  
          with tf.variable_scope(name) as vs:
              W = tf.get_variable(name='W', shape=(n_in, n_units), initializer=W_init, **W_init_args )
              b = tf.get_variable(name='b', shape=(n_units), initializer=b_init, **b_init_args )
              self.outputs = act(tf.matmul(self.inputs, W) + b)#, name=name)    # 1.2
  
          set_keep[name] = tf.placeholder(tf.float32)
          W_dropcon = tf.nn.dropout(W,  set_keep[name])
          self.outputs = act(tf.matmul(self.inputs, W_dropcon) + b)
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_drop.update( {set_keep[name]: keep} )
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( [W, b] )
  
  ## Convolutional layer (Pro)
  
  class Conv1dLayer(Layer):
      """
      The :class:`Conv1dLayer` class is a 1D CNN layer, see `tf.nn.conv1d <https://www.tensorflow.org/versions/master/api_docs/python/nn.html#conv1d>`_.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer, [batch, in_width, in_channels].
      act : activation function, None for identity.
      shape : list of shape
          shape of the filters, [filter_length, in_channels, out_channels].
      stride : an int.
          The number of entries by which the filter is moved right at each step.
      padding : a string from: "SAME", "VALID".
          The type of padding algorithm to use.
      use_cudnn_on_gpu : An optional bool. Defaults to True.
      data_format : An optional string from "NHWC", "NCHW". Defaults to "NHWC", the data is stored in the order of [batch, in_width, in_channels]. The "NCHW" format stores data as [batch, in_channels, in_width].
      W_init : weights initializer
          The initializer for initializing the weight matrix.
      b_init : biases initializer or None
          The initializer for initializing the bias vector. If None, skip biases.
      W_init_args : dictionary
          The arguments for the weights tf.get_variable().
      b_init_args : dictionary
          The arguments for the biases tf.get_variable().
      name : a string or None
          An optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          act = tf.identity,
          shape = [5, 1, 5],
          stride = 1,
          padding='SAME',
          use_cudnn_on_gpu=None,
          data_format=None,
          W_init = tf.truncated_normal_initializer(stddev=0.02),
          b_init = tf.constant_initializer(value=0.0),
          W_init_args = {},
          b_init_args = {},
          name ='cnn_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          print("  [TL] Conv1dLayer %s: shape:%s stride:%s pad:%s act:%s" %
                              (self.name, str(shape), str(stride), padding, act.__name__))
          if act is None:
              act = tf.identity
          with tf.variable_scope(name) as vs:
              W = tf.get_variable(name='W_conv1d', shape=shape, initializer=W_init, **W_init_args )
              if b_init:
                  b = tf.get_variable(name='b_conv1d', shape=(shape[-1]), initializer=b_init, **b_init_args )
                  self.outputs = act( tf.nn.conv1d(self.inputs, W, stride=stride, padding=padding,
                              use_cudnn_on_gpu=use_cudnn_on_gpu, data_format=data_format) + b ) #1.2
              else:
                  self.outputs = act( tf.nn.conv1d(self.inputs, W, stride=stride, padding=padding,
                              use_cudnn_on_gpu=use_cudnn_on_gpu, data_format=data_format))
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          if b_init:
              self.all_params.extend( [W, b] )
          else:
              self.all_params.extend( [W] )
  
  class Conv2dLayer(Layer):
      """
      The :class:`Conv2dLayer` class is a 2D CNN layer, see `tf.nn.conv2d <https://www.tensorflow.org/versions/master/api_docs/python/nn.html#conv2d>`_.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      act : activation function
          The function that is applied to the layer activations.
      shape : list of shape
          shape of the filters, [filter_height, filter_width, in_channels, out_channels].
      strides : a list of ints.
          The stride of the sliding window for each dimension of input.\n
          It Must be in the same order as the dimension specified with format.
      padding : a string from: "SAME", "VALID".
          The type of padding algorithm to use.
      W_init : weights initializer
          The initializer for initializing the weight matrix.
      b_init : biases initializer or None
          The initializer for initializing the bias vector. If None, skip biases.
      W_init_args : dictionary
          The arguments for the weights tf.get_variable().
      b_init_args : dictionary
          The arguments for the biases tf.get_variable().
      name : a string or None
          An optional name to attach to this layer.
  
      Notes
      ------
      - shape = [h, w, the number of output channel of previous layer, the number of output channels]
      - the number of output channel of a layer is its last dimension.
  
      Examples
      --------
      >>> x = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
      >>> network = tl.layers.InputLayer(x, name='input_layer')
      >>> network = tl.layers.Conv2dLayer(network,
      ...                   act = tf.nn.relu,
      ...                   shape = [5, 5, 1, 32],  # 32 features for each 5x5 patch
      ...                   strides=[1, 1, 1, 1],
      ...                   padding='SAME',
      ...                   W_init=tf.truncated_normal_initializer(stddev=5e-2),
      ...                   W_init_args={},
      ...                   b_init = tf.constant_initializer(value=0.0),
      ...                   b_init_args = {},
      ...                   name ='cnn_layer1')     # output: (?, 28, 28, 32)
      >>> network = tl.layers.PoolLayer(network,
      ...                   ksize=[1, 2, 2, 1],
      ...                   strides=[1, 2, 2, 1],
      ...                   padding='SAME',
      ...                   pool = tf.nn.max_pool,
      ...                   name ='pool_layer1',)   # output: (?, 14, 14, 32)
  
      >>> Without TensorLayer, you can implement 2d convolution as follow.
      >>> W = tf.Variable(W_init(shape=[5, 5, 1, 32], ), name='W_conv')
      >>> b = tf.Variable(b_init(shape=[32], ), name='b_conv')
      >>> outputs = tf.nn.relu( tf.nn.conv2d(inputs, W,
      ...                       strides=[1, 1, 1, 1],
      ...                       padding='SAME') + b )
      """
      def __init__(
          self,
          layer = None,
          act = tf.identity,
          shape = [5, 5, 1, 100],
          strides=[1, 1, 1, 1],
          padding='SAME',
          W_init = tf.truncated_normal_initializer(stddev=0.02),
          b_init = tf.constant_initializer(value=0.0),
          W_init_args = {},
          b_init_args = {},
          name ='cnn_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          print("  [TL] Conv2dLayer %s: shape:%s strides:%s pad:%s act:%s" %
                              (self.name, str(shape), str(strides), padding, act.__name__))
  
          with tf.variable_scope(name) as vs:
              W = tf.get_variable(name='W_conv2d', shape=shape, initializer=W_init, **W_init_args )
              if b_init:
                  b = tf.get_variable(name='b_conv2d', shape=(shape[-1]), initializer=b_init, **b_init_args )
                  self.outputs = act( tf.nn.conv2d(self.inputs, W, strides=strides, padding=padding) + b ) #1.2
              else:
                  self.outputs = act( tf.nn.conv2d(self.inputs, W, strides=strides, padding=padding))
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          if b_init:
              self.all_params.extend( [W, b] )
          else:
              self.all_params.extend( [W] )
  
  class DeConv2dLayer(Layer):
      """
      The :class:`DeConv2dLayer` class is deconvolutional 2D layer, see `tf.nn.conv2d_transpose <https://www.tensorflow.org/versions/master/api_docs/python/nn.html#conv2d_transpose>`_.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      act : activation function
          The function that is applied to the layer activations.
      shape : list of shape
          shape of the filters, [height, width, output_channels, in_channels], filter's in_channels dimension must match that of value.
      output_shape : list of output shape
          representing the output shape of the deconvolution op.
      strides : a list of ints.
          The stride of the sliding window for each dimension of the input tensor.
      padding : a string from: "SAME", "VALID".
          The type of padding algorithm to use.
      W_init : weights initializer
          The initializer for initializing the weight matrix.
      b_init : biases initializer
          The initializer for initializing the bias vector. If None, skip biases.
      W_init_args : dictionary
          The arguments for the weights initializer.
      b_init_args : dictionary
          The arguments for the biases initializer.
      name : a string or None
          An optional name to attach to this layer.
  
      Notes
      -----
      - shape = [h, w, the number of output channels of this layer, the number of output channel of previous layer]
      - output_shape = [batch_size, any, any, the number of output channels of this layer]
      - the number of output channel of a layer is its last dimension.
  
      Examples
      ---------
      - A part of the generator in DCGAN example
      >>> batch_size = 64
      >>> inputs = tf.placeholder(tf.float32, [batch_size, 100], name='z_noise')
      >>> net_in = tl.layers.InputLayer(inputs, name='g/in')
      >>> net_h0 = tl.layers.DenseLayer(net_in, n_units = 8192,
      ...                            W_init = tf.random_normal_initializer(stddev=0.02),
      ...                            act = tf.identity, name='g/h0/lin')
      >>> print(net_h0.outputs._shape)
      ... (64, 8192)
      >>> net_h0 = tl.layers.ReshapeLayer(net_h0, shape = [-1, 4, 4, 512], name='g/h0/reshape')
      >>> net_h0 = tl.layers.BatchNormLayer(net_h0, act=tf.nn.relu, is_train=is_train, name='g/h0/batch_norm')
      >>> print(net_h0.outputs._shape)
      ... (64, 4, 4, 512)
      >>> net_h1 = tl.layers.DeConv2dLayer(net_h0,
      ...                            shape = [5, 5, 256, 512],
      ...                            output_shape = [batch_size, 8, 8, 256],
      ...                            strides=[1, 2, 2, 1],
      ...                            act=tf.identity, name='g/h1/decon2d')
      >>> net_h1 = tl.layers.BatchNormLayer(net_h1, act=tf.nn.relu, is_train=is_train, name='g/h1/batch_norm')
      >>> print(net_h1.outputs._shape)
      ... (64, 8, 8, 256)
  
      - U-Net
      >>> ....
      >>> conv10 = tl.layers.Conv2dLayer(conv9, act=tf.nn.relu,
      ...        shape=[3,3,1024,1024], strides=[1,1,1,1], padding='SAME',
      ...        W_init=w_init, b_init=b_init, name='conv10')
      >>> print(conv10.outputs)
      ... (batch_size, 32, 32, 1024)
      >>> deconv1 = tl.layers.DeConv2dLayer(conv10, act=tf.nn.relu,
      ...         shape=[3,3,512,1024], strides=[1,2,2,1], output_shape=[batch_size,64,64,512],
      ...         padding='SAME', W_init=w_init, b_init=b_init, name='devcon1_1')
      """
      def __init__(
          self,
          layer = None,
          act = tf.identity,
          shape = [3, 3, 128, 256],
          output_shape = [1, 256, 256, 128],
          strides = [1, 2, 2, 1],
          padding = 'SAME',
          W_init = tf.truncated_normal_initializer(stddev=0.02),
          b_init = tf.constant_initializer(value=0.0),
          W_init_args = {},
          b_init_args = {},
          name ='decnn2d_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          print("  [TL] DeConv2dLayer %s: shape:%s out_shape:%s strides:%s pad:%s act:%s" %
                              (self.name, str(shape), str(output_shape), str(strides), padding, act.__name__))
          # print("  DeConv2dLayer: Untested")
          with tf.variable_scope(name) as vs:
              W = tf.get_variable(name='W_deconv2d', shape=shape, initializer=W_init, **W_init_args )
              if b_init:
                  b = tf.get_variable(name='b_deconv2d', shape=(shape[-2]), initializer=b_init, **b_init_args )
                  self.outputs = act( tf.nn.conv2d_transpose(self.inputs, W, output_shape=output_shape, strides=strides, padding=padding) + b )
              else:
                  self.outputs = act( tf.nn.conv2d_transpose(self.inputs, W, output_shape=output_shape, strides=strides, padding=padding))
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          if b_init:
              self.all_params.extend( [W, b] )
          else:
              self.all_params.extend( [W] )
  
  class Conv3dLayer(Layer):
      """
      The :class:`Conv3dLayer` class is a 3D CNN layer, see `tf.nn.conv3d <https://www.tensorflow.org/versions/master/api_docs/python/nn.html#conv3d>`_.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      act : activation function
          The function that is applied to the layer activations.
      shape : list of shape
          shape of the filters, [filter_depth, filter_height, filter_width, in_channels, out_channels].
      strides : a list of ints. 1-D of length 4.
          The stride of the sliding window for each dimension of input. Must be in the same order as the dimension specified with format.
      padding : a string from: "SAME", "VALID".
          The type of padding algorithm to use.
      W_init : weights initializer
          The initializer for initializing the weight matrix.
      b_init : biases initializer
          The initializer for initializing the bias vector.
      W_init_args : dictionary
          The arguments for the weights initializer.
      b_init_args : dictionary
          The arguments for the biases initializer.
      name : a string or None
          An optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          act = tf.identity,
          shape = [2, 2, 2, 64, 128],
          strides=[1, 2, 2, 2, 1],
          padding='SAME',
          W_init = tf.truncated_normal_initializer(stddev=0.02),
          b_init = tf.constant_initializer(value=0.0),
          W_init_args = {},
          b_init_args = {},
          name ='cnn3d_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          print("  [TL] Conv3dLayer %s: shape:%s strides:%s pad:%s act:%s" % (self.name, str(shape), str(strides), padding, act.__name__))
  
          with tf.variable_scope(name) as vs:
              # W = tf.Variable(W_init(shape=shape, **W_init_args), name='W_conv')
              # b = tf.Variable(b_init(shape=[shape[-1]], **b_init_args), name='b_conv')
              W = tf.get_variable(name='W_conv3d', shape=shape, initializer=W_init, **W_init_args )
              b = tf.get_variable(name='b_conv3d', shape=(shape[-1]), initializer=b_init, **b_init_args )
              self.outputs = act( tf.nn.conv3d(self.inputs, W, strides=strides, padding=padding, name=None) + b )
  
          # self.outputs = act( tf.nn.conv3d(self.inputs, W, strides=strides, padding=padding, name=None) + b )
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( [W, b] )
  
  class DeConv3dLayer(Layer):
      """The :class:`DeConv3dLayer` class is deconvolutional 3D layer, see `tf.nn.conv3d_transpose <https://www.tensorflow.org/versions/master/api_docs/python/nn.html#conv3d_transpose>`_.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      act : activation function
          The function that is applied to the layer activations.
      shape : list of shape
          shape of the filters, [depth, height, width, output_channels, in_channels], filter's in_channels dimension must match that of value.
      output_shape : list of output shape
          representing the output shape of the deconvolution op.
      strides : a list of ints.
          The stride of the sliding window for each dimension of the input tensor.
      padding : a string from: "SAME", "VALID".
          The type of padding algorithm to use.
      W_init : weights initializer
          The initializer for initializing the weight matrix.
      b_init : biases initializer
          The initializer for initializing the bias vector.
      W_init_args : dictionary
          The arguments for the weights initializer.
      b_init_args : dictionary
          The arguments for the biases initializer.
      name : a string or None
          An optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          act = tf.identity,
          shape = [2, 2, 2, 128, 256],
          output_shape = [1, 12, 32, 32, 128],
          strides = [1, 2, 2, 2, 1],
          padding = 'SAME',
          W_init = tf.truncated_normal_initializer(stddev=0.02),
          b_init = tf.constant_initializer(value=0.0),
          W_init_args = {},
          b_init_args = {},
          name ='decnn3d_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          print("  [TL] DeConv3dLayer %s: shape:%s out_shape:%s strides:%s pad:%s act:%s" %
                              (self.name, str(shape), str(output_shape), str(strides), padding, act.__name__))
  
          with tf.variable_scope(name) as vs:
              W = tf.get_variable(name='W_deconv3d', shape=shape, initializer=W_init, **W_init_args )
              b = tf.get_variable(name='b_deconv3d', shape=(shape[-2]), initializer=b_init, **b_init_args )
  
              self.outputs = act( tf.nn.conv3d_transpose(self.inputs, W, output_shape=output_shape, strides=strides, padding=padding) + b )
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( [W, b] )
  
  class UpSampling2dLayer(Layer):
      """The :class:`UpSampling2dLayer` class is upSampling 2d layer, see `tf.image.resize_images <https://www.tensorflow.org/versions/master/api_docs/python/image/resizing#resize_images>`_.
  
      Parameters
      -----------
      layer : a layer class with 4-D Tensor of shape [batch, height, width, channels] or 3-D Tensor of shape [height, width, channels].
      size : a tupe of int or float.
          (height, width) scale factor or new size of height and width.
      is_scale : boolean, if True (default), size is scale factor, otherwise, size is number of pixels of height and width.
      method : 0, 1, 2, 3. ResizeMethod. Defaults to ResizeMethod.BILINEAR.
          - ResizeMethod.BILINEAR, Bilinear interpolation.
          - ResizeMethod.NEAREST_NEIGHBOR, Nearest neighbor interpolation.
          - ResizeMethod.BICUBIC, Bicubic interpolation.
          - ResizeMethod.AREA, Area interpolation.
      align_corners : bool. If true, exactly align all 4 corners of the input and output. Defaults to false.
      name : a string or None
          An optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          size = [],
          is_scale = True,
          method = 0,
          align_corners = False,
          name ='upsample2d_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          if len(self.inputs.get_shape()) == 3:
              if is_scale:
                  size_h = size[0] * int(self.inputs.get_shape()[0])
                  size_w = size[1] * int(self.inputs.get_shape()[1])
                  size = [size_h, size_w]
          elif len(self.inputs.get_shape()) == 4:
              if is_scale:
                  size_h = size[0] * int(self.inputs.get_shape()[1])
                  size_w = size[1] * int(self.inputs.get_shape()[2])
                  size = [size_h, size_w]
          else:
              raise Exception("Donot support shape %s" % self.inputs.get_shape())
          print("  [TL] UpSampling2dLayer %s: is_scale:%s size:%s method:%d align_corners:%s" %
                                  (name, is_scale, size, method, align_corners))
          with tf.variable_scope(name) as vs:
              try:
                  self.outputs = tf.image.resize_images(self.inputs, size=size, method=method, align_corners=align_corners)
              except: # for TF 0.10
                  self.outputs = tf.image.resize_images(self.inputs, new_height=size[0], new_width=size[1], method=method, align_corners=align_corners)
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
  
  class DownSampling2dLayer(Layer):
      """The :class:`DownSampling2dLayer` class is downSampling 2d layer, see `tf.image.resize_images <https://www.tensorflow.org/versions/master/api_docs/python/image/resizing#resize_images>`_.
  
      Parameters
      -----------
      layer : a layer class with 4-D Tensor of shape [batch, height, width, channels] or 3-D Tensor of shape [height, width, channels].
      size : a tupe of int or float.
          (height, width) scale factor or new size of height and width.
      is_scale : boolean, if True (default), size is scale factor, otherwise, size is number of pixels of height and width.
      method : 0, 1, 2, 3. ResizeMethod. Defaults to ResizeMethod.BILINEAR.
          - ResizeMethod.BILINEAR, Bilinear interpolation.
          - ResizeMethod.NEAREST_NEIGHBOR, Nearest neighbor interpolation.
          - ResizeMethod.BICUBIC, Bicubic interpolation.
          - ResizeMethod.AREA, Area interpolation.
      align_corners : bool. If true, exactly align all 4 corners of the input and output. Defaults to false.
      name : a string or None
          An optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          size = [],
          is_scale = True,
          method = 0,
          align_corners = False,
          name ='downsample2d_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          if len(self.inputs.get_shape()) == 3:
              if is_scale:
                  size_h = size[0] * int(self.inputs.get_shape()[0])
                  size_w = size[1] * int(self.inputs.get_shape()[1])
                  size = [size_h, size_w]
          elif len(self.inputs.get_shape()) == 4:
              if is_scale:
                  size_h = size[0] * int(self.inputs.get_shape()[1])
                  size_w = size[1] * int(self.inputs.get_shape()[2])
                  size = [size_h, size_w]
          else:
              raise Exception("Donot support shape %s" % self.inputs.get_shape())
          print("  [TL] DownSampling2dLayer %s: is_scale:%s size:%s method:%d, align_corners:%s" %
                                  (name, is_scale, size, method, align_corners))
          with tf.variable_scope(name) as vs:
              try:
                  self.outputs = tf.image.resize_images(self.inputs, size=size, method=method, align_corners=align_corners)
              except: # for TF 0.10
                  self.outputs = tf.image.resize_images(self.inputs, new_height=size[0], new_width=size[1], method=method, align_corners=align_corners)
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
  
  class AtrousConv2dLayer(Layer):
      """The :class:`AtrousConv2dLayer` class is Atrous convolution (a.k.a. convolution with holes or dilated convolution) 2D layer, see `tf.nn.atrous_conv2d <https://www.tensorflow.org/versions/master/api_docs/python/nn.html#atrous_conv2d>`_.
  
      Parameters
      -----------
      layer : a layer class with 4-D Tensor of shape [batch, height, width, channels].
      filters : A 4-D Tensor with the same type as value and shape [filter_height, filter_width, in_channels, out_channels]. filters' in_channels dimension must match that of value. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height filter_height + (filter_height - 1) * (rate - 1) and effective width filter_width + (filter_width - 1) * (rate - 1), produced by inserting rate - 1 zeros along consecutive elements across the filters' spatial dimensions.
      n_filter : number of filter.
      filter_size : tuple (height, width) for filter size.
      rate : A positive int32. The stride with which we sample input values across the height and width dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the height and width dimensions. In the literature, the same parameter is sometimes called input stride or dilation.
      act : activation function, None for linear.
      padding : A string, either 'VALID' or 'SAME'. The padding algorithm.
      W_init : weights initializer. The initializer for initializing the weight matrix.
      b_init : biases initializer or None. The initializer for initializing the bias vector. If None, skip biases.
      W_init_args : dictionary. The arguments for the weights tf.get_variable().
      b_init_args : dictionary. The arguments for the biases tf.get_variable().
      name : a string or None, an optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          n_filter = 32,
          filter_size = (3,3),
          rate = 2,
          act = None,
          padding = 'SAME',
          W_init = tf.truncated_normal_initializer(stddev=0.02),
          b_init = tf.constant_initializer(value=0.0),
          W_init_args = {},
          b_init_args = {},
          name = 'atrou2d'
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          print("  [TL] AtrousConv2dLayer %s: n_filter:%d filter_size:%s rate:%d pad:%s act:%s" %
                              (self.name, n_filter, filter_size, rate, padding, act.__name__))
          if act is None:
              act = tf.identity
          with tf.variable_scope(name) as vs:
              shape = [filter_size[0], filter_size[1], int(self.inputs.get_shape()[-1]), n_filter]
              filters = tf.get_variable(name='filter', shape=shape, initializer=W_init, **W_init_args )
              if b_init:
                  b = tf.get_variable(name='b', shape=(n_filter), initializer=b_init, **b_init_args )
                  self.outputs = act(tf.nn.atrous_conv2d(self.inputs, filters, rate, padding) + b)
              else:
                  self.outputs = act(tf.nn.atrous_conv2d(self.inputs, filters, rate, padding))
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          if b_init:
              self.all_params.extend( [filters, b] )
          else:
              self.all_params.extend( [filters] )
  
  class SeparableConv2dLayer(Layer):# Untested
      """The :class:`SeparableConv2dLayer` class is 2-D convolution with separable filters, see `tf.layers.separable_conv2d <https://www.tensorflow.org/api_docs/python/tf/layers/separable_conv2d>`_.
  
      Parameters
      -----------
      layer : a layer class
      filters : integer, the dimensionality of the output space (i.e. the number output of filters in the convolution).
      kernel_size : a tuple or list of N positive integers specifying the spatial dimensions of of the filters. Can be a single integer to specify the same value for all spatial dimensions.
      strides : a tuple or list of N positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
      padding : one of "valid" or "same" (case-insensitive).
      data_format : A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shapedata_format = 'NWHC' (batch, width, height, channels) while channels_first corresponds to inputs with shape (batch, channels, width, height).
      dilation_rate : an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any stride value != 1.
      depth_multiplier : The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to num_filters_in * depth_multiplier.
      act (activation) : Activation function. Set it to None to maintain a linear activation.
      use_bias : Boolean, whether the layer uses a bias.
      depthwise_initializer : An initializer for the depthwise convolution kernel.
      pointwise_initializer : An initializer for the pointwise convolution kernel.
      bias_initializer : An initializer for the bias vector. If None, no bias will be applied.
      depthwise_regularizer : Optional regularizer for the depthwise convolution kernel.
      pointwise_regularizer : Optional regularizer for the pointwise convolution kernel.
      bias_regularizer : Optional regularizer for the bias vector.
      activity_regularizer : Regularizer function for the output.
      name : a string or None, an optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          filters = None,
          kernel_size=5,
          strides=(1, 1),
          padding='valid',
          data_format='channels_last',
          dilation_rate=(1, 1),
          depth_multiplier=1,
          act=None,
          use_bias=True,
          depthwise_initializer=None,
          pointwise_initializer=None,
          bias_initializer=tf.zeros_initializer,
          depthwise_regularizer=None,
          pointwise_regularizer=None,
          bias_regularizer=None,
          activity_regularizer=None,
          name = 'atrou2d'
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          assert filters is not None
          assert tf.__version__ > "0.12.1", "This layer only supports for TF 1.0+"
          if act is None:
              act = tf.identity
  
          bias_initializer = bias_initializer()
  
          print("  [TL] SeparableConv2dLayer %s: filters:%s kernel_size:%s strides:%s padding:%s dilation_rate:%s depth_multiplier:%s act:%s" %
                              (self.name, str(filters), str(kernel_size), str(strides), padding, str(dilation_rate), str(depth_multiplier), act.__name__))
  
          with tf.variable_scope(name) as vs:
              self.outputs = tf.layers.separable_conv2d(self.inputs, filters, kernel_size,
                   strides=strides, padding=padding, data_format=data_format,
                   dilation_rate=dilation_rate, depth_multiplier=depth_multiplier, activation=act,
                   use_bias=use_bias, depthwise_initializer=depthwise_initializer, pointwise_initializer=pointwise_initializer,
                   bias_initializer=bias_initializer, depthwise_regularizer=depthwise_regularizer,
                   pointwise_regularizer=pointwise_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer,)
                   #trainable=True, name=None, reuse=None)
  
              variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( variables )
  
  ## Initializers for Convuolutional Layers
  def deconv2d_bilinear_upsampling_initializer(shape):
      """Returns initializer that can be passed to DeConv2dLayer to initalize the
      weights to correspond to channel wise bilinear upsampling.
      Used in some segmantic segmentation approches such as [FCN](https://arxiv.org/abs/1605.06211)
  
      Parameters
      ----------
          shape : list of shape
              shape of the filters, [height, width, output_channels, in_channels], must match that passed to DeConv2dLayer
  
      Returns
      ----------
          tf.constant_initializer
              with weights set to correspond to per channel bilinear upsampling when passed as W_int in DeConv2dLayer
  
      Examples
      --------
      >>> rescale_factor = 2 #upsampling by a factor of 2, ie e.g 100->200
      >>> filter_size = (2 * rescale_factor - rescale_factor % 2) #Corresponding bilinear filter size
      >>> num_in_channels = 3
      >>> num_out_channels = 3
      >>> deconv_filter_shape = [filter_size, filter_size, num_out_channels, num_in_channels]
      >>> x = tf.placeholder(tf.float32, [1, imsize, imsize, num_channels])
      >>> network = tl.layers.InputLayer(x, name='input_layer')
      >>> bilinear_init = deconv2d_bilinear_upsampling_initializer(shape=filter_shape)
      >>> network = tl.layers.DeConv2dLayer(network,
                              shape = filter_shape,
                              output_shape = [1, imsize*rescale_factor, imsize*rescale_factor, num_out_channels],
                              strides=[1, rescale_factor, rescale_factor, 1],
                              W_init=bilinear_init,
                              padding='SAME',
                              act=tf.identity, name='g/h1/decon2d')
      """
      if shape[0] != shape[1]:
          raise Exception('deconv2d_bilinear_upsampling_initializer only supports symmetrical filter sizes')
      if shape[3] < shape [2]:
          raise Exception('deconv2d_bilinear_upsampling_initializer behaviour is not defined for num_in_channels < num_out_channels ')
  
      filter_size = shape[0]
      num_out_channels = shape[2]
      num_in_channels = shape[3]
  
      #Create bilinear filter kernel as numpy array
      bilinear_kernel = np.zeros([filter_size, filter_size], dtype=np.float32)
      scale_factor = (filter_size + 1) // 2
      if filter_size % 2 == 1:
          center = scale_factor - 1
      else:
          center = scale_factor - 0.5
      for x in range(filter_size):
          for y in range(filter_size):
              bilinear_kernel[x,y] = (1 - abs(x - center) / scale_factor) * \
                                     (1 - abs(y - center) / scale_factor)
      weights = np.zeros((filter_size, filter_size, num_out_channels, num_in_channels))
      for i in range(num_out_channels):
          weights[:, :, i, i] = bilinear_kernel
  
      #assign numpy array to constant_initalizer and pass to get_variable
      bilinear_weights_init = tf.constant_initializer(value=weights, dtype=tf.float32)
      return bilinear_weights_init
  
  ## Convolutional layer (Simplified)
  def Conv1d(net, n_filter=32, filter_size=5, stride=1, act=None,
          padding='SAME', use_cudnn_on_gpu=None,data_format=None,
          W_init = tf.truncated_normal_initializer(stddev=0.02),
          b_init = tf.constant_initializer(value=0.0),
          W_init_args = {}, b_init_args = {}, name ='conv1d',):
      """Wrapper for :class:`Conv1dLayer`, if you don't understand how to use :class:`Conv1dLayer`, this function may be easier.
  
      Parameters
      ----------
      net : TensorLayer layer.
      n_filter : number of filter.
      filter_size : an int.
      stride : an int.
      act : None or activation function.
      others : see :class:`Conv1dLayer`.
      """
      if act is None:
          act = tf.identity
      net = Conv1dLayer(layer = net,
              act = act,
              shape = [filter_size, int(net.outputs.get_shape()[-1]), n_filter],
              stride = stride,
              padding = padding,
              use_cudnn_on_gpu = use_cudnn_on_gpu,
              data_format = data_format,
              W_init = W_init,
              b_init = b_init,
              W_init_args = W_init_args,
              b_init_args = b_init_args,
              name = name,
          )
      return net
  
  def Conv2d(net, n_filter=32, filter_size=(3, 3), strides=(1, 1), act = None,
          padding='SAME', W_init = tf.truncated_normal_initializer(stddev=0.02), b_init = tf.constant_initializer(value=0.0),
          W_init_args = {}, b_init_args = {}, name ='conv2d',):
      """Wrapper for :class:`Conv2dLayer`, if you don't understand how to use :class:`Conv2dLayer`, this function may be easier.
  
      Parameters
      ----------
      net : TensorLayer layer.
      n_filter : number of filter.
      filter_size : tuple (height, width) for filter size.
      strides : tuple (height, width) for strides.
      act : None or activation function.
      others : see :class:`Conv2dLayer`.
  
      Examples
      --------
      >>> w_init = tf.truncated_normal_initializer(stddev=0.01)
      >>> b_init = tf.constant_initializer(value=0.0)
      >>> inputs = InputLayer(x, name='inputs')
      >>> conv1 = Conv2d(inputs, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_1')
      >>> conv1 = Conv2d(conv1, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_2')
      >>> pool1 = MaxPool2d(conv1, (2, 2), padding='SAME', name='pool1')
      >>> conv2 = Conv2d(pool1, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_1')
      >>> conv2 = Conv2d(conv2, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_2')
      >>> pool2 = MaxPool2d(conv2, (2, 2), padding='SAME', name='pool2')
      """
      assert len(strides) == 2, "len(strides) should be 2, Conv2d and Conv2dLayer are different."
      if act is None:
          act = tf.identity
      net = Conv2dLayer(net,
                         act = act,
                         shape = [filter_size[0], filter_size[1], int(net.outputs.get_shape()[-1]), n_filter],  # 32 features for each 5x5 patch
                         strides = [1, strides[0], strides[1], 1],
                         padding = padding,
                         W_init = W_init,
                         W_init_args = W_init_args,
                         b_init = b_init,
                         b_init_args = b_init_args,
                         name = name)
      return net
  
  def DeConv2d(net, n_out_channel = 32, filter_size=(3, 3),
          out_size = (30, 30), strides = (2, 2), padding = 'SAME', batch_size = None, act = None,
          W_init = tf.truncated_normal_initializer(stddev=0.02), b_init = tf.constant_initializer(value=0.0),
          W_init_args = {}, b_init_args = {}, name ='decnn2d'):
      """Wrapper for :class:`DeConv2dLayer`, if you don't understand how to use :class:`DeConv2dLayer`, this function may be easier.
  
      Parameters
      ----------
      net : TensorLayer layer.
      n_out_channel : int, number of output channel.
      filter_size : tuple of (height, width) for filter size.
      out_size :  tuple of (height, width) of output.
      batch_size : int or None, batch_size. If None, try to find the batch_size from the first dim of net.outputs (you should tell the batch_size when define the input placeholder).
      strides : tuple of (height, width) for strides.
      act : None or activation function.
      others : see :class:`DeConv2dLayer`.
      """
      assert len(strides) == 2, "len(strides) should be 2, DeConv2d and DeConv2dLayer are different."
      if act is None:
          act = tf.identity
      if batch_size is None:
          batch_size = tf.shape(net.outputs)[0]
      net = DeConv2dLayer(layer = net,
                      act = act,
                      shape = [filter_size[0], filter_size[1], n_out_channel, int(net.outputs.get_shape()[-1])],
                      output_shape = [batch_size, int(out_size[0]), int(out_size[1]), n_out_channel],
                      strides = [1, strides[0], strides[1], 1],
                      padding = padding,
                      W_init = W_init,
                      b_init = b_init,
                      W_init_args = W_init_args,
                      b_init_args = b_init_args,
                      name = name)
      return net
  
  def MaxPool1d(net, filter_size, strides, padding='valid', data_format='channels_last', name=None): #Untested
      """Wrapper for `tf.layers.max_pooling1d <https://www.tensorflow.org/api_docs/python/tf/layers/max_pooling1d>`_ .
  
      Parameters
      ------------
      net : TensorLayer layer, the tensor over which to pool. Must have rank 3.
      filter_size (pool_size) : An integer or tuple/list of a single integer, representing the size of the pooling window.
      strides : An integer or tuple/list of a single integer, specifying the strides of the pooling operation.
      padding : A string. The padding method, either 'valid' or 'same'. Case-insensitive.
      data_format : A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, length, channels) while channels_first corresponds to inputs with shape (batch, channels, length).
      name : A string, the name of the layer.
  
      Returns
      --------
      - A :class:`Layer` which the output tensor, of rank 3.
      """
      print("  [TL] MaxPool1d %s: filter_size:%s strides:%s padding:%s" %
                          (name, str(filter_size), str(strides), str(padding)))
      outputs = tf.layers.max_pooling1d(net.outputs, filter_size, strides, padding=padding, data_format=data_format, name=name)
  
      net_new = copy.copy(net)
      net_new.outputs = outputs
      net_new.all_layers.extend( [outputs] )
      return net_new
  
  def MeanPool1d(net, filter_size, strides, padding='valid', data_format='channels_last', name=None): #Untested
      """Wrapper for `tf.layers.average_pooling1d <https://www.tensorflow.org/api_docs/python/tf/layers/average_pooling1d>`_ .
  
      Parameters
      ------------
      net : TensorLayer layer, the tensor over which to pool. Must have rank 3.
      filter_size (pool_size) : An integer or tuple/list of a single integer, representing the size of the pooling window.
      strides : An integer or tuple/list of a single integer, specifying the strides of the pooling operation.
      padding : A string. The padding method, either 'valid' or 'same'. Case-insensitive.
      data_format : A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, length, channels) while channels_first corresponds to inputs with shape (batch, channels, length).
      name : A string, the name of the layer.
  
      Returns
      --------
      - A :class:`Layer` which the output tensor, of rank 3.
      """
      print("  [TL] MeanPool1d %s: filter_size:%s strides:%s padding:%s" %
                          (name, str(filter_size), str(strides), str(padding)))
      outputs = tf.layers.average_pooling1d(net.outputs, filter_size, strides, padding=padding, data_format=data_format, name=name)
  
      net_new = copy.copy(net)
      net_new.outputs = outputs
      net_new.all_layers.extend( [outputs] )
      return net_new
  
  def MaxPool2d(net, filter_size=(2, 2), strides=None, padding='SAME', name='maxpool'):
      """Wrapper for :class:`PoolLayer`.
  
      Parameters
      -----------
      net : TensorLayer layer.
      filter_size : tuple of (height, width) for filter size.
      strides : tuple of (height, width). Default is the same with filter_size.
      others : see :class:`PoolLayer`.
      """
      if strides is None:
          strides = filter_size
      assert len(strides) == 2, "len(strides) should be 2, MaxPool2d and PoolLayer are different."
      net = PoolLayer(net, ksize=[1, filter_size[0], filter_size[1], 1],
              strides=[1, strides[0], strides[1], 1],
              padding=padding,
              pool = tf.nn.max_pool,
              name = name)
      return net
  
  def MeanPool2d(net, filter_size=(2, 2), strides=None, padding='SAME', name='meanpool'):
      """Wrapper for :class:`PoolLayer`.
  
      Parameters
      -----------
      net : TensorLayer layer.
      filter_size : tuple of (height, width) for filter size.
      strides : tuple of (height, width). Default is the same with filter_size.
      others : see :class:`PoolLayer`.
      """
      if strides is None:
          strides = filter_size
      assert len(strides) == 2, "len(strides) should be 2, MeanPool2d and PoolLayer are different."
      net = PoolLayer(net, ksize=[1, filter_size[0], filter_size[1], 1],
              strides=[1, strides[0], strides[1], 1],
              padding=padding,
              pool = tf.nn.avg_pool,
              name = name)
      return net
  
  def MaxPool3d(net, filter_size, strides, padding='valid', data_format='channels_last', name=None): #Untested
      """Wrapper for `tf.layers.max_pooling3d <https://www.tensorflow.org/api_docs/python/tf/layers/max_pooling3d>`_ .
  
      Parameters
      ------------
      net : TensorLayer layer, the tensor over which to pool. Must have rank 5.
      filter_size (pool_size) : An integer or tuple/list of 3 integers: (pool_depth, pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions.
      strides : An integer or tuple/list of 3 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions.
      padding : A string. The padding method, either 'valid' or 'same'. Case-insensitive.
      data_format : A string. The ordering of the dimensions in the inputs. channels_last (default) and channels_first are supported. channels_last corresponds to inputs with shape (batch, depth, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, depth, height, width).
      name : A string, the name of the layer.
      """
      print("  [TL] MaxPool3d %s: filter_size:%s strides:%s padding:%s" %
                          (name, str(filter_size), str(strides), str(padding)))
      outputs = tf.layers.max_pooling3d(net.outputs, filter_size, strides, padding=padding, data_format=data_format, name=name)
  
      net_new = copy.copy(net)
      net_new.outputs = outputs
      net_new.all_layers.extend( [outputs] )
      return net_new
  
  def MeanPool3d(net, filter_size, strides, padding='valid', data_format='channels_last', name=None): #Untested
      """Wrapper for `tf.layers.average_pooling3d <https://www.tensorflow.org/api_docs/python/tf/layers/average_pooling3d>`_
  
      Parameters
      ------------
      net : TensorLayer layer, the tensor over which to pool. Must have rank 5.
      filter_size (pool_size) : An integer or tuple/list of 3 integers: (pool_depth, pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions.
      strides : An integer or tuple/list of 3 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions.
      padding : A string. The padding method, either 'valid' or 'same'. Case-insensitive.
      data_format : A string. The ordering of the dimensions in the inputs. channels_last (default) and channels_first are supported. channels_last corresponds to inputs with shape (batch, depth, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, depth, height, width).
      name : A string, the name of the layer.
      """
      print("  [TL] MeanPool3d %s: filter_size:%s strides:%s padding:%s name:%s" %
                          (name, str(filter_size), str(strides), str(padding)))
      outputs = tf.layers.average_pooling3d(net.outputs, filter_size, strides, padding=padding, data_format=data_format, name=name)
  
      net_new = copy.copy(net)
      net_new.outputs = outputs
      net_new.all_layers.extend( [outputs] )
      return net_new
  
  ## Super resolution
  def SubpixelConv2d(net, scale=2, n_out_channel=None, act=tf.identity, name='subpixel_conv2d'):
      """The :class:`SubpixelConv2d` class is a sub-pixel 2d convolutional ayer, usually be used
      for super-resolution application.
  
      Parameters
      ------------
      net : TensorLayer layer.
      scale : int, upscaling ratio, a wrong setting will lead to Dimension size error.
      n_out_channel : int or None, the number of output channels.
          Note that, the number of input channels == (scale x scale) x The number of output channels.
          If None, automatically set n_out_channel == the number of input channels / (scale x scale).
      act : activation function.
      name : string.
          An optional name to attach to this layer.
  
      Examples
      ---------
      >>> # examples here just want to tell you how to set the n_out_channel.
      >>> x = np.random.rand(2, 16, 16, 4)
      >>> X = tf.placeholder("float32", shape=(2, 16, 16, 4), name="X")
      >>> net = InputLayer(X, name='input')
      >>> net = SubpixelConv2d(net, scale=2, n_out_channel=1, name='subpixel_conv2d')
      >>> y = sess.run(net.outputs, feed_dict={X: x})
      >>> print(x.shape, y.shape)
      ... (2, 16, 16, 4) (2, 32, 32, 1)
      >>>
      >>> x = np.random.rand(2, 16, 16, 4*10)
      >>> X = tf.placeholder("float32", shape=(2, 16, 16, 4*10), name="X")
      >>> net = InputLayer(X, name='input2')
      >>> net = SubpixelConv2d(net, scale=2, n_out_channel=10, name='subpixel_conv2d2')
      >>> y = sess.run(net.outputs, feed_dict={X: x})
      >>> print(x.shape, y.shape)
      ... (2, 16, 16, 40) (2, 32, 32, 10)
      >>>
      >>> x = np.random.rand(2, 16, 16, 25*10)
      >>> X = tf.placeholder("float32", shape=(2, 16, 16, 25*10), name="X")
      >>> net = InputLayer(X, name='input3')
      >>> net = SubpixelConv2d(net, scale=5, n_out_channel=None, name='subpixel_conv2d3')
      >>> y = sess.run(net.outputs, feed_dict={X: x})
      >>> print(x.shape, y.shape)
      ... (2, 16, 16, 250) (2, 80, 80, 10)
  
      References
      ------------
      - `Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network <https://arxiv.org/pdf/1609.05158.pdf>`_
      """
      # github/Tetrachrome/subpixel  https://github.com/Tetrachrome/subpixel/blob/master/subpixel.py
  
      _err_log = "SubpixelConv2d: The number of input channels == (scale x scale) x The number of output channels"
  
      scope_name = tf.get_variable_scope().name
      if scope_name:
          name = scope_name + '/' + name
  
      def _phase_shift(I, r):
          if tf.__version__ < '1.0':
              raise Exception("Only support TF1.0+")
          bsize, a, b, c = I.get_shape().as_list()
          bsize = tf.shape(I)[0] # Handling Dimension(None) type for undefined batch dim
          X = tf.reshape(I, (bsize, a, b, r, r))
          X = tf.transpose(X, (0, 1, 2, 4, 3))  # bsize, a, b, 1, 1 # tf 0.12
          # X = tf.split(1, a, X)  # a, [bsize, b, r, r] # tf 0.12
          X = tf.split(X, a, 1)
          # X = tf.concat(2, [tf.squeeze(x, axis=1) for x in X])  # bsize, b, a*r, r # tf 0.12
          X = tf.concat([tf.squeeze(x, axis=1) for x in X], 2)
          # X = tf.split(1, b, X)  # b, [bsize, a*r, r] # tf 0.12
          X = tf.split(X, b, 1)
          # X = tf.concat(2, [tf.squeeze(x, axis=1) for x in X])  # bsize, a*r, b*r # tf 0.12
          X = tf.concat([tf.squeeze(x, axis=1) for x in X], 2)
          return tf.reshape(X, (bsize, a*r, b*r, 1))
  
      def _PS(X, r, n_out_channel):
          if n_out_channel > 1:
              assert int(X.get_shape()[-1]) == (r ** 2) * n_out_channel, _err_log
              Xc = tf.split(X, n_out_channel, 3)
              X = tf.concat([_phase_shift(x, r) for x in Xc], 3)
          elif n_out_channel == 1:
              assert int(X.get_shape()[-1]) == (r ** 2), _err_log
              X = _phase_shift(X, r)
          else:
              print(_err_log)
          return X
  
      inputs = net.outputs
  
      if n_out_channel is None:
          assert int(inputs.get_shape()[-1])/ (scale ** 2) % 1 == 0, _err_log
          n_out_channel = int(int(inputs.get_shape()[-1])/ (scale ** 2))
  
      print("  [TL] SubpixelConv2d  %s: scale: %d n_out_channel: %s act: %s" % (name, scale, n_out_channel, act.__name__))
  
      net_new = Layer(inputs, name=name)
      # with tf.name_scope(name):
      with tf.variable_scope(name) as vs:
          net_new.outputs = act(_PS(inputs, r=scale, n_out_channel=n_out_channel))
  
      net_new.all_layers = list(net.all_layers)
      net_new.all_params = list(net.all_params)
      net_new.all_drop = dict(net.all_drop)
      net_new.all_layers.extend( [net_new.outputs] )
      return net_new
  
  
  # ## Normalization layer
  class LocalResponseNormLayer(Layer):
      """The :class:`LocalResponseNormLayer` class is for Local Response Normalization, see ``tf.nn.local_response_normalization``.
      The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently.
      Within a given vector, each component is divided by the weighted, squared sum of inputs within depth_radius.
  
      Parameters
      -----------
      layer : a layer class. Must be one of the following types: float32, half. 4-D.
      depth_radius : An optional int. Defaults to 5. 0-D. Half-width of the 1-D normalization window.
      bias : An optional float. Defaults to 1. An offset (usually positive to avoid dividing by 0).
      alpha : An optional float. Defaults to 1. A scale factor, usually positive.
      beta : An optional float. Defaults to 0.5. An exponent.
      name : A string or None, an optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          depth_radius = None,
          bias = None,
          alpha = None,
          beta = None,
          name ='lrn_layer',
      ):
          self.inputs = layer.outputs
          print("  [TL] LocalResponseNormLayer %s: depth_radius: %d, bias: %f, alpha: %f, beta: %f" %
                              (self.name, depth_radius, bias, alpha, beta))
          with tf.variable_scope(name) as vs:
              self.outputs = tf.nn.local_response_normalization(self.inputs, depth_radius=depth_radius, bias=bias, alpha=alpha, beta=beta)
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
  
  class BatchNormLayer(Layer):
      """
      The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``.
  
      Batch normalization on fully-connected or convolutional maps.
  
      Parameters
      -----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      decay : float, default is 0.9.
          A decay factor for ExponentialMovingAverage, use larger value for large dataset.
      epsilon : float
          A small float number to avoid dividing by 0.
      act : activation function.
      is_train : boolean
          Whether train or inference.
      beta_init : beta initializer
          The initializer for initializing beta
      gamma_init : gamma initializer
          The initializer for initializing gamma
      name : a string or None
          An optional name to attach to this layer.
  
      References
      ----------
      - `Source <https://github.com/ry/tensorflow-resnet/blob/master/resnet.py>`_
      - `stackoverflow <http://stackoverflow.com/questions/38312668/how-does-one-do-inference-with-batch-normalization-with-tensor-flow>`_
      """
      def __init__(
          self,
          layer = None,
          decay = 0.9,
          epsilon = 0.00001,
          act = tf.identity,
          is_train = False,
          beta_init = tf.zeros_initializer,
          gamma_init = tf.random_normal_initializer(mean=1.0, stddev=0.002), # tf.ones_initializer,
          name ='batchnorm_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          print("  [TL] BatchNormLayer %s: decay:%f epsilon:%f act:%s is_train:%s" %
                              (self.name, decay, epsilon, act.__name__, is_train))
          x_shape = self.inputs.get_shape()
          params_shape = x_shape[-1:]
  
          from tensorflow.python.training import moving_averages
          from tensorflow.python.ops import control_flow_ops
  
          with tf.variable_scope(name) as vs:
              axis = list(range(len(x_shape) - 1))
  
              ## 1. beta, gamma
              if tf.__version__ > '0.12.1' and beta_init == tf.zeros_initializer:
                  beta_init = beta_init()
              beta = tf.get_variable('beta', shape=params_shape,
                                 initializer=beta_init,
                                 trainable=is_train)#, restore=restore)
  
              gamma = tf.get_variable('gamma', shape=params_shape,
                                  initializer=gamma_init, trainable=is_train,
                                  )#restore=restore)
  
              ## 2.
              if tf.__version__ > '0.12.1':
                  moving_mean_init = tf.zeros_initializer()
              else:
                  moving_mean_init = tf.zeros_initializer
              moving_mean = tf.get_variable('moving_mean',
                                        params_shape,
                                        initializer=moving_mean_init,
                                        trainable=False,)#   restore=restore)
              moving_variance = tf.get_variable('moving_variance',
                                            params_shape,
                                            initializer=tf.constant_initializer(1.),
                                            trainable=False,)#   restore=restore)
  
              ## 3.
              # These ops will only be preformed when training.
              mean, variance = tf.nn.moments(self.inputs, axis)
              try:    # TF12
                  update_moving_mean = moving_averages.assign_moving_average(
                                  moving_mean, mean, decay, zero_debias=False)     # if zero_debias=True, has bias
                  update_moving_variance = moving_averages.assign_moving_average(
                                  moving_variance, variance, decay, zero_debias=False) # if zero_debias=True, has bias
                  # print("TF12 moving")
              except Exception as e:  # TF11
                  update_moving_mean = moving_averages.assign_moving_average(
                                  moving_mean, mean, decay)
                  update_moving_variance = moving_averages.assign_moving_average(
                                  moving_variance, variance, decay)
                  # print("TF11 moving")
  
              def mean_var_with_update():
                  with tf.control_dependencies([update_moving_mean, update_moving_variance]):
                      return tf.identity(mean), tf.identity(variance)
  
              if is_train:
                  mean, var = mean_var_with_update()
                  self.outputs = act( tf.nn.batch_normalization(self.inputs, mean, var, beta, gamma, epsilon) )
              else:
                  self.outputs = act( tf.nn.batch_normalization(self.inputs, moving_mean, moving_variance, beta, gamma, epsilon) )
  
              variables = [beta, gamma, moving_mean, moving_variance]
  
              # print(len(variables))
              # for idx, v in enumerate(variables):
              #     print("  var {:3}: {:15}   {}".format(idx, str(v.get_shape()), v))
              # exit()
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( variables )
  
  # class BatchNormLayer_TF(Layer):   # Work well TF contrib https://github.com/tensorflow/tensorflow/blob/b826b79718e3e93148c3545e7aa3f90891744cc0/tensorflow/contrib/layers/python/layers/layers.py#L100
  #     """
  #     The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``.
  #
  #     Batch normalization on fully-connected or convolutional maps.
  #
  #     Parameters
  #     -----------
  #     layer : a :class:`Layer` instance
  #         The `Layer` class feeding into this layer.
  #     decay : float
  #         A decay factor for ExponentialMovingAverage.
  #     center: If True, subtract `beta`. If False, `beta` is ignored.
  #     scale: If True, multiply by `gamma`. If False, `gamma` is
  #         not used. When the next layer is linear (also e.g. `nn.relu`), this can be
  #         disabled since the scaling can be done by the next layer.
  #     epsilon : float
  #         A small float number to avoid dividing by 0.
  #     act : activation function.
  #     is_train : boolean
  #         Whether train or inference.
  #     beta_init : beta initializer
  #         The initializer for initializing beta
  #     gamma_init : gamma initializer
  #         The initializer for initializing gamma
  #     name : a string or None
  #         An optional name to attach to this layer.
  #
  #     References
  #     ----------
  #     - `Source <https://github.com/ry/tensorflow-resnet/blob/master/resnet.py>`_
  #     - `stackoverflow <http://stackoverflow.com/questions/38312668/how-does-one-do-inference-with-batch-normalization-with-tensor-flow>`_
  #     """
  #     def __init__(
  #         self,
  #         layer = None,
  #         decay = 0.95,#.999,
  #         center = True,
  #         scale = True,
  #         epsilon = 0.00001,
  #         act = tf.identity,
  #         is_train = False,
  #         beta_init = tf.zeros_initializer,
  #         # gamma_init = tf.ones_initializer,
  #         gamma_init = tf.random_normal_initializer(mean=1.0, stddev=0.002),
  #         name ='batchnorm_layer',
  #     ):
  #         Layer.__init__(self, name=name)
  #         self.inputs = layer.outputs
  #         print("  [TL] BatchNormLayer %s: decay: %f, epsilon: %f, act: %s, is_train: %s" %
  #                             (self.name, decay, epsilon, act.__name__, is_train))
  #         from tensorflow.contrib.layers.python.layers import utils
  #         from tensorflow.contrib.framework.python.ops import variables
  #         from tensorflow.python.ops import init_ops
  #         from tensorflow.python.ops import nn
  #         from tensorflow.python.training import moving_averages
  #         from tensorflow.python.framework import ops
  #         from tensorflow.python.ops import variable_scope
  #         variables_collections = None
  #         outputs_collections=None
  #         updates_collections=None#ops.GraphKeys.UPDATE_OPS
  #         # with variable_scope.variable_op_scope([inputs],
  #         #                                     scope, 'BatchNorm', reuse=reuse) as sc:
  #         # with variable_scope.variable_op_scope([self.inputs], None, name) as vs:
  #         with tf.variable_scope(name) as vs:
  #             inputs_shape = self.inputs.get_shape()
  #             dtype = self.inputs.dtype.base_dtype
  #             axis = list(range(len(inputs_shape) - 1)) # [0, 1, 2]
  #             params_shape = inputs_shape[-1:]
  #             # Allocate parameters for the beta and gamma of the normalization.
  #             beta, gamma = None, None
  #             if center:
  #               beta_collections = utils.get_variable_collections(variables_collections,
  #                                                                 'beta')
  #               beta = variables.model_variable('beta',
  #                                               shape=params_shape,
  #                                               dtype=dtype,
  #                                             #   initializer=init_ops.zeros_initializer,
  #                                               initializer=beta_init,
  #                                               collections=beta_collections,)
  #                                             #   trainable=trainable)
  #             if scale:
  #               gamma_collections = utils.get_variable_collections(variables_collections,
  #                                                                  'gamma')
  #               gamma = variables.model_variable('gamma',
  #                                                shape=params_shape,
  #                                                dtype=dtype,
  #                                             #    initializer=init_ops.ones_initializer,
  #                                                initializer=gamma_init,
  #                                                collections=gamma_collections,)
  #                                             #    trainable=trainable)
  #             # Create moving_mean and moving_variance variables and add them to the
  #             # appropiate collections.
  #             moving_mean_collections = utils.get_variable_collections(
  #                 variables_collections,
  #                 'moving_mean')
  #             moving_mean = variables.model_variable(
  #                 'moving_mean',
  #                 shape=params_shape,
  #                 dtype=dtype,
  #                 # initializer=init_ops.zeros_initializer,
  #                 initializer=tf.zeros_initializer,
  #                 trainable=False,
  #                 collections=moving_mean_collections)
  #             moving_variance_collections = utils.get_variable_collections(
  #                 variables_collections,
  #                 'moving_variance')
  #             moving_variance = variables.model_variable(
  #                 'moving_variance',
  #                 shape=params_shape,
  #                 dtype=dtype,
  #                 # initializer=init_ops.ones_initializer,
  #                 initializer=tf.constant_initializer(1.),
  #                 trainable=False,
  #                 collections=moving_variance_collections)
  #             if is_train:
  #               # Calculate the moments based on the individual batch.
  #               mean, variance = nn.moments(self.inputs, axis, shift=moving_mean)
  #               # Update the moving_mean and moving_variance moments.
  #             #   update_moving_mean = moving_averages.assign_moving_average(
  #             #       moving_mean, mean, decay)
  #             #   update_moving_variance = moving_averages.assign_moving_average(
  #             #       moving_variance, variance, decay)
  #             #   if updates_collections is None:
  #             #     # Make sure the updates are computed here.
  #             #       with ops.control_dependencies([update_moving_mean,
  #             #                                        update_moving_variance]):
  #             #          outputs = nn.batch_normalization(
  #             #               self.inputs, mean, variance, beta, gamma, epsilon)
  #
  #               update_moving_mean = tf.assign(moving_mean,
  #                                    moving_mean * decay + mean * (1 - decay))
  #               update_moving_variance = tf.assign(moving_variance,
  #                                   moving_variance * decay + variance * (1 - decay))
  #               with tf.control_dependencies([update_moving_mean, update_moving_variance]):
  #                   outputs = nn.batch_normalization(
  #                               self.inputs, mean, variance, beta, gamma, epsilon)
  #             #   else:
  #             #     # Collect the updates to be computed later.
  #             #     ops.add_to_collections(updates_collections, update_moving_mean)
  #             #     ops.add_to_collections(updates_collections, update_moving_variance)
  #             #     outputs = nn.batch_normalization(
  #             #         self.inputs, mean, variance, beta, gamma, epsilon)
  #             else:
  #             #   mean, variance = nn.moments(self.inputs, axis, shift=moving_mean)
  #               outputs = nn.batch_normalization(
  #                   self.inputs, moving_mean, moving_variance, beta, gamma, epsilon)
  #                 # self.inputs, mean, variance, beta, gamma, epsilon)
  #             outputs.set_shape(self.inputs.get_shape())
  #             # if activation_fn:
  #             self.outputs = act(outputs)
  #
  #             # variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
  #             # return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
  #             variables = [beta, gamma, moving_mean, moving_variance]
  #
  #         mean, variance = nn.moments(self.inputs, axis, shift=moving_mean)
  #         self.check_mean = mean
  #         self.check_variance = variance
  #
  #         self.all_layers = list(layer.all_layers)
  #         self.all_params = list(layer.all_params)
  #         self.all_drop = dict(layer.all_drop)
  #         self.all_layers.extend( [self.outputs] )
  #         self.all_params.extend( variables )
  #
  # class BatchNormLayer5(Layer):   # Akara Work well
  #     """
  #     The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``.
  #
  #     Batch normalization on fully-connected or convolutional maps.
  #
  #     Parameters
  #     -----------
  #     layer : a :class:`Layer` instance
  #         The `Layer` class feeding into this layer.
  #     decay : float
  #         A decay factor for ExponentialMovingAverage.
  #     epsilon : float
  #         A small float number to avoid dividing by 0.
  #     act : activation function.
  #     is_train : boolean
  #         Whether train or inference.
  #     beta_init : beta initializer
  #         The initializer for initializing beta
  #     gamma_init : gamma initializer
  #         The initializer for initializing gamma
  #     name : a string or None
  #         An optional name to attach to this layer.
  #
  #     References
  #     ----------
  #     - `Source <https://github.com/ry/tensorflow-resnet/blob/master/resnet.py>`_
  #     - `stackoverflow <http://stackoverflow.com/questions/38312668/how-does-one-do-inference-with-batch-normalization-with-tensor-flow>`_
  #     """
  #     def __init__(
  #         self,
  #         layer = None,
  #         decay = 0.9,
  #         epsilon = 0.00001,
  #         act = tf.identity,
  #         is_train = False,
  #         beta_init = tf.zeros_initializer,
  #         # gamma_init = tf.ones_initializer,
  #         gamma_init = tf.random_normal_initializer(mean=1.0, stddev=0.002),
  #         name ='batchnorm_layer',
  #     ):
  #         Layer.__init__(self, name=name)
  #         self.inputs = layer.outputs
  #         print("  [TL] BatchNormLayer %s: decay: %f, epsilon: %f, act: %s, is_train: %s" %
  #                             (self.name, decay, epsilon, act.__name__, is_train))
  #         x_shape = self.inputs.get_shape()
  #         params_shape = x_shape[-1:]
  #
  #         from tensorflow.python.training import moving_averages
  #         from tensorflow.python.ops import control_flow_ops
  #
  #         with tf.variable_scope(name) as vs:
  #             axis = list(range(len(x_shape) - 1))
  #
  #             ## 1. beta, gamma
  #             beta = tf.get_variable('beta', shape=params_shape,
  #                                initializer=beta_init,
  #                                trainable=is_train)#, restore=restore)
  #
  #             gamma = tf.get_variable('gamma', shape=params_shape,
  #                                 initializer=gamma_init, trainable=is_train,
  #                                 )#restore=restore)
  #
  #             ## 2. moving variables during training (not update by gradient!)
  #             moving_mean = tf.get_variable('moving_mean',
  #                                       params_shape,
  #                                       initializer=tf.zeros_initializer,
  #                                       trainable=False,)#   restore=restore)
  #             moving_variance = tf.get_variable('moving_variance',
  #                                           params_shape,
  #                                           initializer=tf.constant_initializer(1.),
  #                                           trainable=False,)#   restore=restore)
  #
  #             batch_mean, batch_var = tf.nn.moments(self.inputs, axis)
  #             ## 3.
  #             # These ops will only be preformed when training.
  #             def mean_var_with_update():
  #                 try:    # TF12
  #                     update_moving_mean = moving_averages.assign_moving_average(
  #                                     moving_mean, batch_mean, decay, zero_debias=False)     # if zero_debias=True, has bias
  #                     update_moving_variance = moving_averages.assign_moving_average(
  #                                     moving_variance, batch_var, decay, zero_debias=False) # if zero_debias=True, has bias
  #                     # print("TF12 moving")
  #                 except Exception as e:  # TF11
  #                     update_moving_mean = moving_averages.assign_moving_average(
  #                                     moving_mean, batch_mean, decay)
  #                     update_moving_variance = moving_averages.assign_moving_average(
  #                                     moving_variance, batch_var, decay)
  #                     # print("TF11 moving")
  #
  #             # def mean_var_with_update():
  #                 with tf.control_dependencies([update_moving_mean, update_moving_variance]):
  #                     # return tf.identity(update_moving_mean), tf.identity(update_moving_variance)
  #                     return tf.identity(batch_mean), tf.identity(batch_var)
  #
  #             # if not is_train:
  #             if is_train:
  #                 mean, var = mean_var_with_update()
  #             else:
  #                 mean, var = (moving_mean, moving_variance)
  #
  #             normed = tf.nn.batch_normalization(
  #               x=self.inputs,
  #               mean=mean,
  #               variance=var,
  #               offset=beta,
  #               scale=gamma,
  #               variance_epsilon=epsilon,
  #               name="tf_bn"
  #             )
  #             self.outputs = act( normed )
  #
  #             variables = [beta, gamma, moving_mean, moving_variance]
  #             # print(len(variables))
  #             # for idx, v in enumerate(variables):
  #             #     print("  var {:3}: {:15}   {}".format(idx, str(v.get_shape()), v))
  #             # exit()
  #
  #         self.all_layers = list(layer.all_layers)
  #         self.all_params = list(layer.all_params)
  #         self.all_drop = dict(layer.all_drop)
  #         self.all_layers.extend( [self.outputs] )
  #         self.all_params.extend( variables )
  #         # self.all_params.extend( [beta, gamma] )
  #
  # class BatchNormLayer4(Layer): # work TFlearn https://github.com/tflearn/tflearn/blob/master/tflearn/layers/normalization.py
  #     """
  #     The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``.
  #
  #     Batch normalization on fully-connected or convolutional maps.
  #
  #     Parameters
  #     -----------
  #     layer : a :class:`Layer` instance
  #         The `Layer` class feeding into this layer.
  #     decay : float
  #         A decay factor for ExponentialMovingAverage.
  #     epsilon : float
  #         A small float number to avoid dividing by 0.
  #     act : activation function.
  #     is_train : boolean
  #         Whether train or inference.
  #     beta_init : beta initializer
  #         The initializer for initializing beta
  #     gamma_init : gamma initializer
  #         The initializer for initializing gamma
  #     name : a string or None
  #         An optional name to attach to this layer.
  #
  #     References
  #     ----------
  #     - `Source <https://github.com/ry/tensorflow-resnet/blob/master/resnet.py>`_
  #     - `stackoverflow <http://stackoverflow.com/questions/38312668/how-does-one-do-inference-with-batch-normalization-with-tensor-flow>`_
  #     """
  #     def __init__(
  #         self,
  #         layer = None,
  #         decay = 0.999,
  #         epsilon = 0.00001,
  #         act = tf.identity,
  #         is_train = None,
  #         beta_init = tf.zeros_initializer,
  #         # gamma_init = tf.ones_initializer,
  #         gamma_init = tf.random_normal_initializer(mean=1.0, stddev=0.002),
  #         name ='batchnorm_layer',
  #     ):
  #         Layer.__init__(self, name=name)
  #         self.inputs = layer.outputs
  #         print("  [TL] BatchNormLayer %s: decay: %f, epsilon: %f, act: %s, is_train: %s" %
  #                             (self.name, decay, epsilon, act.__name__, is_train))
  #         input_shape = self.inputs.get_shape()
  #         # params_shape = input_shape[-1:]
  #         input_ndim = len(input_shape)
  #         from tensorflow.python.training import moving_averages
  #         from tensorflow.python.ops import control_flow_ops
  #
  #         # gamma_init = tf.random_normal_initializer(mean=gamma, stddev=stddev)
  #
  #         # Variable Scope fix for older TF
  #         scope = name
  #         try:
  #             vscope = tf.variable_scope(scope, default_name=name, values=[self.inputs],)
  #                                     #    reuse=reuse)
  #         except Exception:
  #             vscope = tf.variable_op_scope([self.inputs], scope, name)#, reuse=reuse)
  #
  #         with vscope as scope:
  #             name = scope.name
  #         # with tf.variable_scope(name) as vs:
  #             beta = tf.get_variable('beta', shape=[input_shape[-1]],
  #                                 initializer=beta_init,)
  #                             #    initializer=tf.constant_initializer(beta),)
  #                             #    trainable=trainable, )#restore=restore)
  #             gamma = tf.get_variable('gamma', shape=[input_shape[-1]],
  #                                 initializer=gamma_init, )#trainable=trainable,)
  #                                 # restore=restore)
  #
  #             axis = list(range(input_ndim - 1))
  #             moving_mean = tf.get_variable('moving_mean',
  #                                       input_shape[-1:],
  #                                       initializer=tf.zeros_initializer,
  #                                       trainable=False,)
  #                                     #   restore=restore)
  #             moving_variance = tf.get_variable('moving_variance',
  #                                           input_shape[-1:],
  #                                           initializer=tf.constant_initializer(1.),
  #                                           trainable=False,)
  #                                         #   restore=restore)
  #
  #             # Define a function to update mean and variance
  #             def update_mean_var():
  #                 mean, variance = tf.nn.moments(self.inputs, axis)
  #
  #                 # Fix TF 0.12
  #                 try:
  #                     update_moving_mean = moving_averages.assign_moving_average(
  #                         moving_mean, mean, decay, zero_debias=False)            # if zero_debias=True, accuracy is high ..
  #                     update_moving_variance = moving_averages.assign_moving_average(
  #                         moving_variance, variance, decay, zero_debias=False)
  #                 except Exception as e:  # TF 11
  #                     update_moving_mean = moving_averages.assign_moving_average(
  #                         moving_mean, mean, decay)
  #                     update_moving_variance = moving_averages.assign_moving_average(
  #                         moving_variance, variance, decay)
  #
  #                 with tf.control_dependencies(
  #                         [update_moving_mean, update_moving_variance]):
  #                     return tf.identity(mean), tf.identity(variance)
  #
  #             # Retrieve variable managing training mode
  #             # is_training = tflearn.get_training_mode()
  #             if not is_train:    # test : mean=0, std=1
  #             # if is_train:      # train : mean=0, std=1
  #                 is_training = tf.cast(tf.ones([]), tf.bool)
  #             else:
  #                 is_training = tf.cast(tf.zeros([]), tf.bool)
  #             mean, var = tf.cond(
  #                 is_training, update_mean_var, lambda: (moving_mean, moving_variance))
  #                             #  ones                 zeros
  #             try:
  #                 inference = tf.nn.batch_normalization(
  #                     self.inputs, mean, var, beta, gamma, epsilon)
  #                 inference.set_shape(input_shape)
  #             # Fix for old Tensorflow
  #             except Exception as e:
  #                 inference = tf.nn.batch_norm_with_global_normalization(
  #                     self.inputs, mean, var, beta, gamma, epsilon,
  #                     scale_after_normalization=True,
  #                 )
  #                 inference.set_shape(input_shape)
  #
  #             variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope.name)    # 2 params beta, gamma
  #                 # variables = [beta, gamma, moving_mean, moving_variance]
  #
  #             # print(len(variables))
  #             # for idx, v in enumerate(variables):
  #             #     print("  var {:3}: {:15}   {}".format(idx, str(v.get_shape()), v.name))
  #             # exit()
  #
  #         # Add attributes for easy access
  #         # inference.scope = scope
  #         inference.scope = name
  #         inference.beta = beta
  #         inference.gamma = gamma
  #
  #         self.outputs = act( inference )
  #
  #         self.all_layers = list(layer.all_layers)
  #         self.all_params = list(layer.all_params)
  #         self.all_drop = dict(layer.all_drop)
  #         self.all_layers.extend( [self.outputs] )
  #         self.all_params.extend( variables )
  
  # class BatchNormLayer2(Layer):   # don't work http://r2rt.com/implementing-batch-normalization-in-tensorflow.html
  #     """
  #     The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``.
  #
  #     Batch normalization on fully-connected or convolutional maps.
  #
  #     Parameters
  #     -----------
  #     layer : a :class:`Layer` instance
  #         The `Layer` class feeding into this layer.
  #     decay : float
  #         A decay factor for ExponentialMovingAverage.
  #     epsilon : float
  #         A small float number to avoid dividing by 0.
  #     act : activation function.
  #     is_train : boolean
  #         Whether train or inference.
  #     beta_init : beta initializer
  #         The initializer for initializing beta
  #     gamma_init : gamma initializer
  #         The initializer for initializing gamma
  #     name : a string or None
  #         An optional name to attach to this layer.
  #
  #     References
  #     ----------
  #     - `Source <https://github.com/ry/tensorflow-resnet/blob/master/resnet.py>`_
  #     - `stackoverflow <http://stackoverflow.com/questions/38312668/how-does-one-do-inference-with-batch-normalization-with-tensor-flow>`_
  #     """
  #     def __init__(
  #         self,
  #         layer = None,
  #         decay = 0.999,
  #         epsilon = 0.00001,
  #         act = tf.identity,
  #         is_train = None,
  #         beta_init = tf.zeros_initializer,
  #         # gamma_init = tf.ones_initializer,
  #         gamma_init = tf.random_normal_initializer(mean=1.0, stddev=0.002),
  #         name ='batchnorm_layer',
  #     ):
  #         Layer.__init__(self, name=name)
  #         self.inputs = layer.outputs
  #         print("  [TL] BatchNormLayer %s: decay: %f, epsilon: %f, act: %s, is_train: %s" %
  #                             (self.name, decay, epsilon, act.__name__, is_train))
  #         x_shape = self.inputs.get_shape()
  #         params_shape = x_shape[-1:]
  #
  #         with tf.variable_scope(name) as vs:
  #             gamma = tf.get_variable("gamma", shape=params_shape,
  #                         initializer=gamma_init)
  #             beta = tf.get_variable("beta", shape=params_shape,
  #                         initializer=beta_init)
  #             pop_mean = tf.get_variable("pop_mean", shape=params_shape,
  #                         initializer=tf.zeros_initializer, trainable=False)
  #             pop_var = tf.get_variable("pop_var", shape=params_shape,
  #                         initializer=tf.constant_initializer(1.), trainable=False)
  #
  #             if is_train:
  #                 batch_mean, batch_var = tf.nn.moments(self.inputs, list(range(len(x_shape) - 1)))
  #                 train_mean = tf.assign(pop_mean,
  #                                        pop_mean * decay + batch_mean * (1 - decay))
  #                 train_var = tf.assign(pop_var,
  #                                       pop_var * decay + batch_var * (1 - decay))
  #                 with tf.control_dependencies([train_mean, train_var]):
  #                     self.outputs = act(tf.nn.batch_normalization(self.inputs,
  #                         batch_mean, batch_var, beta, gamma, epsilon))
  #             else:
  #                 self.outputs = act(tf.nn.batch_normalization(self.inputs,
  #                     pop_mean, pop_var, beta, gamma, epsilon))
  #                     # self.outputs = act( tf.nn.batch_normalization(self.inputs, mean, variance, beta, gamma, epsilon) )
  #             # variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)  # 8 params in TF12 if zero_debias=True
  #             variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=vs.name)    # 2 params beta, gamma
  #                 # variables = [beta, gamma, moving_mean, moving_variance]
  #
  #             # print(len(variables))
  #             # for idx, v in enumerate(variables):
  #             #     print("  var {:3}: {:15}   {}".format(idx, str(v.get_shape()), v.name))
  #             # exit()
  #
  #         self.all_layers = list(layer.all_layers)
  #         self.all_params = list(layer.all_params)
  #         self.all_drop = dict(layer.all_drop)
  #         self.all_layers.extend( [self.outputs] )
  #         self.all_params.extend( variables )
  
  # class BatchNormLayer3(Layer):   # don't work http://r2rt.com/implementing-batch-normalization-in-tensorflow.html
  #     """
  #     The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization`` and ``tf.nn.moments``.
  #
  #     Batch normalization on fully-connected or convolutional maps.
  #
  #     Parameters
  #     -----------
  #     layer : a :class:`Layer` instance
  #         The `Layer` class feeding into this layer.
  #     decay : float
  #         A decay factor for ExponentialMovingAverage.
  #     epsilon : float
  #         A small float number to avoid dividing by 0.
  #     act : activation function.
  #     is_train : boolean
  #         Whether train or inference.
  #     beta_init : beta initializer
  #         The initializer for initializing beta
  #     gamma_init : gamma initializer
  #         The initializer for initializing gamma
  #     name : a string or None
  #         An optional name to attach to this layer.
  #
  #     References
  #     ----------
  #     - `Source <https://github.com/ry/tensorflow-resnet/blob/master/resnet.py>`_
  #     - `stackoverflow <http://stackoverflow.com/questions/38312668/how-does-one-do-inference-with-batch-normalization-with-tensor-flow>`_
  #     """
  #     def __init__(
  #         self,
  #         layer = None,
  #         decay = 0.999,
  #         epsilon = 0.00001,
  #         act = tf.identity,
  #         is_train = None,
  #         beta_init = tf.zeros_initializer,
  #         # gamma_init = tf.ones_initializer,
  #         gamma_init = tf.random_normal_initializer(mean=1.0, stddev=0.002),
  #         name ='batchnorm_layer',
  #     ):
  #         """
  #         Batch normalization on convolutional maps.
  #         Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
  #         Args:
  #             x:           Tensor, 4D BHWD input maps
  #             n_out:       integer, depth of input maps
  #             phase_train: boolean tf.Varialbe, true indicates training phase
  #             scope:       string, variable scope
  #         Return:
  #             normed:      batch-normalized maps
  #         """
  #         Layer.__init__(self, name=name)
  #         self.inputs = layer.outputs
  #         print("  [TL] BatchNormLayer %s: decay: %f, epsilon: %f, act: %s, is_train: %s" %
  #                             (self.name, decay, epsilon, act.__name__, is_train))
  #         x_shape = self.inputs.get_shape()
  #         params_shape = x_shape[-1:]
  #
  #         if is_train:
  #             phase_train = tf.cast(tf.ones([]), tf.bool)
  #         else:
  #             phase_train = tf.cast(tf.zeros([]), tf.bool)
  #
  #         with tf.variable_scope(name) as vs:
  #             gamma = tf.get_variable("gamma", shape=params_shape,
  #                         initializer=gamma_init)
  #             beta = tf.get_variable("beta", shape=params_shape,
  #                         initializer=beta_init)
  #             batch_mean, batch_var = tf.nn.moments(self.inputs, list(range(len(x_shape) - 1)),#[0,1,2],
  #                             name='moments')
  #             ema = tf.train.ExponentialMovingAverage(decay=decay)
  #
  #             def mean_var_with_update():
  #                 ema_apply_op = ema.apply([batch_mean, batch_var])
  #                 with tf.control_dependencies([ema_apply_op]):
  #                     return tf.identity(batch_mean), tf.identity(batch_var)
  #
  #             mean, var = tf.cond(phase_train,
  #                                 mean_var_with_update,
  #                                 lambda: (ema.average(batch_mean), ema.average(batch_var)))
  #             normed = tf.nn.batch_normalization(self.inputs, mean, var, beta, gamma, epsilon)
  #             self.outputs = act( normed )
  #             variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=vs.name)    # 2 params beta, gamma
  #                 # variables = [beta, gamma, moving_mean, moving_variance]
  #
  #             # print(len(variables))
  #             # for idx, v in enumerate(variables):
  #             #     print("  var {:3}: {:15}   {}".format(idx, str(v.get_shape()), v.name))
  #             # exit()
  #
  #         self.all_layers = list(layer.all_layers)
  #         self.all_params = list(layer.all_params)
  #         self.all_drop = dict(layer.all_drop)
  #         self.all_layers.extend( [self.outputs] )
  #         self.all_params.extend( variables )
  
  # class BatchNormLayer_old(Layer):  # don't work
  #     """
  #     The :class:`BatchNormLayer` class is a normalization layer, see ``tf.nn.batch_normalization``.
  #
  #     Batch normalization on fully-connected or convolutional maps.
  #
  #     Parameters
  #     -----------
  #     layer : a :class:`Layer` instance
  #         The `Layer` class feeding into this layer.
  #     decay : float
  #         A decay factor for ExponentialMovingAverage.
  #     epsilon : float
  #         A small float number to avoid dividing by 0.
  #     is_train : boolean
  #         Whether train or inference.
  #     name : a string or None
  #         An optional name to attach to this layer.
  #
  #     References
  #     ----------
  #     - `tf.nn.batch_normalization <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.batch_normalization.md>`_
  #     - `stackoverflow <http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow>`_
  #     - `tensorflow.contrib <https://github.com/tensorflow/tensorflow/blob/b826b79718e3e93148c3545e7aa3f90891744cc0/tensorflow/contrib/layers/python/layers/layers.py#L100>`_
  #     """
  #     def __init__(
  #         self,
  #         layer = None,
  #         act = tf.identity,
  #         decay = 0.999,
  #         epsilon = 0.001,
  #         is_train = None,
  #         name ='batchnorm_layer',
  #     ):
  #         Layer.__init__(self, name=name)
  #         self.inputs = layer.outputs
  #         print("  [TL] BatchNormLayer %s: decay: %f, epsilon: %f, is_train: %s" %
  #                             (self.name, decay, epsilon, is_train))
  #         if is_train == None:
  #             raise Exception("is_train must be True or False")
  #
  #         # (name, input_var, decay, epsilon, is_train)
  #         inputs_shape = self.inputs.get_shape()
  #         axis = list(range(len(inputs_shape) - 1))
  #         params_shape = inputs_shape[-1:]
  #
  #         with tf.variable_scope(name) as vs:
  #             beta = tf.get_variable(name='beta', shape=params_shape,
  #                                  initializer=tf.constant_initializer(0.0))
  #             gamma = tf.get_variable(name='gamma', shape=params_shape,
  #                                   initializer=tf.constant_initializer(1.0))
  #             batch_mean, batch_var = tf.nn.moments(self.inputs,
  #                                                 axis,
  #                                                 name='moments')
  #             ema = tf.train.ExponentialMovingAverage(decay=decay)
  #
  #             def mean_var_with_update():
  #               ema_apply_op = ema.apply([batch_mean, batch_var])
  #               with tf.control_dependencies([ema_apply_op]):
  #                   return tf.identity(batch_mean), tf.identity(batch_var)
  #
  #             if is_train:
  #                 is_train = tf.cast(tf.ones(1), tf.bool)
  #             else:
  #                 is_train = tf.cast(tf.zeros(1), tf.bool)
  #
  #             is_train = tf.reshape(is_train, [])
  #
  #             # print(is_train)
  #             # exit()
  #
  #             mean, var = tf.cond(
  #               is_train,
  #               mean_var_with_update,
  #               lambda: (ema.average(batch_mean), ema.average(batch_var))
  #             )
  #             normed = tf.nn.batch_normalization(
  #               x=self.inputs,
  #               mean=mean,
  #               variance=var,
  #               offset=beta,
  #               scale=gamma,
  #               variance_epsilon=epsilon,
  #               name='tf_bn'
  #             )
  #         self.outputs = act( normed )
  #
  #         self.all_layers = list(layer.all_layers)
  #         self.all_params = list(layer.all_params)
  #         self.all_drop = dict(layer.all_drop)
  #         self.all_layers.extend( [self.outputs] )
  #         self.all_params.extend( [beta, gamma] )
  
  ## Pooling layer
  class PoolLayer(Layer):
      """
      The :class:`PoolLayer` class is a Pooling layer, you can choose
      ``tf.nn.max_pool`` and ``tf.nn.avg_pool`` for 2D or
      ``tf.nn.max_pool3d`` and ``tf.nn.avg_pool3d`` for 3D.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      ksize : a list of ints that has length >= 4.
          The size of the window for each dimension of the input tensor.
      strides : a list of ints that has length >= 4.
          The stride of the sliding window for each dimension of the input tensor.
      padding : a string from: "SAME", "VALID".
          The type of padding algorithm to use.
      pool : a pooling function
          - see `TensorFlow pooling APIs <https://www.tensorflow.org/versions/master/api_docs/python/nn.html#pooling>`_
          - class ``tf.nn.max_pool``
          - class ``tf.nn.avg_pool``
          - class ``tf.nn.max_pool3d``
          - class ``tf.nn.avg_pool3d``
      name : a string or None
          An optional name to attach to this layer.
  
      Examples
      --------
      - see :class:`Conv2dLayer`.
      """
      def __init__(
          self,
          layer = None,
          ksize=[1, 2, 2, 1],
          strides=[1, 2, 2, 1],
          padding='SAME',
          pool = tf.nn.max_pool,
          name ='pool_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          print("  [TL] PoolLayer   %s: ksize:%s strides:%s padding:%s pool:%s" %
                              (self.name, str(ksize), str(strides), padding, pool.__name__))
  
          self.outputs = pool(self.inputs, ksize=ksize, strides=strides, padding=padding, name=name)
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
  
  ## Padding layer
  class PadLayer(Layer):
      """
      The :class:`PadLayer` class is a Padding layer for any modes and dimensions.
      Please see `tf.pad <https://www.tensorflow.org/api_docs/python/tf/pad>`_ for usage.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      padding : a Tensor of type int32.
      mode : one of "CONSTANT", "REFLECT", or "SYMMETRIC" (case-insensitive)
      name : a string or None
          An optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          paddings = None,
          mode = 'CONSTANT',
          name = 'pad_layer',
      ):
          Layer.__init__(self, name=name)
          assert paddings is not None, "paddings should be a Tensor of type int32. see https://www.tensorflow.org/api_docs/python/tf/pad"
          self.inputs = layer.outputs
          print("  [TL] PoolLayer   %s: paddings:%s mode:%s" %
                              (self.name, list(paddings.get_shape()), mode))
  
          self.outputs = tf.pad(self.inputs, paddings=paddings, mode=mode, name=name)
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
  
  ## TimeDistributedLayer
  class TimeDistributedLayer(Layer):
      """
      The :class:`TimeDistributedLayer` class that applies a function to every timestep of the input tensor.
      For example, if using :class:`DenseLayer` as the ``layer_class``, inputs [batch_size , length, dim]
      outputs [batch_size , length, new_dim].
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer, [batch_size , length, dim]
      layer_class : a :class:`Layer` class
      args : dictionary
          The arguments for the ``layer_class``.
      name : a string or None
          An optional name to attach to this layer.
  
      Examples
      --------
      >>> batch_size = 32
      >>> timestep = 20
      >>> input_dim = 100
      >>> x = tf.placeholder(dtype=tf.float32, shape=[batch_size, timestep,  input_dim], name="encode_seqs")
      >>> net = InputLayer(x, name='input')
      >>> net = TimeDistributedLayer(net, layer_class=DenseLayer, args={'n_units':50, 'name':'dense'}, name='time_dense')
      ... [TL] InputLayer  input: (32, 20, 100)
      ... [TL] TimeDistributedLayer time_dense: layer_class:DenseLayer
      >>> print(net.outputs._shape)
      ... (32, 20, 50)
      >>> net.print_params(False)
      ... param   0: (100, 50)          time_dense/dense/W:0
      ... param   1: (50,)              time_dense/dense/b:0
      ... num of params: 5050
      """
      def __init__(
          self,
          layer = None,
          layer_class = None,
          args = {},
          name ='time_distributed',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          print("  [TL] TimeDistributedLayer %s: layer_class:%s args:%s" %
                              (self.name, layer_class.__name__, args))
  
          if not args: args = dict()
          assert isinstance(args, dict), "'args' must be a dict."
  
          if not isinstance(self.inputs, tf.Tensor):
              self.inputs = tf.transpose(tf.stack(self.inputs), [1, 0, 2])
  
          input_shape = self.inputs.get_shape()
  
          timestep = input_shape[1]
          x = tf.unstack(self.inputs, axis=1)
  
          with ops.suppress_stdout():
              for i in range(0, timestep):
                  with tf.variable_scope(name, reuse=(False if i==0 else True)) as vs:
                      set_name_reuse((False if i==0 else True))
                      net = layer_class(InputLayer(x[i], name=args['name']+str(i)), **args)
                      # net = layer_class(InputLayer(x[i], name="input_"+args['name']), **args)
                      x[i] = net.outputs
                      variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
  
          self.outputs = tf.stack(x, axis=1, name=name)
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( variables )
  
  
  
  ## Recurrent layer
  class RNNLayer(Layer):
      """
      The :class:`RNNLayer` class is a RNN layer, you can implement vanilla RNN,
      LSTM and GRU with it.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      cell_fn : a TensorFlow's core RNN cell as follow (Note TF1.0+ and TF1.0- are different).
          - see `RNN Cells in TensorFlow <https://www.tensorflow.org/api_docs/python/>`_
      cell_init_args : a dictionary
          The arguments for the cell initializer.
      n_hidden : a int
          The number of hidden units in the layer.
      initializer : initializer
          The initializer for initializing the parameters.
      n_steps : a int
          The sequence length.
      initial_state : None or RNN State
          If None, initial_state is zero_state.
      return_last : boolean
          - If True, return the last output, "Sequence input and single output"
          - If False, return all outputs, "Synced sequence input and output"
          - In other word, if you want to apply one or more RNN(s) on this layer, set to False.
      return_seq_2d : boolean
          - When return_last = False
          - If True, return 2D Tensor [n_example, n_hidden], for stacking DenseLayer after it.
          - If False, return 3D Tensor [n_example/n_steps, n_steps, n_hidden], for stacking multiple RNN after it.
      name : a string or None
          An optional name to attach to this layer.
  
      Variables
      --------------
      outputs : a tensor
          The output of this RNN.
          return_last = False, outputs = all cell_output, which is the hidden state.
              cell_output.get_shape() = (?, n_hidden)
  
      final_state : a tensor or StateTuple
          When state_is_tuple = False,
          it is the final hidden and cell states, states.get_shape() = [?, 2 * n_hidden].\n
          When state_is_tuple = True, it stores two elements: (c, h), in that order.
          You can get the final state after each iteration during training, then
          feed it to the initial state of next iteration.
  
      initial_state : a tensor or StateTuple
          It is the initial state of this RNN layer, you can use it to initialize
          your state at the begining of each epoch or iteration according to your
          training procedure.
  
      batch_size : int or tensor
          Is int, if able to compute the batch_size, otherwise, tensor for ``?``.
  
      Examples
      --------
      - For words
      >>> input_data = tf.placeholder(tf.int32, [batch_size, num_steps])
      >>> network = tl.layers.EmbeddingInputlayer(
      ...                 inputs = input_data,
      ...                 vocabulary_size = vocab_size,
      ...                 embedding_size = hidden_size,
      ...                 E_init = tf.random_uniform_initializer(-init_scale, init_scale),
      ...                 name ='embedding_layer')
      >>> if is_training:
      >>>     network = tl.layers.DropoutLayer(network, keep=keep_prob, name='drop1')
      >>> network = tl.layers.RNNLayer(network,
      ...             cell_fn=tf.nn.rnn_cell.BasicLSTMCell,
      ...             cell_init_args={'forget_bias': 0.0},# 'state_is_tuple': True},
      ...             n_hidden=hidden_size,
      ...             initializer=tf.random_uniform_initializer(-init_scale, init_scale),
      ...             n_steps=num_steps,
      ...             return_last=False,
      ...             name='basic_lstm_layer1')
      >>> lstm1 = network
      >>> if is_training:
      >>>     network = tl.layers.DropoutLayer(network, keep=keep_prob, name='drop2')
      >>> network = tl.layers.RNNLayer(network,
      ...             cell_fn=tf.nn.rnn_cell.BasicLSTMCell,
      ...             cell_init_args={'forget_bias': 0.0}, # 'state_is_tuple': True},
      ...             n_hidden=hidden_size,
      ...             initializer=tf.random_uniform_initializer(-init_scale, init_scale),
      ...             n_steps=num_steps,
      ...             return_last=False,
      ...             return_seq_2d=True,
      ...             name='basic_lstm_layer2')
      >>> lstm2 = network
      >>> if is_training:
      >>>     network = tl.layers.DropoutLayer(network, keep=keep_prob, name='drop3')
      >>> network = tl.layers.DenseLayer(network,
      ...             n_units=vocab_size,
      ...             W_init=tf.random_uniform_initializer(-init_scale, init_scale),
      ...             b_init=tf.random_uniform_initializer(-init_scale, init_scale),
      ...             act = tl.activation.identity, name='output_layer')
  
      - For CNN+LSTM
      >>> x = tf.placeholder(tf.float32, shape=[batch_size, image_size, image_size, 1])
      >>> network = tl.layers.InputLayer(x, name='input_layer')
      >>> network = tl.layers.Conv2dLayer(network,
      ...                         act = tf.nn.relu,
      ...                         shape = [5, 5, 1, 32],  # 32 features for each 5x5 patch
      ...                         strides=[1, 2, 2, 1],
      ...                         padding='SAME',
      ...                         name ='cnn_layer1')
      >>> network = tl.layers.PoolLayer(network,
      ...                         ksize=[1, 2, 2, 1],
      ...                         strides=[1, 2, 2, 1],
      ...                         padding='SAME',
      ...                         pool = tf.nn.max_pool,
      ...                         name ='pool_layer1')
      >>> network = tl.layers.Conv2dLayer(network,
      ...                         act = tf.nn.relu,
      ...                         shape = [5, 5, 32, 10], # 10 features for each 5x5 patch
      ...                         strides=[1, 2, 2, 1],
      ...                         padding='SAME',
      ...                         name ='cnn_layer2')
      >>> network = tl.layers.PoolLayer(network,
      ...                         ksize=[1, 2, 2, 1],
      ...                         strides=[1, 2, 2, 1],
      ...                         padding='SAME',
      ...                         pool = tf.nn.max_pool,
      ...                         name ='pool_layer2')
      >>> network = tl.layers.FlattenLayer(network, name='flatten_layer')
      >>> network = tl.layers.ReshapeLayer(network, shape=[-1, num_steps, int(network.outputs._shape[-1])])
      >>> rnn1 = tl.layers.RNNLayer(network,
      ...                         cell_fn=tf.nn.rnn_cell.LSTMCell,
      ...                         cell_init_args={},
      ...                         n_hidden=200,
      ...                         initializer=tf.random_uniform_initializer(-0.1, 0.1),
      ...                         n_steps=num_steps,
      ...                         return_last=False,
      ...                         return_seq_2d=True,
      ...                         name='rnn_layer')
      >>> network = tl.layers.DenseLayer(rnn1, n_units=3,
      ...                         act = tl.activation.identity, name='output_layer')
  
      Notes
      -----
      Input dimension should be rank 3 : [batch_size, n_steps, n_features], if no, please see :class:`ReshapeLayer`.
  
      References
      ----------
      - `Neural Network RNN Cells in TensorFlow <https://www.tensorflow.org/api_docs/python/rnn_cell/>`_
      - `tensorflow/python/ops/rnn.py <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/rnn.py>`_
      - `tensorflow/python/ops/rnn_cell.py <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/rnn_cell.py>`_
      - see TensorFlow tutorial ``ptb_word_lm.py``, TensorLayer tutorials ``tutorial_ptb_lstm*.py`` and ``tutorial_generate_text.py``
      """
      def __init__(
          self,
          layer = None,
          cell_fn = None,#tf.nn.rnn_cell.BasicRNNCell,
          cell_init_args = {},
          n_hidden = 100,
          initializer = tf.random_uniform_initializer(-0.1, 0.1),
          n_steps = 5,
          initial_state = None,
          return_last = False,
          # is_reshape = True,
          return_seq_2d = False,
          name = 'rnn_layer',
      ):
          Layer.__init__(self, name=name)
          if cell_fn is None:
              raise Exception("Please put in cell_fn")
          if 'GRU' in cell_fn.__name__:
              try:
                  cell_init_args.pop('state_is_tuple')
              except:
                  pass
  
          self.inputs = layer.outputs
  
          print("  [TL] RNNLayer %s: n_hidden:%d n_steps:%d in_dim:%d in_shape:%s cell_fn:%s " % (self.name, n_hidden,
              n_steps, self.inputs.get_shape().ndims, self.inputs.get_shape(), cell_fn.__name__))
          # You can get the dimension by .get_shape() or ._shape, and check the
          # dimension by .with_rank() as follow.
          # self.inputs.get_shape().with_rank(2)
          # self.inputs.get_shape().with_rank(3)
  
          # Input dimension should be rank 3 [batch_size, n_steps(max), n_features]
          try:
              self.inputs.get_shape().with_rank(3)
          except:
              raise Exception("RNN : Input dimension should be rank 3 : [batch_size, n_steps, n_features]")
  
  
          # is_reshape : boolean (deprecate)
          #     Reshape the inputs to 3 dimension tensor.\n
          #     If input is［batch_size, n_steps, n_features], we do not need to reshape it.\n
          #     If input is [batch_size * n_steps, n_features], we need to reshape it.
          # if is_reshape:
          #     self.inputs = tf.reshape(self.inputs, shape=[-1, n_steps, int(self.inputs._shape[-1])])
  
          fixed_batch_size = self.inputs.get_shape().with_rank_at_least(1)[0]
  
          if fixed_batch_size.value:
              batch_size = fixed_batch_size.value
              print("       RNN batch_size (concurrent processes): %d" % batch_size)
          else:
              from tensorflow.python.ops import array_ops
              batch_size = array_ops.shape(self.inputs)[0]
              print("       non specified batch_size, uses a tensor instead.")
          self.batch_size = batch_size
  
          # Simplified version of tensorflow.models.rnn.rnn.py's rnn().
          # This builds an unrolled LSTM for tutorial purposes only.
          # In general, use the rnn() or state_saving_rnn() from rnn.py.
          #
          # The alternative version of the code below is:
          #
          # from tensorflow.models.rnn import rnn
          # inputs = [tf.squeeze(input_, [1])
          #           for input_ in tf.split(1, num_steps, inputs)]
          # outputs, state = rnn.rnn(cell, inputs, initial_state=self._initial_state)
          outputs = []
          self.cell = cell = cell_fn(num_units=n_hidden, **cell_init_args)
          if initial_state is None:
              self.initial_state = cell.zero_state(batch_size, dtype=tf.float32)  # 1.2.3
          state = self.initial_state
          # with tf.variable_scope("model", reuse=None, initializer=initializer):
          with tf.variable_scope(name, initializer=initializer) as vs:
              for time_step in range(n_steps):
                  if time_step > 0: tf.get_variable_scope().reuse_variables()
                  (cell_output, state) = cell(self.inputs[:, time_step, :], state)
                  outputs.append(cell_output)
  
              # Retrieve just the RNN variables.
              # rnn_variables = [v for v in tf.all_variables() if v.name.startswith(vs.name)]
              rnn_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
  
          print("     n_params : %d" % (len(rnn_variables)))
  
          if return_last:
              # 2D Tensor [batch_size, n_hidden]
              self.outputs = outputs[-1]
          else:
              if return_seq_2d:
                  # PTB tutorial: stack dense layer after that, or compute the cost from the output
                  # 2D Tensor [n_example, n_hidden]
                  try: # TF1.0
                      self.outputs = tf.reshape(tf.concat(outputs, 1), [-1, n_hidden])
                  except: # TF0.12
                      self.outputs = tf.reshape(tf.concat(1, outputs), [-1, n_hidden])
  
  
              else:
                  # <akara>: stack more RNN layer after that
                  # 3D Tensor [n_example/n_steps, n_steps, n_hidden]
                  try: # TF1.0
                      self.outputs = tf.reshape(tf.concat(outputs, 1), [-1, n_steps, n_hidden])
                  except: # TF0.12
                      self.outputs = tf.reshape(tf.concat(1, outputs), [-1, n_steps, n_hidden])
  
          self.final_state = state
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          # print(type(self.outputs))
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( rnn_variables )
  
  class BiRNNLayer(Layer):
      """
      The :class:`BiRNNLayer` class is a Bidirectional RNN layer.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      cell_fn : a TensorFlow's core RNN cell as follow (Note TF1.0+ and TF1.0- are different).
          - see `RNN Cells in TensorFlow <https://www.tensorflow.org/api_docs/python/>`_
      cell_init_args : a dictionary
          The arguments for the cell initializer.
      n_hidden : a int
          The number of hidden units in the layer.
      initializer : initializer
          The initializer for initializing the parameters.
      n_steps : a int
          The sequence length.
      fw_initial_state : None or forward RNN State
          If None, initial_state is zero_state.
      bw_initial_state : None or backward RNN State
          If None, initial_state is zero_state.
      dropout : `tuple` of `float`: (input_keep_prob, output_keep_prob).
          The input and output keep probability.
      n_layer : a int, default is 1.
          The number of RNN layers.
      return_last : boolean
          - If True, return the last output, "Sequence input and single output"
          - If False, return all outputs, "Synced sequence input and output"
          - In other word, if you want to apply one or more RNN(s) on this layer, set to False.
      return_seq_2d : boolean
          - When return_last = False
          - If True, return 2D Tensor [n_example, n_hidden], for stacking DenseLayer after it.
          - If False, return 3D Tensor [n_example/n_steps, n_steps, n_hidden], for stacking multiple RNN after it.
      name : a string or None
          An optional name to attach to this layer.
  
      Variables
      --------------
      outputs : a tensor
          The output of this RNN.
          return_last = False, outputs = all cell_output, which is the hidden state.
              cell_output.get_shape() = (?, n_hidden)
  
      fw(bw)_final_state : a tensor or StateTuple
          When state_is_tuple = False,
          it is the final hidden and cell states, states.get_shape() = [?, 2 * n_hidden].\n
          When state_is_tuple = True, it stores two elements: (c, h), in that order.
          You can get the final state after each iteration during training, then
          feed it to the initial state of next iteration.
  
      fw(bw)_initial_state : a tensor or StateTuple
          It is the initial state of this RNN layer, you can use it to initialize
          your state at the begining of each epoch or iteration according to your
          training procedure.
  
      batch_size : int or tensor
          Is int, if able to compute the batch_size, otherwise, tensor for ``?``.
  
      Notes
      -----
      - Input dimension should be rank 3 : [batch_size, n_steps, n_features], if no, please see :class:`ReshapeLayer`.
      - For predicting, the sequence length has to be the same with the sequence length of training, while, for normal
      RNN, we can use sequence length of 1 for predicting.
  
      References
      ----------
      - `Source <https://github.com/akaraspt/deepsleep/blob/master/deepsleep/model.py>`_
      """
      def __init__(
          self,
          layer = None,
          cell_fn = None, #tf.nn.rnn_cell.LSTMCell,
          cell_init_args = {'use_peepholes':True, 'state_is_tuple':True},
          n_hidden = 100,
          initializer = tf.random_uniform_initializer(-0.1, 0.1),
          n_steps = 5,
          fw_initial_state = None,
          bw_initial_state = None,
          dropout = None,
          n_layer = 1,
          return_last = False,
          return_seq_2d = False,
          name = 'birnn_layer',
      ):
          Layer.__init__(self, name=name)
          if cell_fn is None:
              raise Exception("Please put in cell_fn")
          if 'GRU' in cell_fn.__name__:
              try:
                  cell_init_args.pop('state_is_tuple')
              except:
                  pass
  
          self.inputs = layer.outputs
  
          print("  [TL] BiRNNLayer %s: n_hidden:%d n_steps:%d in_dim:%d in_shape:%s cell_fn:%s dropout:%s n_layer:%d " % (self.name, n_hidden,
              n_steps, self.inputs.get_shape().ndims, self.inputs.get_shape(), cell_fn.__name__, dropout, n_layer))
  
          fixed_batch_size = self.inputs.get_shape().with_rank_at_least(1)[0]
  
          if fixed_batch_size.value:
              self.batch_size = fixed_batch_size.value
              print("       RNN batch_size (concurrent processes): %d" % self.batch_size)
          else:
              from tensorflow.python.ops import array_ops
              self.batch_size = array_ops.shape(self.inputs)[0]
              print("       non specified batch_size, uses a tensor instead.")
  
          # Input dimension should be rank 3 [batch_size, n_steps(max), n_features]
          try:
              self.inputs.get_shape().with_rank(3)
          except:
              raise Exception("RNN : Input dimension should be rank 3 : [batch_size, n_steps, n_features]")
  
          with tf.variable_scope(name, initializer=initializer) as vs:
              self.fw_cell = cell_fn(num_units=n_hidden, **cell_init_args)
              self.bw_cell = cell_fn(num_units=n_hidden, **cell_init_args)
              # Apply dropout
              if dropout:
                  if type(dropout) in [tuple, list]:
                      in_keep_prob = dropout[0]
                      out_keep_prob = dropout[1]
                  elif isinstance(dropout, float):
                      in_keep_prob, out_keep_prob = dropout, dropout
                  else:
                      raise Exception("Invalid dropout type (must be a 2-D tuple of "
                                      "float)")
                  try: # TF 1.0
                      DropoutWrapper_fn = tf.contrib.rnn.DropoutWrapper
                  except:
                      DropoutWrapper_fn = tf.nn.rnn_cell.DropoutWrapper
                  self.fw_cell = DropoutWrapper_fn(
                            self.fw_cell,
                            input_keep_prob=in_keep_prob,
                            output_keep_prob=out_keep_prob)
                  self.bw_cell = DropoutWrapper_fn(
                            self.bw_cell,
                            input_keep_prob=in_keep_prob,
                            output_keep_prob=out_keep_prob)
              # Apply multiple layers
              if n_layer > 1:
                  try: # TF1.0
                      MultiRNNCell_fn = tf.contrib.rnn.MultiRNNCell
                  except:
                      MultiRNNCell_fn = tf.nn.rnn_cell.MultiRNNCell
  
                  try:
                      self.fw_cell = MultiRNNCell_fn([self.fw_cell] * n_layer,
                                                            state_is_tuple=True)
                      self.bw_cell = MultiRNNCell_fn([self.bw_cell] * n_layer,
                                                            state_is_tuple=True)
                  except:
                      self.fw_cell = MultiRNNCell_fn([self.fw_cell] * n_layer)
                      self.bw_cell = MultiRNNCell_fn([self.bw_cell] * n_layer)
  
              # Initial state of RNN
              if fw_initial_state is None:
                  self.fw_initial_state = self.fw_cell.zero_state(self.batch_size, dtype=tf.float32)
              else:
                  self.fw_initial_state = fw_initial_state
              if bw_initial_state is None:
                  self.bw_initial_state = self.bw_cell.zero_state(self.batch_size, dtype=tf.float32)
              else:
                  self.bw_initial_state = bw_initial_state
              # exit()
              # Feedforward to MultiRNNCell
              try: ## TF1.0
                  list_rnn_inputs = tf.unstack(self.inputs, axis=1)
              except: ## TF0.12
                  list_rnn_inputs = tf.unpack(self.inputs, axis=1)
  
              try: # TF1.0
                  bidirectional_rnn_fn = tf.contrib.rnn.static_bidirectional_rnn
              except:
                  bidirectional_rnn_fn = tf.nn.bidirectional_rnn
              outputs, fw_state, bw_state = bidirectional_rnn_fn(               # outputs, fw_state, bw_state = tf.contrib.rnn.static_bidirectional_rnn(
                  cell_fw=self.fw_cell,
                  cell_bw=self.bw_cell,
                  inputs=list_rnn_inputs,
                  initial_state_fw=self.fw_initial_state,
                  initial_state_bw=self.bw_initial_state
              )
  
              if return_last:
                  self.outputs = outputs[-1]
              else:
                  self.outputs = outputs
                  if return_seq_2d:
                      # 2D Tensor [n_example, n_hidden]
                      try: # TF1.0
                          self.outputs = tf.reshape(tf.concat(outputs, 1), [-1, n_hidden*2])
                      except: # TF0.12
                          self.outputs = tf.reshape(tf.concat(1, outputs), [-1, n_hidden*2])
                  else:
                      # <akara>: stack more RNN layer after that
                      # 3D Tensor [n_example/n_steps, n_steps, n_hidden]
  
                      try: # TF1.0
                          self.outputs = tf.reshape(tf.concat(outputs,1), [-1, n_steps, n_hidden*2])
                      except: # TF0.12
                          self.outputs = tf.reshape(tf.concat(1, outputs), [-1, n_steps, n_hidden*2])
              self.fw_final_state = fw_state
              self.bw_final_state = bw_state
  
              # Retrieve just the RNN variables.
              rnn_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
  
          print("     n_params : %d" % (len(rnn_variables)))
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( rnn_variables )
  
  # Advanced Ops for Dynamic RNN
  def advanced_indexing_op(input, index):
      """Advanced Indexing for Sequences, returns the outputs by given sequence lengths.
      When return the last output :class:`DynamicRNNLayer` uses it to get the last outputs with the sequence lengths.
  
      Parameters
      -----------
      input : tensor for data
          [batch_size, n_step(max), n_features]
      index : tensor for indexing, i.e. sequence_length in Dynamic RNN.
          [batch_size]
  
      Examples
      ---------
      >>> batch_size, max_length, n_features = 3, 5, 2
      >>> z = np.random.uniform(low=-1, high=1, size=[batch_size, max_length, n_features]).astype(np.float32)
      >>> b_z = tf.constant(z)
      >>> sl = tf.placeholder(dtype=tf.int32, shape=[batch_size])
      >>> o = advanced_indexing_op(b_z, sl)
      >>>
      >>> sess = tf.InteractiveSession()
      >>> tl.layers.initialize_global_variables(sess)
      >>>
      >>> order = np.asarray([1,1,2])
      >>> print("real",z[0][order[0]-1], z[1][order[1]-1], z[2][order[2]-1])
      >>> y = sess.run([o], feed_dict={sl:order})
      >>> print("given",order)
      >>> print("out", y)
      ... real [-0.93021595  0.53820813] [-0.92548317 -0.77135968] [ 0.89952248  0.19149846]
      ... given [1 1 2]
      ... out [array([[-0.93021595,  0.53820813],
      ...             [-0.92548317, -0.77135968],
      ...             [ 0.89952248,  0.19149846]], dtype=float32)]
  
      References
      -----------
      - Modified from TFlearn (the original code is used for fixed length rnn), `references <https://github.com/tflearn/tflearn/blob/master/tflearn/layers/recurrent.py>`_.
      """
      batch_size = tf.shape(input)[0]
      # max_length = int(input.get_shape()[1])    # for fixed length rnn, length is given
      max_length = tf.shape(input)[1]             # for dynamic_rnn, length is unknown
      dim_size = int(input.get_shape()[2])
      index = tf.range(0, batch_size) * max_length + (index - 1)
      flat = tf.reshape(input, [-1, dim_size])
      relevant = tf.gather(flat, index)
      return relevant
  
  def retrieve_seq_length_op(data):
      """An op to compute the length of a sequence from input shape of [batch_size, n_step(max), n_features],
      it can be used when the features of padding (on right hand side) are all zeros.
  
      Parameters
      -----------
      data : tensor
          [batch_size, n_step(max), n_features] with zero padding on right hand side.
  
      Examples
      ---------
      >>> data = [[[1],[2],[0],[0],[0]],
      ...         [[1],[2],[3],[0],[0]],
      ...         [[1],[2],[6],[1],[0]]]
      >>> data = np.asarray(data)
      >>> print(data.shape)
      ... (3, 5, 1)
      >>> data = tf.constant(data)
      >>> sl = retrieve_seq_length_op(data)
      >>> sess = tf.InteractiveSession()
      >>> tl.layers.initialize_global_variables(sess)
      >>> y = sl.eval()
      ... [2 3 4]
  
      - Multiple features
      >>> data = [[[1,2],[2,2],[1,2],[1,2],[0,0]],
      ...         [[2,3],[2,4],[3,2],[0,0],[0,0]],
      ...         [[3,3],[2,2],[5,3],[1,2],[0,0]]]
      >>> sl
      ... [4 3 4]
  
      References
      ------------
      - Borrow from `TFlearn <https://github.com/tflearn/tflearn/blob/master/tflearn/layers/recurrent.py>`_.
      """
      with tf.name_scope('GetLength'):
          ## TF 1.0 change reduction_indices to axis
          used = tf.sign(tf.reduce_max(tf.abs(data), 2))
          length = tf.reduce_sum(used, 1)
          ## TF < 1.0
          # used = tf.sign(tf.reduce_max(tf.abs(data), reduction_indices=2))
          # length = tf.reduce_sum(used, reduction_indices=1)
          length = tf.cast(length, tf.int32)
      return length
  
  def retrieve_seq_length_op2(data):
      """An op to compute the length of a sequence, from input shape of [batch_size, n_step(max)],
      it can be used when the features of padding (on right hand side) are all zeros.
  
      Parameters
      -----------
      data : tensor
          [batch_size, n_step(max)] with zero padding on right hand side.
  
      Examples
      --------
      >>> data = [[1,2,0,0,0],
      ...         [1,2,3,0,0],
      ...         [1,2,6,1,0]]
      >>> o = retrieve_seq_length_op2(data)
      >>> sess = tf.InteractiveSession()
      >>> tl.layers.initialize_global_variables(sess)
      >>> print(o.eval())
      ... [2 3 4]
      """
      return tf.reduce_sum(tf.cast(tf.greater(data, tf.zeros_like(data)), tf.int32), 1)
  
  
  def retrieve_seq_length_op3(data, pad_val=0):
      data_shape_size = data.get_shape().ndims
      if data_shape_size == 3:
          return tf.reduce_sum(tf.cast(tf.reduce_any(tf.not_equal(data, pad_val), axis=2), dtype=tf.int32), 1)
      elif data_shape_size == 2:
          return tf.reduce_sum(tf.cast(tf.not_equal(data, pad_val), dtype=tf.int32), 1)
      elif data_shape_size == 1:
          raise ValueError("retrieve_seq_length_op3: data has wrong shape!")
      else:
          raise ValueError("retrieve_seq_length_op3: handling data_shape_size %s hasn't been implemented!" % (data_shape_size))
  
  
  def target_mask_op(data, pad_val=0):
      data_shape_size = data.get_shape().ndims
      if data_shape_size == 3:
          return tf.cast(tf.reduce_any(tf.not_equal(data, pad_val), axis=2), dtype=tf.int32)
      elif data_shape_size == 2:
          return tf.cast(tf.not_equal(data, pad_val), dtype=tf.int32)
      elif data_shape_size == 1:
          raise ValueError("target_mask_op: data has wrong shape!")
      else:
          raise ValueError("target_mask_op: handling data_shape_size %s hasn't been implemented!" % (data_shape_size))
  
  
  # Dynamic RNN
  class DynamicRNNLayer(Layer):
      """
      The :class:`DynamicRNNLayer` class is a Dynamic RNN layer, see ``tf.nn.dynamic_rnn``.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      cell_fn : a TensorFlow's core RNN cell as follow (Note TF1.0+ and TF1.0- are different).
          - see `RNN Cells in TensorFlow <https://www.tensorflow.org/api_docs/python/>`_
      cell_init_args : a dictionary
          The arguments for the cell initializer.
      n_hidden : a int
          The number of hidden units in the layer.
      initializer : initializer
          The initializer for initializing the parameters.
      sequence_length : a tensor, array or None. The sequence length of each row of input data, see ``Advanced Ops for Dynamic RNN``.
          - If None, it uses ``retrieve_seq_length_op`` to compute the sequence_length, i.e. when the features of padding (on right hand side) are all zeros.
          - If using word embedding, you may need to compute the sequence_length from the ID array (the integer features before word embedding) by using ``retrieve_seq_length_op2`` or ``retrieve_seq_length_op``.
          - You can also input an numpy array.
          - More details about TensorFlow dynamic_rnn in `Wild-ML Blog <http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/>`_.
      initial_state : None or RNN State
          If None, initial_state is zero_state.
      dropout : `tuple` of `float`: (input_keep_prob, output_keep_prob).
          The input and output keep probability.
      n_layer : a int, default is 1.
          The number of RNN layers.
      return_last : boolean
          - If True, return the last output, "Sequence input and single output"
          - If False, return all outputs, "Synced sequence input and output"
          - In other word, if you want to apply one or more RNN(s) on this layer, set to False.
      return_seq_2d : boolean
          - When return_last = False
          - If True, return 2D Tensor [n_example, n_hidden], for stacking DenseLayer or computing cost after it.
          - If False, return 3D Tensor [n_example/n_steps(max), n_steps(max), n_hidden], for stacking multiple RNN after it.
      name : a string or None
          An optional name to attach to this layer.
  
      Variables
      ------------
      outputs : a tensor
          The output of this RNN.
          return_last = False, outputs = all cell_output, which is the hidden state.
              cell_output.get_shape() = (?, n_hidden)
  
      final_state : a tensor or StateTuple
          When state_is_tuple = False,
          it is the final hidden and cell states, states.get_shape() = [?, 2 * n_hidden].\n
          When state_is_tuple = True, it stores two elements: (c, h), in that order.
          You can get the final state after each iteration during training, then
          feed it to the initial state of next iteration.
  
      initial_state : a tensor or StateTuple
          It is the initial state of this RNN layer, you can use it to initialize
          your state at the begining of each epoch or iteration according to your
          training procedure.
  
      sequence_length : a tensor or array, shape = [batch_size]
          The sequence lengths computed by Advanced Opt or the given sequence lengths.
  
      Notes
      -----
      Input dimension should be rank 3 : [batch_size, n_steps(max), n_features], if no, please see :class:`ReshapeLayer`.
  
      Examples
      --------
      >>> input_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="input_seqs")
      >>> network = tl.layers.EmbeddingInputlayer(
      ...             inputs = input_seqs,
      ...             vocabulary_size = vocab_size,
      ...             embedding_size = embedding_size,
      ...             name = 'seq_embedding')
      >>> network = tl.layers.DynamicRNNLayer(network,
      ...             cell_fn = tf.contrib.rnn.BasicLSTMCell, # for TF0.2 tf.nn.rnn_cell.BasicLSTMCell,
      ...             n_hidden = embedding_size,
      ...             dropout = 0.7,
      ...             sequence_length = tl.layers.retrieve_seq_length_op2(input_seqs),
      ...             return_seq_2d = True,     # stack denselayer or compute cost after it
      ...             name = 'dynamic_rnn')
      ... network = tl.layers.DenseLayer(network, n_units=vocab_size,
      ...             act=tf.identity, name="output")
  
      References
      ----------
      - `Wild-ML Blog <http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/>`_
      - `dynamic_rnn.ipynb <https://github.com/dennybritz/tf-rnn/blob/master/dynamic_rnn.ipynb>`_
      - `tf.nn.dynamic_rnn <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.dynamic_rnn.md>`_
      - `tflearn rnn <https://github.com/tflearn/tflearn/blob/master/tflearn/layers/recurrent.py>`_
      - ``tutorial_dynamic_rnn.py``
      """
      def __init__(
          self,
          layer = None,
          cell_fn = None,#tf.nn.rnn_cell.LSTMCell,
          cell_init_args = {'state_is_tuple' : True},
          n_hidden = 256,
          initializer = tf.random_uniform_initializer(-0.1, 0.1),
          sequence_length = None,
          initial_state = None,
          dropout = None,
          n_layer = 1,
          return_last = False,
          return_seq_2d = False,
          dynamic_rnn_init_args={},
          name = 'dyrnn_layer',
      ):
          Layer.__init__(self, name=name)
          if cell_fn is None:
              raise Exception("Please put in cell_fn")
          if 'GRU' in cell_fn.__name__:
              try:
                  cell_init_args.pop('state_is_tuple')
              except:
                  pass
          self.inputs = layer.outputs
  
          print("  [TL] DynamicRNNLayer %s: n_hidden:%d, in_dim:%d in_shape:%s cell_fn:%s dropout:%s n_layer:%d" % (self.name, n_hidden,
               self.inputs.get_shape().ndims, self.inputs.get_shape(), cell_fn.__name__, dropout, n_layer))
  
          # Input dimension should be rank 3 [batch_size, n_steps(max), n_features]
          try:
              self.inputs.get_shape().with_rank(3)
          except:
              raise Exception("RNN : Input dimension should be rank 3 : [batch_size, n_steps(max), n_features]")
  
          # Get the batch_size
          fixed_batch_size = self.inputs.get_shape().with_rank_at_least(1)[0]
          if fixed_batch_size:
              batch_size = fixed_batch_size
              print("       batch_size (concurrent processes): %d" % batch_size)
          else:
              from tensorflow.python.ops import array_ops
              batch_size = array_ops.shape(self.inputs)[0]
              print("       non specified batch_size, uses a tensor instead.")
          self.batch_size = batch_size
  
          # Creats the cell function
          # cell_instance_fn=lambda: cell_fn(num_units=n_hidden, **cell_init_args) # HanSheng
          self.cell = cell_fn(num_units=n_hidden, **cell_init_args)
  
          # Apply dropout
          if dropout:
              if type(dropout) in [tuple, list]:
                  in_keep_prob = dropout[0]
                  out_keep_prob = dropout[1]
              elif isinstance(dropout, float):
                  in_keep_prob, out_keep_prob = dropout, dropout
              else:
                  raise Exception("Invalid dropout type (must be a 2-D tuple of "
                                  "float)")
              try: # TF1.0
                  DropoutWrapper_fn = tf.contrib.rnn.DropoutWrapper
              except:
                  DropoutWrapper_fn = tf.nn.rnn_cell.DropoutWrapper
  
              # cell_instance_fn1=cell_instance_fn        # HanSheng
              # cell_instance_fn=DropoutWrapper_fn(
              #                     cell_instance_fn1(),
              #                     input_keep_prob=in_keep_prob,
              #                     output_keep_prob=out_keep_prob)
              self.cell = DropoutWrapper_fn(self.cell,
                        input_keep_prob=in_keep_prob, output_keep_prob=1.0)#out_keep_prob)
  
          # Apply multiple layers
          if n_layer > 1:
              try:
                  MultiRNNCell_fn = tf.contrib.rnn.MultiRNNCell
              except:
                  MultiRNNCell_fn = tf.nn.rnn_cell.MultiRNNCell
  
              # cell_instance_fn2=cell_instance_fn # HanSheng
              try:
                  # cell_instance_fn=lambda: MultiRNNCell_fn([cell_instance_fn2() for _ in range(n_layer)], state_is_tuple=True) # HanSheng
                  self.cell = MultiRNNCell_fn([self.cell] * n_layer, state_is_tuple=True)
              except: # when GRU
                  # cell_instance_fn=lambda: MultiRNNCell_fn([cell_instance_fn2() for _ in range(n_layer)]) # HanSheng
                  self.cell = MultiRNNCell_fn([self.cell] * n_layer)
  
          if dropout:
              self.cell = DropoutWrapper_fn(self.cell,
                        input_keep_prob=1.0, output_keep_prob=out_keep_prob)
  
          # self.cell=cell_instance_fn() # HanSheng
  
          # Initialize initial_state
          if initial_state is None:
              self.initial_state = self.cell.zero_state(batch_size, dtype=tf.float32)
          else:
              self.initial_state = initial_state
  
          # Computes sequence_length
          if sequence_length is None:
              try: ## TF1.0
                  sequence_length = retrieve_seq_length_op(
                              self.inputs if isinstance(self.inputs, tf.Tensor) else tf.stack(self.inputs))
              except: ## TF0.12
                  sequence_length = retrieve_seq_length_op(
                              self.inputs if isinstance(self.inputs, tf.Tensor) else tf.pack(self.inputs))
  
          # Main - Computes outputs and last_states
          with tf.variable_scope(name, initializer=initializer) as vs:
              outputs, last_states = tf.nn.dynamic_rnn(
                  cell=self.cell,
                  # inputs=X
                  inputs = self.inputs,
                  # dtype=tf.float64,
                  sequence_length=sequence_length,
                  initial_state = self.initial_state,
                  **dynamic_rnn_init_args
                  )
              rnn_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
  
              # print("     n_params : %d" % (len(rnn_variables)))
              # Manage the outputs
              if return_last:
                  # [batch_size, n_hidden]
                  # outputs = tf.transpose(tf.pack(outputs), [1, 0, 2]) # TF1.0 tf.pack --> tf.stack
                  self.outputs = advanced_indexing_op(outputs, sequence_length)
              else:
                  # [batch_size, n_step(max), n_hidden]
                  # self.outputs = result[0]["outputs"]
                  # self.outputs = outputs    # it is 3d, but it is a list
                  if return_seq_2d:
                      # PTB tutorial:
                      # 2D Tensor [n_example, n_hidden]
                      try: # TF1.0
                          self.outputs = tf.reshape(tf.concat(outputs, 1), [-1, n_hidden])
                      except: # TF0.12
                          self.outputs = tf.reshape(tf.concat(1, outputs), [-1, n_hidden])
                  else:
                      # <akara>:
                      # 3D Tensor [batch_size, n_steps(max), n_hidden]
                      max_length = tf.shape(outputs)[1]
                      batch_size = tf.shape(outputs)[0]
  
  
                      try: # TF1.0
                          self.outputs = tf.reshape(tf.concat(outputs, 1), [batch_size, max_length, n_hidden])
                      except: # TF0.12
                          self.outputs = tf.reshape(tf.concat(1, outputs), [batch_size, max_length, n_hidden])
                      # self.outputs = tf.reshape(tf.concat(1, outputs), [-1, max_length, n_hidden])
  
          # Final state
          self.final_state = last_states
  
          self.sequence_length = sequence_length
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
  
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( rnn_variables )
  
  # Bidirectional Dynamic RNN
  class BiDynamicRNNLayer(Layer):
      """
      The :class:`BiDynamicRNNLayer` class is a RNN layer, you can implement vanilla RNN,
      LSTM and GRU with it.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      cell_fn : a TensorFlow's core RNN cell as follow (Note TF1.0+ and TF1.0- are different).
          - see `RNN Cells in TensorFlow <https://www.tensorflow.org/api_docs/python/>`_
      cell_init_args : a dictionary
          The arguments for the cell initializer.
      n_hidden : a int
          The number of hidden units in the layer.
      initializer : initializer
          The initializer for initializing the parameters.
      sequence_length : a tensor, array or None
          The sequence length of each row of input data, see ``Advanced Ops for Dynamic RNN``.
              - If None, it uses ``retrieve_seq_length_op`` to compute the sequence_length, i.e. when the features of padding (on right hand side) are all zeros.
              - If using word embedding, you may need to compute the sequence_length from the ID array (the integer features before word embedding) by using ``retrieve_seq_length_op2`` or ``retrieve_seq_length_op``.
              - You can also input an numpy array.
              - More details about TensorFlow dynamic_rnn in `Wild-ML Blog <http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/>`_.
      fw_initial_state : None or forward RNN State
          If None, initial_state is zero_state.
      bw_initial_state : None or backward RNN State
          If None, initial_state is zero_state.
      dropout : `tuple` of `float`: (input_keep_prob, output_keep_prob).
          The input and output keep probability.
      n_layer : a int, default is 1.
          The number of RNN layers.
      return_last : boolean
          If True, return the last output, "Sequence input and single output"\n
          If False, return all outputs, "Synced sequence input and output"\n
          In other word, if you want to apply one or more RNN(s) on this layer, set to False.
      return_seq_2d : boolean
          - When return_last = False
          - If True, return 2D Tensor [n_example, 2 * n_hidden], for stacking DenseLayer or computing cost after it.
          - If False, return 3D Tensor [n_example/n_steps(max), n_steps(max), 2 * n_hidden], for stacking multiple RNN after it.
      name : a string or None
          An optional name to attach to this layer.
  
      Variables
      -----------------------
      outputs : a tensor
          The output of this RNN.
          return_last = False, outputs = all cell_output, which is the hidden state.
              cell_output.get_shape() = (?, 2 * n_hidden)
  
      fw(bw)_final_state : a tensor or StateTuple
          When state_is_tuple = False,
          it is the final hidden and cell states, states.get_shape() = [?, 2 * n_hidden].\n
          When state_is_tuple = True, it stores two elements: (c, h), in that order.
          You can get the final state after each iteration during training, then
          feed it to the initial state of next iteration.
  
      fw(bw)_initial_state : a tensor or StateTuple
          It is the initial state of this RNN layer, you can use it to initialize
          your state at the begining of each epoch or iteration according to your
          training procedure.
  
      sequence_length : a tensor or array, shape = [batch_size]
          The sequence lengths computed by Advanced Opt or the given sequence lengths.
  
      Notes
      -----
      Input dimension should be rank 3 : [batch_size, n_steps(max), n_features], if no, please see :class:`ReshapeLayer`.
  
  
      References
      ----------
      - `Wild-ML Blog <http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/>`_
      - `bidirectional_rnn.ipynb <https://github.com/dennybritz/tf-rnn/blob/master/bidirectional_rnn.ipynb>`_
      """
      def __init__(
          self,
          layer = None,
          cell_fn = None,#tf.nn.rnn_cell.LSTMCell,
          cell_init_args = {'state_is_tuple':True},
          n_hidden = 256,
          initializer = tf.random_uniform_initializer(-0.1, 0.1),
          sequence_length = None,
          fw_initial_state = None,
          bw_initial_state = None,
          dropout = None,
          n_layer = 1,
          return_last = False,
          return_seq_2d = False,
          dynamic_rnn_init_args={},
          name = 'bi_dyrnn_layer',
      ):
          Layer.__init__(self, name=name)
          if cell_fn is None:
              raise Exception("Please put in cell_fn")
          if 'GRU' in cell_fn.__name__:
              try:
                  cell_init_args.pop('state_is_tuple')
              except:
                  pass
          self.inputs = layer.outputs
  
          print("  [TL] BiDynamicRNNLayer %s: n_hidden:%d in_dim:%d in_shape:%s cell_fn:%s dropout:%s n_layer:%d" %
                (self.name, n_hidden, self.inputs.get_shape().ndims, self.inputs.get_shape(), cell_fn.__name__, dropout, n_layer))
  
          # Input dimension should be rank 3 [batch_size, n_steps(max), n_features]
          try:
              self.inputs.get_shape().with_rank(3)
          except:
              raise Exception("RNN : Input dimension should be rank 3 : [batch_size, n_steps(max), n_features]")
  
          # Get the batch_size
          fixed_batch_size = self.inputs.get_shape().with_rank_at_least(1)[0]
          if fixed_batch_size.value:
              batch_size = fixed_batch_size.value
              print("       batch_size (concurrent processes): %d" % batch_size)
          else:
              from tensorflow.python.ops import array_ops
              batch_size = array_ops.shape(self.inputs)[0]
              print("       non specified batch_size, uses a tensor instead.")
          self.batch_size = batch_size
  
          with tf.variable_scope(name, initializer=initializer) as vs:
              # Creats the cell function
              # cell_instance_fn=lambda: cell_fn(num_units=n_hidden, **cell_init_args) # HanSheng
              self.fw_cell = cell_fn(num_units=n_hidden, **cell_init_args)
              self.bw_cell = cell_fn(num_units=n_hidden, **cell_init_args)
  
              # Apply dropout
              if dropout:
                  if type(dropout) in [tuple, list]:
                      in_keep_prob = dropout[0]
                      out_keep_prob = dropout[1]
                  elif isinstance(dropout, float):
                      in_keep_prob, out_keep_prob = dropout, dropout
                  else:
                      raise Exception("Invalid dropout type (must be a 2-D tuple of "
                                      "float)")
                  try:
                      DropoutWrapper_fn = tf.contrib.rnn.DropoutWrapper
                  except:
                      DropoutWrapper_fn = tf.nn.rnn_cell.DropoutWrapper
  
                      # cell_instance_fn1=cell_instance_fn            # HanSheng
                      # cell_instance_fn=lambda: DropoutWrapper_fn(
                      #                     cell_instance_fn1(),
                      #                     input_keep_prob=in_keep_prob,
                      #                     output_keep_prob=out_keep_prob)
  
                  self.fw_cell = DropoutWrapper_fn(
                      self.fw_cell,
                      input_keep_prob=in_keep_prob,
                      output_keep_prob=out_keep_prob)
                  self.bw_cell = DropoutWrapper_fn(
                      self.bw_cell,
                      input_keep_prob=in_keep_prob,
                      output_keep_prob=out_keep_prob)
              # Apply multiple layers
              if n_layer > 1:
                  try:
                      MultiRNNCell_fn = tf.contrib.rnn.MultiRNNCell
                  except:
                      MultiRNNCell_fn = tf.nn.rnn_cell.MultiRNNCell
  
                  # cell_instance_fn2=cell_instance_fn            # HanSheng
                  # cell_instance_fn=lambda: MultiRNNCell_fn([cell_instance_fn2() for _ in range(n_layer)])
                  self.fw_cell = MultiRNNCell_fn([self.fw_cell] * n_layer)
                  self.bw_cell = MultiRNNCell_fn([self.bw_cell] * n_layer)
              # self.fw_cell=cell_instance_fn()
              # self.bw_cell=cell_instance_fn()
              # Initial state of RNN
              if fw_initial_state is None:
                  self.fw_initial_state = self.fw_cell.zero_state(self.batch_size, dtype=tf.float32)
              else:
                  self.fw_initial_state = fw_initial_state
              if bw_initial_state is None:
                  self.bw_initial_state = self.bw_cell.zero_state(self.batch_size, dtype=tf.float32)
              else:
                  self.bw_initial_state = bw_initial_state
              # Computes sequence_length
              if sequence_length is None:
                  try: ## TF1.0
                      sequence_length = retrieve_seq_length_op(
                          self.inputs if isinstance(self.inputs, tf.Tensor) else tf.stack(self.inputs))
                  except: ## TF0.12
                      sequence_length = retrieve_seq_length_op(
                          self.inputs if isinstance(self.inputs, tf.Tensor) else tf.pack(self.inputs))
  
              outputs, (states_fw, states_bw) = tf.nn.bidirectional_dynamic_rnn(
                  cell_fw=self.fw_cell,
                  cell_bw=self.bw_cell,
                  inputs=self.inputs,
                  sequence_length=sequence_length,
                  initial_state_fw=self.fw_initial_state,
                  initial_state_bw=self.bw_initial_state,
                  **dynamic_rnn_init_args
              )
              rnn_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
  
              print("     n_params : %d" % (len(rnn_variables)))
              # Manage the outputs
              try: # TF1.0
                  outputs = tf.concat(outputs, 2)
              except: # TF0.12
                  outputs = tf.concat(2, outputs)
              if return_last:
                  # [batch_size, 2 * n_hidden]
                  self.outputs = advanced_indexing_op(outputs, sequence_length)
              else:
                  # [batch_size, n_step(max), 2 * n_hidden]
                  if return_seq_2d:
                      # PTB tutorial:
                      # 2D Tensor [n_example, 2 * n_hidden]
                      try: # TF1.0
                          self.outputs = tf.reshape(tf.concat(outputs, 1), [-1, 2 * n_hidden])
                      except: # TF0.12
                          self.outputs = tf.reshape(tf.concat(1, outputs), [-1, 2 * n_hidden])
                  else:
                      # <akara>:
                      # 3D Tensor [batch_size, n_steps(max), 2 * n_hidden]
                      max_length = tf.shape(outputs)[1]
                      batch_size = tf.shape(outputs)[0]
                      try: # TF1.0
                          self.outputs = tf.reshape(tf.concat(outputs, 1), [batch_size, max_length, 2 * n_hidden])
                      except: # TF0.12
                          self.outputs = tf.reshape(tf.concat(1, outputs), [batch_size, max_length, 2 * n_hidden])
                      # self.outputs = tf.reshape(tf.concat(1, outputs), [-1, max_length, 2 * n_hidden])
  
          # Final state
          self.fw_final_states = states_fw
          self.bw_final_states = states_bw
  
          self.sequence_length = sequence_length
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
  
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( rnn_variables )
  
  # Seq2seq
  class Seq2Seq(Layer):
      """
      The :class:`Seq2Seq` class is a simple :class:`DynamicRNNLayer` based Seq2seq layer,
      both encoder and decoder are :class:`DynamicRNNLayer`, network details
      see `Model <https://camo.githubusercontent.com/242210d7d0151cae91107ee63bff364a860db5dd/687474703a2f2f6936342e74696e797069632e636f6d2f333031333674652e706e67>`_
      and `Sequence to Sequence Learning with Neural Networks <https://arxiv.org/abs/1409.3215>`_ .
  
      Parameters
      ----------
      net_encode_in : a :class:`Layer` instance
          Encode sequences, [batch_size, None, n_features].
      net_decode_in : a :class:`Layer` instance
          Decode sequences, [batch_size, None, n_features].
      cell_fn : a TensorFlow's core RNN cell as follow (Note TF1.0+ and TF1.0- are different).
          - see `RNN Cells in TensorFlow <https://www.tensorflow.org/api_docs/python/>`_
      cell_init_args : a dictionary
          The arguments for the cell initializer.
      n_hidden : a int
          The number of hidden units in the layer.
      initializer : initializer
          The initializer for initializing the parameters.
      encode_sequence_length : tensor for encoder sequence length, see :class:`DynamicRNNLayer` .
      decode_sequence_length : tensor for decoder sequence length, see :class:`DynamicRNNLayer` .
      initial_state : None or forward RNN State
          If None, initial_state is of encoder zero_state.
      dropout : `tuple` of `float`: (input_keep_prob, output_keep_prob).
          The input and output keep probability.
      n_layer : a int, default is 1.
          The number of RNN layers.
      return_seq_2d : boolean
          - When return_last = False
          - If True, return 2D Tensor [n_example, n_hidden], for stacking DenseLayer or computing cost after it.
          - If False, return 3D Tensor [n_example/n_steps(max), n_steps(max), n_hidden], for stacking multiple RNN after it.
      name : a string or None
          An optional name to attach to this layer.
  
      Variables
      ------------
      outputs : a tensor
          The output of RNN decoder.
  
      final_state : a tensor or StateTuple
          Final state of decoder, see :class:`DynamicRNNLayer` .
  
      Examples
      ----------
      >>> from tensorlayer.layers import *
      >>> batch_size = 32
      >>> encode_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="encode_seqs")
      >>> decode_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="decode_seqs")
      >>> target_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="target_seqs")
      >>> target_mask = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="target_mask") # tl.prepro.sequences_get_mask()
      >>> with tf.variable_scope("model"):
      ...     # for chatbot, you can use the same embedding layer,
      ...     # for translation, you may want to use 2 seperated embedding layers
      >>>     with tf.variable_scope("embedding") as vs:
      >>>         net_encode = EmbeddingInputlayer(
      ...                 inputs = encode_seqs,
      ...                 vocabulary_size = 10000,
      ...                 embedding_size = 200,
      ...                 name = 'seq_embedding')
      >>>         vs.reuse_variables()
      >>>         tl.layers.set_name_reuse(True)
      >>>         net_decode = EmbeddingInputlayer(
      ...                 inputs = decode_seqs,
      ...                 vocabulary_size = 10000,
      ...                 embedding_size = 200,
      ...                 name = 'seq_embedding')
      >>>     net = Seq2Seq(net_encode, net_decode,
      ...             cell_fn = tf.contrib.rnn.BasicLSTMCell,
      ...             n_hidden = 200,
      ...             initializer = tf.random_uniform_initializer(-0.1, 0.1),
      ...             encode_sequence_length = retrieve_seq_length_op2(encode_seqs),
      ...             decode_sequence_length = retrieve_seq_length_op2(decode_seqs),
      ...             initial_state = None,
      ...             dropout = None,
      ...             n_layer = 1,
      ...             return_seq_2d = True,
      ...             name = 'seq2seq')
      >>> net_out = DenseLayer(net, n_units=10000, act=tf.identity, name='output')
      >>> e_loss = tl.cost.cross_entropy_seq_with_mask(logits=net_out.outputs, target_seqs=target_seqs, input_mask=target_mask, return_details=False, name='cost')
      >>> y = tf.nn.softmax(net_out.outputs)
      >>> net_out.print_params(False)
  
      Notes
      --------
      - How to feed data: `Sequence to Sequence Learning with Neural Networks <https://arxiv.org/pdf/1409.3215v3.pdf>`_
      - input_seqs : ``['how', 'are', 'you', '<PAD_ID'>]``
      - decode_seqs : ``['<START_ID>', 'I', 'am', 'fine', '<PAD_ID'>]``
      - target_seqs : ``['I', 'am', 'fine', '<END_ID']``
      - target_mask : ``[1, 1, 1, 1, 0]``
      - related functions : tl.prepro <pad_sequences, precess_sequences, sequences_add_start_id, sequences_get_mask>
      """
      def __init__(
          self,
          net_encode_in = None,
          net_decode_in = None,
          cell_fn = None,#tf.nn.rnn_cell.LSTMCell,
          cell_init_args = {'state_is_tuple':True},
          n_hidden = 256,
          initializer = tf.random_uniform_initializer(-0.1, 0.1),
          encode_sequence_length = None,
          decode_sequence_length = None,
          initial_state = None,
          dropout = None,
          n_layer = 1,
          # return_last = False,
          return_seq_2d = False,
          name = 'seq2seq',
      ):
          Layer.__init__(self, name=name)
          if cell_fn is None:
              raise Exception("Please put in cell_fn")
          if 'GRU' in cell_fn.__name__:
              try:
                  cell_init_args.pop('state_is_tuple')
              except:
                  pass
          # self.inputs = layer.outputs
          print("  [**] Seq2Seq %s: n_hidden:%d cell_fn:%s dropout:%s n_layer:%d" %
                (self.name, n_hidden, cell_fn.__name__, dropout, n_layer))
  
          with tf.variable_scope(name) as vs:#, reuse=reuse):
              # tl.layers.set_name_reuse(reuse)
              # network = InputLayer(self.inputs, name=name+'/input')
              network_encode = DynamicRNNLayer(net_encode_in,
                       cell_fn = cell_fn,
                       cell_init_args = cell_init_args,
                       n_hidden = n_hidden,
                       initial_state = initial_state,
                       dropout = dropout,
                       n_layer = n_layer,
                       sequence_length = encode_sequence_length,
                       return_last = False,
                       return_seq_2d = True,
                       name = name+'_encode')
              # vs.reuse_variables()
              # tl.layers.set_name_reuse(True)
              network_decode = DynamicRNNLayer(net_decode_in,
                       cell_fn = cell_fn,
                       cell_init_args = cell_init_args,
                       n_hidden = n_hidden,
                       initial_state = network_encode.final_state,
                       dropout = dropout,
                       n_layer = n_layer,
                       sequence_length = decode_sequence_length,
                       return_last = False,
                       return_seq_2d = return_seq_2d,
                       name = name+'_decode')
              self.outputs = network_decode.outputs
  
              rnn_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
  
          # Final state
          self.final_state = network_decode.final_state
  
          # self.sequence_length = sequence_length
          self.all_layers = list(network_decode.all_layers)
          self.all_params = list(network_decode.all_params)
          self.all_drop = dict(network_decode.all_drop)
  
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( rnn_variables )
  
          self.all_layers = list_remove_repeat(self.all_layers)
          self.all_params = list_remove_repeat(self.all_params)
  
  class PeekySeq2Seq(Layer):
      """
      Waiting for contribution.
      The :class:`PeekySeq2Seq` class, see `Model <https://camo.githubusercontent.com/7f690d451036938a51e62feb77149c8bb4be6675/687474703a2f2f6936342e74696e797069632e636f6d2f333032617168692e706e67>`_
      and `Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation <https://arxiv.org/abs/1406.1078>`_ .
      """
      def __init__(
          self,
          net_encode_in = None,
          net_decode_in = None,
          cell_fn = None,#tf.nn.rnn_cell.LSTMCell,
          cell_init_args = {'state_is_tuple':True},
          n_hidden = 256,
          initializer = tf.random_uniform_initializer(-0.1, 0.1),
          in_sequence_length = None,
          out_sequence_length = None,
          initial_state = None,
          dropout = None,
          n_layer = 1,
          # return_last = False,
          return_seq_2d = False,
          name = 'peeky_seq2seq',
      ):
          Layer.__init__(self, name=name)
          if cell_fn is None:
              raise Exception("Please put in cell_fn")
          # self.inputs = layer.outputs
          print("  [TL] PeekySeq2seq %s: n_hidden:%d cell_fn:%s dropout:%s n_layer:%d" %
                (self.name, n_hidden, cell_fn.__name__, dropout, n_layer))
  
  class AttentionSeq2Seq(Layer):
      """
      Waiting for contribution.
      The :class:`AttentionSeq2Seq` class, see `Model <https://camo.githubusercontent.com/0e2e4e5fb2dd47846c2fe027737a5df5e711df1b/687474703a2f2f6936342e74696e797069632e636f6d2f6132727733642e706e67>`_
      and `Neural Machine Translation by Jointly Learning to Align and Translate <https://arxiv.org/pdf/1409.0473v6.pdf>`_ .
      """
      def __init__(
          self,
          net_encode_in = None,
          net_decode_in = None,
          cell_fn = None,#tf.nn.rnn_cell.LSTMCell,
          cell_init_args = {'state_is_tuple':True},
          n_hidden = 256,
          initializer = tf.random_uniform_initializer(-0.1, 0.1),
          in_sequence_length = None,
          out_sequence_length = None,
          initial_state = None,
          dropout = None,
          n_layer = 1,
          # return_last = False,
          return_seq_2d = False,
          name = 'attention_seq2seq',
      ):
          Layer.__init__(self, name=name)
          if cell_fn is None:
              raise Exception("Please put in cell_fn")
          # self.inputs = layer.outputs
          print("  [TL] PeekySeq2seq %s: n_hidden:%d cell_fn:%s dropout:%s n_layer:%d" %
                (self.name, n_hidden, cell_fn.__name__, dropout, n_layer))
  
  ## Shape layer
  class FlattenLayer(Layer):
      """
      The :class:`FlattenLayer` class is layer which reshape high-dimension
      input to a vector. Then we can apply DenseLayer, RNNLayer, ConcatLayer and
      etc on the top of it.
  
      [batch_size, mask_row, mask_col, n_mask] ---> [batch_size, mask_row * mask_col * n_mask]
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      name : a string or None
          An optional name to attach to this layer.
  
      Examples
      --------
      >>> x = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
      >>> network = tl.layers.InputLayer(x, name='input_layer')
      >>> network = tl.layers.Conv2dLayer(network,
      ...                    act = tf.nn.relu,
      ...                    shape = [5, 5, 32, 64],
      ...                    strides=[1, 1, 1, 1],
      ...                    padding='SAME',
      ...                    name ='cnn_layer')
      >>> network = tl.layers.Pool2dLayer(network,
      ...                    ksize=[1, 2, 2, 1],
      ...                    strides=[1, 2, 2, 1],
      ...                    padding='SAME',
      ...                    pool = tf.nn.max_pool,
      ...                    name ='pool_layer',)
      >>> network = tl.layers.FlattenLayer(network, name='flatten_layer')
      """
      def __init__(
          self,
          layer = None,
          name ='flatten_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          self.outputs = flatten_reshape(self.inputs, name=name)
          self.n_units = int(self.outputs.get_shape()[-1])
          print("  [TL] FlattenLayer %s: %d" % (self.name, self.n_units))
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
  
  class ReshapeLayer(Layer):
      """
      The :class:`ReshapeLayer` class is layer which reshape the tensor.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      shape : a list
          The output shape.
      name : a string or None
          An optional name to attach to this layer.
  
      Examples
      --------
      - The core of this layer is ``tf.reshape``.
      - Use TensorFlow only :
      >>> x = tf.placeholder(tf.float32, shape=[None, 3])
      >>> y = tf.reshape(x, shape=[-1, 3, 3])
      >>> sess = tf.InteractiveSession()
      >>> print(sess.run(y, feed_dict={x:[[1,1,1],[2,2,2],[3,3,3],[4,4,4],[5,5,5],[6,6,6]]}))
      ... [[[ 1.  1.  1.]
      ... [ 2.  2.  2.]
      ... [ 3.  3.  3.]]
      ... [[ 4.  4.  4.]
      ... [ 5.  5.  5.]
      ... [ 6.  6.  6.]]]
      """
      def __init__(
          self,
          layer = None,
          shape = [],
          name ='reshape_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          self.outputs = tf.reshape(self.inputs, shape=shape, name=name)
          print("  [TL] ReshapeLayer %s: %s" % (self.name, self.outputs.get_shape()))
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
  
  class LambdaLayer(Layer):
      """
      The :class:`LambdaLayer` class is a layer which is able to use the provided function.
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      fn : a function
          The function that applies to the outputs of previous layer.
      fn_args : a dictionary
          The arguments for the function (option).
      name : a string or None
          An optional name to attach to this layer.
  
      Examples
      ---------
      >>> x = tf.placeholder(tf.float32, shape=[None, 1], name='x')
      >>> network = tl.layers.InputLayer(x, name='input_layer')
      >>> network = LambdaLayer(network, lambda x: 2*x, name='lambda_layer')
      >>> y = network.outputs
      >>> sess = tf.InteractiveSession()
      >>> out = sess.run(y, feed_dict={x : [[1],[2]]})
      ... [[2],[4]]
      """
      def __init__(
          self,
          layer = None,
          fn = None,
          fn_args = {},
          name = 'lambda_layer',
      ):
          Layer.__init__(self, name=name)
          assert layer is not None
          assert fn is not None
          self.inputs = layer.outputs
          print("  [TL] LambdaLayer  %s" % self.name)
          with tf.variable_scope(name) as vs:
              self.outputs = fn(self.inputs, **fn_args)
              variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( variables )
  
  ## Merge layer
  class ConcatLayer(Layer):
      """
      The :class:`ConcatLayer` class is layer which concat (merge) two or more
      :class:`DenseLayer` to a single class:`DenseLayer`.
  
      Parameters
      ----------
      layer : a list of :class:`Layer` instances
          The `Layer` class feeding into this layer.
      concat_dim : int
          Dimension along which to concatenate.
      name : a string or None
          An optional name to attach to this layer.
  
      Examples
      --------
      >>> sess = tf.InteractiveSession()
      >>> x = tf.placeholder(tf.float32, shape=[None, 784])
      >>> inputs = tl.layers.InputLayer(x, name='input_layer')
      >>> net1 = tl.layers.DenseLayer(inputs, n_units=800, act = tf.nn.relu, name='relu1_1')
      >>> net2 = tl.layers.DenseLayer(inputs, n_units=300, act = tf.nn.relu, name='relu2_1')
      >>> network = tl.layers.ConcatLayer(layer = [net1, net2], name ='concat_layer')
      ...     [TL] InputLayer input_layer (?, 784)
      ...     [TL] DenseLayer relu1_1: 800, <function relu at 0x1108e41e0>
      ...     [TL] DenseLayer relu2_1: 300, <function relu at 0x1108e41e0>
      ...     [TL] ConcatLayer concat_layer, 1100
      ...
      >>> tl.layers.initialize_global_variables(sess)
      >>> network.print_params()
      ...     param 0: (784, 800) (mean: 0.000021, median: -0.000020 std: 0.035525)
      ...     param 1: (800,) (mean: 0.000000, median: 0.000000 std: 0.000000)
      ...     param 2: (784, 300) (mean: 0.000000, median: -0.000048 std: 0.042947)
      ...     param 3: (300,) (mean: 0.000000, median: 0.000000 std: 0.000000)
      ...     num of params: 863500
      >>> network.print_layers()
      ...     layer 0: Tensor("Relu:0", shape=(?, 800), dtype=float32)
      ...     layer 1: Tensor("Relu_1:0", shape=(?, 300), dtype=float32)
      ...
      """
      def __init__(
          self,
          layer = [],
          concat_dim = 1,
          name ='concat_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = []
          for l in layer:
              self.inputs.append(l.outputs)
          try: # TF1.0
              self.outputs = tf.concat(self.inputs, concat_dim, name=name)
          except: # TF0.12
              self.outputs = tf.concat(concat_dim, self.inputs, name=name)
          self.n_units = int(self.outputs.get_shape()[-1])
          print("  [TL] ConcatLayer %s: %d" % (self.name, self.n_units))
  
          self.all_layers = list(layer[0].all_layers)
          self.all_params = list(layer[0].all_params)
          self.all_drop = dict(layer[0].all_drop)
  
          for i in range(1, len(layer)):
              self.all_layers.extend(list(layer[i].all_layers))
              self.all_params.extend(list(layer[i].all_params))
              self.all_drop.update(dict(layer[i].all_drop))
  
          self.all_layers = list_remove_repeat(self.all_layers)
          self.all_params = list_remove_repeat(self.all_params)
          #self.all_drop = list_remove_repeat(self.all_drop) # it is a dict
  
  class ElementwiseLayer(Layer):
      """
      The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation.
  
      Parameters
      ----------
      layer : a list of :class:`Layer` instances
          The `Layer` class feeding into this layer.
      combine_fn : a TensorFlow elemwise-merge function
          e.g. AND is ``tf.minimum`` ;  OR is ``tf.maximum`` ; ADD is ``tf.add`` ; MUL is ``tf.multiply`` and so on.
          See `TensorFlow Math API <https://www.tensorflow.org/versions/master/api_docs/python/math_ops.html#math>`_ .
      name : a string or None
          An optional name to attach to this layer.
  
      Examples
      --------
      - AND Logic
      >>> net_0 = tl.layers.DenseLayer(net_0, n_units=500,
      ...                        act = tf.nn.relu, name='net_0')
      >>> net_1 = tl.layers.DenseLayer(net_1, n_units=500,
      ...                        act = tf.nn.relu, name='net_1')
      >>> net_com = tl.layers.ElementwiseLayer(layer = [net_0, net_1],
      ...                         combine_fn = tf.minimum,
      ...                         name = 'combine_layer')
      """
      def __init__(
          self,
          layer = [],
          combine_fn = tf.minimum,
          name ='elementwise_layer',
      ):
          Layer.__init__(self, name=name)
  
          print("  [TL] ElementwiseLayer %s: size:%s fn:%s" % (self.name, layer[0].outputs.get_shape(), combine_fn.__name__))
  
          self.outputs = layer[0].outputs
          # print(self.outputs._shape, type(self.outputs._shape))
          for l in layer[1:]:
              assert str(self.outputs.get_shape()) == str(l.outputs.get_shape()), "Hint: the input shapes should be the same. %s != %s" %  (self.outputs.get_shape() , str(l.outputs.get_shape()))
              self.outputs = combine_fn(self.outputs, l.outputs, name=name)
  
          self.all_layers = list(layer[0].all_layers)
          self.all_params = list(layer[0].all_params)
          self.all_drop = dict(layer[0].all_drop)
  
          for i in range(1, len(layer)):
              self.all_layers.extend(list(layer[i].all_layers))
              self.all_params.extend(list(layer[i].all_params))
              self.all_drop.update(dict(layer[i].all_drop))
  
          self.all_layers = list_remove_repeat(self.all_layers)
          self.all_params = list_remove_repeat(self.all_params)
          # self.all_drop = list_remove_repeat(self.all_drop)
  
  # Extend
  class ExpandDimsLayer(Layer):
      """
      The :class:`ExpandDimsLayer` class inserts a dimension of 1 into a tensor's shape,
      see `tf.expand_dims() <https://www.tensorflow.org/api_docs/python/array_ops/shapes_and_shaping#expand_dims>`_ .
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      axis : int, 0-D (scalar).
          Specifies the dimension index at which to expand the shape of input.
      name : a string or None
          An optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          axis = None,
          name = 'expand_dims',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
  
          print("  [TL] ExpandDimsLayer  %s: axis:%d" % (self.name, axis))
          with tf.variable_scope(name) as vs:
              try:    # TF12 TF1.0
                  self.outputs = tf.expand_dims(self.inputs, axis=axis)
              except: # TF11
                  self.outputs = tf.expand_dims(self.inputs, dim=axis)
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          # self.all_params.extend( variables )
  
  class TileLayer(Layer):
      """
      The :class:`TileLayer` class constructs a tensor by tiling a given tensor,
      see `tf.tile() <https://www.tensorflow.org/api_docs/python/array_ops/slicing_and_joining#tile>`_ .
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      multiples: a list of int
          Must be one of the following types: int32, int64. 1-D. Length must be the same as the number of dimensions in input
      name : a string or None
          An optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          multiples = None,
          name = 'tile',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
  
          print("  [TL] TileLayer  %s: multiples:%s" % (self.name, multiples))
          with tf.variable_scope(name) as vs:
              self.outputs = tf.tile(self.inputs, multiples=multiples)
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          # self.all_params.extend( variables )
  
  ## TF-Slim layer
  class SlimNetsLayer(Layer):
      """
      The :class:`SlimNetsLayer` class can be used to merge all TF-Slim nets into
      TensorLayer. Model can be found in `slim-model <https://github.com/tensorflow/models/tree/master/slim#pre-trained-models>`_ , more about slim
      see `slim-git <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim>`_ .
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      slim_layer : a slim network function
          The network you want to stack onto, end with ``return net, end_points``.
      slim_args : dictionary
          The arguments for the slim model.
      name : a string or None
          An optional name to attach to this layer.
  
      Examples
      --------
      - see Inception V3 example on `Github <https://github.com/zsdonghao/tensorlayer>`_
  
      Notes
      -----
      The due to TF-Slim stores the layers as dictionary, the ``all_layers`` in this
      network is not in order ! Fortunately, the ``all_params`` are in order.
      """
      def __init__(
          self,
          layer = None,
          slim_layer = None,
          slim_args = {},
          name ='tfslim_layer',
      ):
          Layer.__init__(self, name=name)
          assert slim_layer is not None
          assert slim_args is not None
          self.inputs = layer.outputs
          print("  [TL] SlimNetsLayer %s: %s" % (self.name, slim_layer.__name__))
  
          # with tf.variable_scope(name) as vs:
          #     net, end_points = slim_layer(self.inputs, **slim_args)
          #     slim_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
  
          net, end_points = slim_layer(self.inputs, **slim_args)
  
          slim_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=name)
          if slim_variables == []:
              print("No variables found under %s : the name of SlimNetsLayer should be matched with the begining of the ckpt file, see tutorial_inceptionV3_tfslim.py for more details" % name)
  
  
          self.outputs = net
  
          slim_layers = []
          for v in end_points.values():
              # tf.contrib.layers.summaries.summarize_activation(v)
              slim_layers.append(v)
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
  
          self.all_layers.extend( slim_layers )
          self.all_params.extend( slim_variables )
  
  ## Keras layer
  class KerasLayer(Layer):
      """
      The :class:`KerasLayer` class can be used to merge all Keras layers into
      TensorLayer. Example can be found here `tutorial_keras.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_keras.py>`_
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      keras_layer : a keras network function
      keras_args : dictionary
          The arguments for the keras model.
      name : a string or None
          An optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          keras_layer = None,
          keras_args = {},
          name ='keras_layer',
      ):
          Layer.__init__(self, name=name)
          assert layer is not None
          assert keras_layer is not None
          self.inputs = layer.outputs
          print("  [TL] KerasLayer %s: %s" % (self.name, keras_layer))
          print("       This API will be removed, please use LambdaLayer instead.")
          with tf.variable_scope(name) as vs:
              self.outputs = keras_layer(self.inputs, **keras_args)
              variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( variables )
  
  ## Estimator layer
  class EstimatorLayer(Layer):
      """
      The :class:`EstimatorLayer` class accepts ``model_fn`` that described the model.
      It is similar with :class:`KerasLayer`, see `tutorial_keras.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_keras.py>`_
  
      Parameters
      ----------
      layer : a :class:`Layer` instance
          The `Layer` class feeding into this layer.
      model_fn : a function that described the model.
      args : dictionary
          The arguments for the model_fn.
      name : a string or None
          An optional name to attach to this layer.
      """
      def __init__(
          self,
          layer = None,
          model_fn = None,
          args = {},
          name ='estimator_layer',
      ):
          Layer.__init__(self, name=name)
          assert layer is not None
          assert model_fn is not None
          self.inputs = layer.outputs
          print("  [TL] EstimatorLayer %s: %s" % (self.name, model_fn))
          print("       This API will be removed, please use LambdaLayer instead.")
          with tf.variable_scope(name) as vs:
              self.outputs = model_fn(self.inputs, **args)
              variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( variables )
  
  ## Special activation
  class PReluLayer(Layer):
      """
      The :class:`PReluLayer` class is Parametric Rectified Linear layer.
  
      Parameters
      ----------
      x : A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
          `int16`, or `int8`.
      channel_shared : `bool`. Single weight is shared by all channels
      a_init : alpha initializer, default zero constant.
          The initializer for initializing the alphas.
      a_init_args : dictionary
          The arguments for the weights initializer.
      name : A name for this activation op (optional).
  
      References
      -----------
      - `Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification <http://arxiv.org/pdf/1502.01852v1.pdf>`_
      """
      def __init__(
          self,
          layer = None,
          channel_shared = False,
          a_init = tf.constant_initializer(value=0.0),
          a_init_args = {},
          # restore = True,
          name="prelu_layer"
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
          print("  [TL] PReluLayer %s: channel_shared:%s" % (self.name, channel_shared))
          if channel_shared:
              w_shape = (1,)
          else:
              w_shape = int(self.inputs.get_shape()[-1])
  
          # with tf.name_scope(name) as scope:
          with tf.variable_scope(name) as vs:
              alphas = tf.get_variable(name='alphas', shape=w_shape, initializer=a_init, **a_init_args )
              try:  ## TF 1.0
                  self.outputs = tf.nn.relu(self.inputs) + tf.multiply(alphas, (self.inputs - tf.abs(self.inputs))) * 0.5
              except: ## TF 0.12
                  self.outputs = tf.nn.relu(self.inputs) + tf.mul(alphas, (self.inputs - tf.abs(self.inputs))) * 0.5
  
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
  
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( [alphas] )
  
  ## Flow control layer
  class MultiplexerLayer(Layer):
      """
      The :class:`MultiplexerLayer` selects one of several input and forwards the selected input into the output,
      see `tutorial_mnist_multiplexer.py`.
  
      Parameters
      ----------
      layer : a list of :class:`Layer` instances
          The `Layer` class feeding into this layer.
      name : a string or None
          An optional name to attach to this layer.
  
  
      Variables
      -----------------------
      sel : a placeholder
          Input an int [0, inf], which input is the output
  
      Examples
      --------
      >>> x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
      >>> y_ = tf.placeholder(tf.int64, shape=[None, ], name='y_')
      >>> # define the network
      >>> net_in = tl.layers.InputLayer(x, name='input_layer')
      >>> net_in = tl.layers.DropoutLayer(net_in, keep=0.8, name='drop1')
      >>> # net 0
      >>> net_0 = tl.layers.DenseLayer(net_in, n_units=800,
      ...                                act = tf.nn.relu, name='net0/relu1')
      >>> net_0 = tl.layers.DropoutLayer(net_0, keep=0.5, name='net0/drop2')
      >>> net_0 = tl.layers.DenseLayer(net_0, n_units=800,
      ...                                act = tf.nn.relu, name='net0/relu2')
      >>> # net 1
      >>> net_1 = tl.layers.DenseLayer(net_in, n_units=800,
      ...                                act = tf.nn.relu, name='net1/relu1')
      >>> net_1 = tl.layers.DropoutLayer(net_1, keep=0.8, name='net1/drop2')
      >>> net_1 = tl.layers.DenseLayer(net_1, n_units=800,
      ...                                act = tf.nn.relu, name='net1/relu2')
      >>> net_1 = tl.layers.DropoutLayer(net_1, keep=0.8, name='net1/drop3')
      >>> net_1 = tl.layers.DenseLayer(net_1, n_units=800,
      ...                                act = tf.nn.relu, name='net1/relu3')
      >>> # multiplexer
      >>> net_mux = tl.layers.MultiplexerLayer(layer = [net_0, net_1], name='mux_layer')
      >>> network = tl.layers.ReshapeLayer(net_mux, shape=[-1, 800], name='reshape_layer') #
      >>> network = tl.layers.DropoutLayer(network, keep=0.5, name='drop3')
      >>> # output layer
      >>> network = tl.layers.DenseLayer(network, n_units=10,
      ...                                act = tf.identity, name='output_layer')
  
      References
      ------------
      - See ``tf.pack() for TF0.12 or tf.stack() for TF1.0`` and ``tf.gather()`` at `TensorFlow - Slicing and Joining <https://www.tensorflow.org/versions/master/api_docs/python/array_ops.html#slicing-and-joining>`_
      """
      def __init__(self,
                 layer = [],
                 name='mux_layer'):
          Layer.__init__(self, name=name)
          self.n_inputs = len(layer)
  
          self.inputs = []
          for l in layer:
              self.inputs.append(l.outputs)
          try: ## TF1.0
              all_inputs = tf.stack(self.inputs, name=name) # pack means concat a list of tensor in a new dim  # 1.2
          except:
              all_inputs = tf.pack(self.inputs, name=name) # pack means concat a list of tensor in a new dim  # 1.2
  
          print("  [TL] MultiplexerLayer %s: n_inputs:%d" % (self.name, self.n_inputs))
  
          self.sel = tf.placeholder(tf.int32)
          self.outputs = tf.gather(all_inputs, self.sel, name=name) # [sel, :, : ...] # 1.2
  
          # print(self.outputs, vars(self.outputs))
          #         # tf.reshape(self.outputs, shape=)
          # exit()
          # the same with ConcatLayer
          self.all_layers = list(layer[0].all_layers)
          self.all_params = list(layer[0].all_params)
          self.all_drop = dict(layer[0].all_drop)
  
          for i in range(1, len(layer)):
              self.all_layers.extend(list(layer[i].all_layers))
              self.all_params.extend(list(layer[i].all_params))
              self.all_drop.update(dict(layer[i].all_drop))
  
          self.all_layers = list_remove_repeat(self.all_layers)
          self.all_params = list_remove_repeat(self.all_params)
          # self.all_drop = list_remove_repeat(self.all_drop)
  ## We can Duplicate the network instead of DemultiplexerLayer
  # class DemultiplexerLayer(Layer):
  #     """
  #     The :class:`DemultiplexerLayer` takes a single input and select one of many output lines, which is connected to the input.
  #
  #     Parameters
  #     ----------
  #     layer : a list of :class:`Layer` instances
  #         The `Layer` class feeding into this layer.
  #     n_outputs : a int
  #         The number of output
  #     name : a string or None
  #         An optional name to attach to this layer.
  #
  #     Field (Class Variables)
  #     -----------------------
  #     sel : a placeholder
  #         Input int [0, inf], the
  #     outputs : a list of Tensor
  #         A list of outputs
  #
  #     Examples
  #     --------
  #     >>>
  #     """
  #     def __init__(self,
  #            layer = None,
  #            name='demux_layer'):
  #         Layer.__init__(self, name=name)
  #         self.outputs = []
  
  ## Wrapper
  class EmbeddingAttentionSeq2seqWrapper(Layer):
    """Sequence-to-sequence model with attention and for multiple buckets (Deprecated after TF0.12).
  
      This example implements a multi-layer recurrent neural network as encoder,
      and an attention-based decoder. This is the same as the model described in
      this paper:
      - `Grammar as a Foreign Language <http://arxiv.org/abs/1412.7449>`_
      please look there for details,
      or into the seq2seq library for complete model implementation.
      This example also allows to use GRU cells in addition to LSTM cells, and
      sampled softmax to handle large output vocabulary size. A single-layer
      version of this model, but with bi-directional encoder, was presented in
      - `Neural Machine Translation by Jointly Learning to Align and Translate <http://arxiv.org/abs/1409.0473>`_
      The sampled softmax is described in Section 3 of the following paper.
      - `On Using Very Large Target Vocabulary for Neural Machine Translation <http://arxiv.org/abs/1412.2007>`_
  
      Parameters
      ----------
      source_vocab_size : size of the source vocabulary.
      target_vocab_size : size of the target vocabulary.
      buckets : a list of pairs (I, O), where I specifies maximum input length
          that will be processed in that bucket, and O specifies maximum output
          length. Training instances that have inputs longer than I or outputs
          longer than O will be pushed to the next bucket and padded accordingly.
          We assume that the list is sorted, e.g., [(2, 4), (8, 16)].
      size : number of units in each layer of the model.
      num_layers : number of layers in the model.
      max_gradient_norm : gradients will be clipped to maximally this norm.
      batch_size : the size of the batches used during training;
          the model construction is independent of batch_size, so it can be
          changed after initialization if this is convenient, e.g., for decoding.
      learning_rate : learning rate to start with.
      learning_rate_decay_factor : decay learning rate by this much when needed.
      use_lstm : if true, we use LSTM cells instead of GRU cells.
      num_samples : number of samples for sampled softmax.
      forward_only : if set, we do not construct the backward pass in the model.
      name : a string or None
          An optional name to attach to this layer.
    """
    def __init__(self,
                 source_vocab_size,
                 target_vocab_size,
                 buckets,
                 size,
                 num_layers,
                 max_gradient_norm,
                 batch_size,
                 learning_rate,
                 learning_rate_decay_factor,
                 use_lstm=False,
                 num_samples=512,
                 forward_only=False,
                 name='wrapper'):
      Layer.__init__(self)#, name=name)
  
      self.source_vocab_size = source_vocab_size
      self.target_vocab_size = target_vocab_size
      self.buckets = buckets
      self.batch_size = batch_size
      self.learning_rate = tf.Variable(float(learning_rate), trainable=False, name='learning_rate')
      self.learning_rate_decay_op = self.learning_rate.assign(
          self.learning_rate * learning_rate_decay_factor)
      self.global_step = tf.Variable(0, trainable=False, name='global_step')
  
      if tf.__version__ >= "0.12":
          raise Exception("Deprecated after TF0.12 : use other seq2seq layers instead.")
  
      # =========== Fake output Layer for compute cost ======
      # If we use sampled softmax, we need an output projection.
      with tf.variable_scope(name) as vs:
          output_projection = None
          softmax_loss_function = None
          # Sampled softmax only makes sense if we sample less than vocabulary size.
          if num_samples > 0 and num_samples < self.target_vocab_size:
            w = tf.get_variable("proj_w", [size, self.target_vocab_size])
            w_t = tf.transpose(w)
            b = tf.get_variable("proj_b", [self.target_vocab_size])
            output_projection = (w, b)
  
            def sampled_loss(inputs, labels):
              labels = tf.reshape(labels, [-1, 1])
              return tf.nn.sampled_softmax_loss(w_t, b, inputs, labels, num_samples,
                      self.target_vocab_size)
            softmax_loss_function = sampled_loss
  
          # ============ Seq Encode Layer =============
          # Create the internal multi-layer cell for our RNN.
          try: # TF1.0
            single_cell = tf.contrib.rnn.GRUCell(size)
          except:
            single_cell = tf.nn.rnn_cell.GRUCell(size)
  
          if use_lstm:
            try: # TF1.0
              single_cell = tf.contrib.rnn.BasicLSTMCell(size)
            except:
              single_cell = tf.nn.rnn_cell.BasicLSTMCell(size)
  
          cell = single_cell
          if num_layers > 1:
            try: # TF1.0
              cell = tf.contrib.rnn.MultiRNNCell([single_cell] * num_layers)
            except:
              cell = tf.nn.rnn_cell.MultiRNNCell([single_cell] * num_layers)
  
          # ============== Seq Decode Layer ============
          # The seq2seq function: we use embedding for the input and attention.
          def seq2seq_f(encoder_inputs, decoder_inputs, do_decode):
            return tf.nn.seq2seq.embedding_attention_seq2seq(
                encoder_inputs, decoder_inputs, cell,
                num_encoder_symbols=source_vocab_size,
                num_decoder_symbols=target_vocab_size,
                embedding_size=size,
                output_projection=output_projection,
                feed_previous=do_decode)
  
          #=============================================================
          # Feeds for inputs.
          self.encoder_inputs = []
          self.decoder_inputs = []
          self.target_weights = []
          for i in xrange(buckets[-1][0]):  # Last bucket is the biggest one.
            self.encoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
                                                      name="encoder{0}".format(i)))
          for i in xrange(buckets[-1][1] + 1):
            self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
                                                      name="decoder{0}".format(i)))
            self.target_weights.append(tf.placeholder(tf.float32, shape=[None],
                                                      name="weight{0}".format(i)))
  
          # Our targets are decoder inputs shifted by one.
          targets = [self.decoder_inputs[i + 1]
                     for i in xrange(len(self.decoder_inputs) - 1)]
          self.targets = targets  # DH add for debug
  
  
          # Training outputs and losses.
          if forward_only:
            self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets(
                self.encoder_inputs, self.decoder_inputs, targets,
                self.target_weights, buckets, lambda x, y: seq2seq_f(x, y, True),
                softmax_loss_function=softmax_loss_function)
            # If we use output projection, we need to project outputs for decoding.
            if output_projection is not None:
              for b in xrange(len(buckets)):
                self.outputs[b] = [
                    tf.matmul(output, output_projection[0]) + output_projection[1]
                    for output in self.outputs[b]
                ]
          else:
            self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets(
                self.encoder_inputs, self.decoder_inputs, targets,
                self.target_weights, buckets,
                lambda x, y: seq2seq_f(x, y, False),
                softmax_loss_function=softmax_loss_function)
  
          # Gradients and SGD update operation for training the model.
          params = tf.trainable_variables()
          if not forward_only:
            self.gradient_norms = []
            self.updates = []
            opt = tf.train.GradientDescentOptimizer(self.learning_rate)
            for b in xrange(len(buckets)):
              gradients = tf.gradients(self.losses[b], params)
              clipped_gradients, norm = tf.clip_by_global_norm(gradients,
                                                               max_gradient_norm)
              self.gradient_norms.append(norm)
              self.updates.append(opt.apply_gradients(
                  zip(clipped_gradients, params), global_step=self.global_step))
  
          # if save into npz
          self.all_params = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
  
      # if save into ckpt
      self.saver = tf.train.Saver(tf.all_variables())
  
    def step(self, session, encoder_inputs, decoder_inputs, target_weights,
             bucket_id, forward_only):
      """Run a step of the model feeding the given inputs.
  
      Parameters
      ----------
      session : tensorflow session to use.
      encoder_inputs : list of numpy int vectors to feed as encoder inputs.
      decoder_inputs : list of numpy int vectors to feed as decoder inputs.
      target_weights : list of numpy float vectors to feed as target weights.
      bucket_id : which bucket of the model to use.
      forward_only : whether to do the backward step or only forward.
  
      Returns
      --------
      A triple consisting of gradient norm (or None if we did not do backward),
      average perplexity, and the outputs.
  
      Raises
      --------
      ValueError : if length of encoder_inputs, decoder_inputs, or
          target_weights disagrees with bucket size for the specified bucket_id.
      """
      # Check if the sizes match.
      encoder_size, decoder_size = self.buckets[bucket_id]
      if len(encoder_inputs) != encoder_size:
        raise ValueError("Encoder length must be equal to the one in bucket,"
                         " %d != %d." % (len(encoder_inputs), encoder_size))
      if len(decoder_inputs) != decoder_size:
        raise ValueError("Decoder length must be equal to the one in bucket,"
                         " %d != %d." % (len(decoder_inputs), decoder_size))
      if len(target_weights) != decoder_size:
        raise ValueError("Weights length must be equal to the one in bucket,"
                         " %d != %d." % (len(target_weights), decoder_size))
      # print('in model.step()')
      # print('a',bucket_id, encoder_size, decoder_size)
  
      # Input feed: encoder inputs, decoder inputs, target_weights, as provided.
      input_feed = {}
      for l in xrange(encoder_size):
        input_feed[self.encoder_inputs[l].name] = encoder_inputs[l]
      for l in xrange(decoder_size):
        input_feed[self.decoder_inputs[l].name] = decoder_inputs[l]
        input_feed[self.target_weights[l].name] = target_weights[l]
      # print(self.encoder_inputs[l].name)
      # print(self.decoder_inputs[l].name)
      # print(self.target_weights[l].name)
  
      # Since our targets are decoder inputs shifted by one, we need one more.
      last_target = self.decoder_inputs[decoder_size].name
      input_feed[last_target] = np.zeros([self.batch_size], dtype=np.int32)
      # print('last_target', last_target)
  
      # Output feed: depends on whether we do a backward step or not.
      if not forward_only:
        output_feed = [self.updates[bucket_id],  # Update Op that does SGD.
                       self.gradient_norms[bucket_id],  # Gradient norm.
                       self.losses[bucket_id]]  # Loss for this batch.
      else:
        output_feed = [self.losses[bucket_id]]  # Loss for this batch.
        for l in xrange(decoder_size):  # Output logits.
          output_feed.append(self.outputs[bucket_id][l])
  
      outputs = session.run(output_feed, input_feed)
      if not forward_only:
        return outputs[1], outputs[2], None  # Gradient norm, loss, no outputs.
      else:
        return None, outputs[0], outputs[1:]  # No gradient norm, loss, outputs.
  
    def get_batch(self, data, bucket_id, PAD_ID=0, GO_ID=1, EOS_ID=2, UNK_ID=3):
      """ Get a random batch of data from the specified bucket, prepare for step.
  
      To feed data in step(..) it must be a list of batch-major vectors, while
      data here contains single length-major cases. So the main logic of this
      function is to re-index data cases to be in the proper format for feeding.
  
      Parameters
      ----------
      data : a tuple of size len(self.buckets) in which each element contains
          lists of pairs of input and output data that we use to create a batch.
      bucket_id : integer, which bucket to get the batch for.
      PAD_ID : int
          Index of Padding in vocabulary
      GO_ID : int
          Index of GO in vocabulary
      EOS_ID : int
          Index of End of sentence in vocabulary
      UNK_ID : int
          Index of Unknown word in vocabulary
  
      Returns
      -------
      The triple (encoder_inputs, decoder_inputs, target_weights) for
      the constructed batch that has the proper format to call step(...) later.
      """
      encoder_size, decoder_size = self.buckets[bucket_id]
      encoder_inputs, decoder_inputs = [], []
  
      # Get a random batch of encoder and decoder inputs from data,
      # pad them if needed, reverse encoder inputs and add GO to decoder.
      for _ in xrange(self.batch_size):
        encoder_input, decoder_input = random.choice(data[bucket_id])
  
        # Encoder inputs are padded and then reversed.
        encoder_pad = [PAD_ID] * (encoder_size - len(encoder_input))
        encoder_inputs.append(list(reversed(encoder_input + encoder_pad)))
  
        # Decoder inputs get an extra "GO" symbol, and are padded then.
        decoder_pad_size = decoder_size - len(decoder_input) - 1
        decoder_inputs.append([GO_ID] + decoder_input +
                              [PAD_ID] * decoder_pad_size)
  
      # Now we create batch-major vectors from the data selected above.
      batch_encoder_inputs, batch_decoder_inputs, batch_weights = [], [], []
  
      # Batch encoder inputs are just re-indexed encoder_inputs.
      for length_idx in xrange(encoder_size):
        batch_encoder_inputs.append(
            np.array([encoder_inputs[batch_idx][length_idx]
                      for batch_idx in xrange(self.batch_size)], dtype=np.int32))
  
      # Batch decoder inputs are re-indexed decoder_inputs, we create weights.
      for length_idx in xrange(decoder_size):
        batch_decoder_inputs.append(
            np.array([decoder_inputs[batch_idx][length_idx]
                      for batch_idx in xrange(self.batch_size)], dtype=np.int32))
  
        # Create target_weights to be 0 for targets that are padding.
        batch_weight = np.ones(self.batch_size, dtype=np.float32)
        for batch_idx in xrange(self.batch_size):
          # We set weight to 0 if the corresponding target is a PAD symbol.
          # The corresponding target is decoder_input shifted by 1 forward.
          if length_idx < decoder_size - 1:
            target = decoder_inputs[batch_idx][length_idx + 1]
          if length_idx == decoder_size - 1 or target == PAD_ID:
            batch_weight[batch_idx] = 0.0
        batch_weights.append(batch_weight)
      return batch_encoder_inputs, batch_decoder_inputs, batch_weights
  
  ## Developing or Untested
  class MaxoutLayer(Layer):
      """
      Waiting for contribution
  
      Single DenseLayer with Max-out behaviour, work well with Dropout.
  
      References
      -----------
      `Goodfellow (2013) Maxout Networks <http://arxiv.org/abs/1302.4389>`_
      """
      def __init__(
          self,
          layer = None,
          n_units = 100,
          name ='maxout_layer',
      ):
          Layer.__init__(self, name=name)
          self.inputs = layer.outputs
  
          print("  [TL] MaxoutLayer %s: %d" % (self.name, self.n_units))
          print("    Waiting for contribution")
          with tf.variable_scope(name) as vs:
              pass
              # W = tf.Variable(init.xavier_init(n_inputs=n_in, n_outputs=n_units, uniform=True), name='W')
              # b = tf.Variable(tf.zeros([n_units]), name='b')
  
          # self.outputs = act(tf.matmul(self.inputs, W) + b)
          # https://www.tensorflow.org/versions/r0.9/api_docs/python/array_ops.html#pack
          # http://stackoverflow.com/questions/34362193/how-to-explicitly-broadcast-a-tensor-to-match-anothers-shape-in-tensorflow
          # tf.concat tf.pack  tf.tile
  
          self.all_layers = list(layer.all_layers)
          self.all_params = list(layer.all_params)
          self.all_drop = dict(layer.all_drop)
          self.all_layers.extend( [self.outputs] )
          self.all_params.extend( [W, b] )
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  #
  