Python rnn.MultiRNNCell方法代碼示例

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def RNN(x, weights, biases):

    # reshape to [1, n_input]
    x = tf.reshape(x, [-1, n_input])

    # Generate a n_input-element sequence of inputs
    # (eg. [had] [a] [general] -> [20] [6] [33])
    x = tf.split(x, n_input, 1)

    # 2-layer LSTM, each layer has n_hidden units.
    # Average Accuracy= 95.20% at 50k iter
    rnn_cell = rnn.MultiRNNCell([rnn.BasicLSTMCell(n_hidden), rnn.BasicLSTMCell(n_hidden)])

    # 1-layer LSTM with n_hidden units but with lower accuracy.
    # Average Accuracy= 90.60% 50k iter
    # Uncomment line below to test but comment out the 2-layer rnn.MultiRNNCell above
    # rnn_cell = rnn.BasicLSTMCell(n_hidden)

    # generate prediction
    outputs, states = rnn.static_rnn(rnn_cell, x, dtype=tf.float32)

    # there are n_input outputs but
    # we only want the last output
    return tf.matmul(outputs[-1], weights['out']) + biases['out'] 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def __init__(self, state_size, num_layers, dropout_prob, base_cell):
        """Define the cell by composing/wrapping with tf.contrib.rnn functions.
        
        Args:
            state_size: number of units in the cell.
            num_layers: how many cells to include in the MultiRNNCell.
            dropout_prob: probability of a node being dropped.
            base_cell: (str) name of underling cell to use (e.g. 'GRUCell')
        """

        self._state_size = state_size
        self._num_layers = num_layers
        self._dropout_prob = dropout_prob
        self._base_cell = base_cell

        def single_cell():
            """Convert cell name (str) to class, and create it."""
            return getattr(tf.contrib.rnn, base_cell)(num_units=state_size)

        if num_layers == 1:
            self._cell = single_cell()
        else:
            self._cell = MultiRNNCell(
                [single_cell() for _ in range(num_layers)]) 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def build_lstm(self):
        def build_cell():
            cell = rnn.BasicLSTMCell(self._hidden_size, forget_bias=1.0, state_is_tuple=True)
            cell = rnn.DropoutWrapper(cell, output_keep_prob=self._keep_prob)
            return cell
        mul_cell = rnn.MultiRNNCell([build_cell() for _ in range(self._num_layer)], 
                                    state_is_tuple=True)
        self._init_state = mul_cell.zero_state(self._num_seq, dtype=tf.float32)
        outputs, self._final_state = tf.nn.dynamic_rnn(mul_cell, self._inputs, 
                                                       initial_state=self._init_state)
        outputs = tf.reshape(outputs, [-1, self._hidden_size])
        W = tf.Variable(tf.truncated_normal([self._hidden_size, self._corpus.word_num],
                                            stddev=0.1, dtype=tf.float32))
        bais = tf.Variable(tf.zeros([1, self._corpus.word_num], 
                                    dtype=tf.float32), dtype=tf.float32)
        self._prediction = tf.nn.softmax(tf.matmul(outputs, W) + bais) 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def create_model(self):
        features = tf.placeholder(tf.int32, [None, self.seq_len])
        embedding = tf.get_variable(
            'embedding', [self.vocab_size + 1, self.n_hidden], dtype=tf.float32)
        x = tf.cast(tf.nn.embedding_lookup(embedding, features), tf.float32)
        labels = tf.placeholder(tf.float32, [None, self.num_classes])
        
        stacked_lstm = rnn.MultiRNNCell(
            [rnn.BasicLSTMCell(self.n_hidden) for _ in range(2)])
        outputs, _ = tf.nn.dynamic_rnn(stacked_lstm, x, dtype=tf.float32)
        fc1 = tf.layers.dense(inputs=outputs[:, -1, :], units=128)
        pred = tf.layers.dense(inputs=fc1, units=self.num_classes)
        
        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred, labels=labels))
        train_op = self.optimizer.minimize(
            loss=loss,
            global_step=tf.train.get_global_step())
        
        correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(labels, 1))
        eval_metric_ops = tf.count_nonzero(correct_pred)
        
        return features, labels, train_op, eval_metric_ops, loss 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def create_model(self):
        features = tf.placeholder(tf.int32, [None, self.seq_len])
        embedding = tf.get_variable("embedding", [self.num_classes, 8])
        x = tf.nn.embedding_lookup(embedding, features)
        labels = tf.placeholder(tf.int32, [None, self.num_classes])
        
        stacked_lstm = rnn.MultiRNNCell(
            [rnn.BasicLSTMCell(self.n_hidden) for _ in range(2)])
        outputs, _ = tf.nn.dynamic_rnn(stacked_lstm, x, dtype=tf.float32)
        pred = tf.layers.dense(inputs=outputs[:,-1,:], units=self.num_classes)
        
        loss = tf.reduce_mean(
            tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred, labels=labels))
        train_op = self.optimizer.minimize(
            loss=loss,
            global_step=tf.train.get_global_step())

        correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(labels, 1))
        eval_metric_ops = tf.count_nonzero(correct_pred)

        return features, labels, train_op, eval_metric_ops, loss 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def __init__(self, nlayers, num_units, input_size=None,
                 use_peepholes=False, cell_clip=None, initializer=None,
                 num_proj=None, proj_clip=None, num_unit_shards=1,
                 num_proj_shards=1, forget_bias=1.0, state_is_tuple=True,
                 activation=tanh):

        super(MultiInputLSTM, self).__init__(num_units, input_size=None,
            use_peepholes=False,cell_clip=None, initializer=None, num_proj=None,
            proj_clip=None, num_unit_shards=1, num_proj_shards=1,
            forget_bias=1.0,state_is_tuple=True, activation=tanh)

        self.cell = super(MultiInputLSTM, self).__call__

        if nlayers > 1:
            self.cell = MultiRNNCell([self.cell] * nlayers)
        self.nlayers = nlayers 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def __build(self):
        w_fc_in = self.__weight_variable([self.nClasses+1, 128], 'w_fc_in')
        b_fc_in = self.__bias_variable([128], 'b_fc_in')
        
        w_fc_o = self.__weight_variable([self.rnn_size, 128], 'w_fc_o')
        b_fc_o = self.__bias_variable([128], 'b_fc_o')
                
        w_output_action = self.__weight_variable([128, self.nClasses], 'w_fc_in')
        b_output_action = self.__bias_variable([self.nClasses], 'b_fc_in')
        
        w_output_len = self.__weight_variable([128, 2], 'w_fc_in')
        b_output_len = self.__bias_variable([2], 'b_fc_in')
        
        x = tf.reshape(self.input_seq, [-1, self.nClasses+1])
        h1 = tf.nn.relu(tf.matmul(x, w_fc_in) + b_fc_in)
        h1 = tf.reshape(h1, [-1,self.max_seq_sz,128])
        #rnn
        h1 = tf.unstack(h1, axis=1)
        def get_cell():
            return rnn.GRUCell(self.rnn_size)   
        gru_cell = rnn.MultiRNNCell([get_cell() for _ in range(self.num_layers)])
        outputs, states = rnn.static_rnn(gru_cell, h1, dtype=tf.float32) 
        #fc_o
        h2 = tf.nn.relu(tf.matmul(outputs[-1], w_fc_o) + b_fc_o)
        #output
        output_label = tf.matmul(h2, w_output_action) + b_output_action
        output_len = tf.nn.relu(tf.matmul(h2, w_output_len) + b_output_len)
        #    
        self.prediction = tf.concat([output_label, output_len], 1)
        self.saver = tf.train.Saver(write_version=tf.train.SaverDef.V2, max_to_keep=100) 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def cell(self):
        """ Return the cell """
        with tf.variable_scope(self.variable_scope, reuse=self.reuse):
            cell = rnn.LSTMCell(self.num_units, reuse=self.reuse)

            if self.num_layers > 1:
                cell = rnn.MultiRNNCell([cell] * self.num_layers)

        return cell 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def construct_rnn_cell(num_units, cell_type='basic_rnn',
                       dropout_keep_probabilities=None):
  """Constructs cells, applies dropout and assembles a `MultiRNNCell`.

  The cell type chosen by DynamicRNNEstimator.__init__() is the same as
  returned by this function when called with the same arguments.

  Args:
    num_units: A single `int` or a list/tuple of `int`s. The size of the
      `RNNCell`s.
    cell_type: A string identifying the `RNNCell` type or a subclass of
      `RNNCell`.
    dropout_keep_probabilities: a list of dropout probabilities or `None`. If a
      list is given, it must have length `len(cell_type) + 1`.

  Returns:
    An initialized `RNNCell`.
  """
  if not isinstance(num_units, (list, tuple)):
    num_units = (num_units,)

  cells = [_get_single_cell(cell_type, n) for n in num_units]
  if dropout_keep_probabilities:
    cells = apply_dropout(cells, dropout_keep_probabilities)
  if len(cells) == 1:
    return cells[0]
  return contrib_rnn.MultiRNNCell(cells) 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def _to_rnn_cell(cell_or_type, num_units, num_layers):
  """Constructs and return an `RNNCell`.

  Args:
    cell_or_type: Either a string identifying the `RNNCell` type, a subclass of
      `RNNCell` or an instance of an `RNNCell`.
    num_units: The number of units in the `RNNCell`.
    num_layers: The number of layers in the RNN.
  Returns:
    An initialized `RNNCell`.
  Raises:
    ValueError: `cell_or_type` is an invalid `RNNCell` name.
    TypeError: `cell_or_type` is not a string or a subclass of `RNNCell`.
  """
  if isinstance(cell_or_type, contrib_rnn.RNNCell):
    return cell_or_type
  if isinstance(cell_or_type, str):
    cell_or_type = _CELL_TYPES.get(cell_or_type)
    if cell_or_type is None:
      raise ValueError('The supported cell types are {}; got {}'.format(
          list(_CELL_TYPES.keys()), cell_or_type))
  if not issubclass(cell_or_type, contrib_rnn.RNNCell):
    raise TypeError(
        'cell_or_type must be a subclass of RNNCell or one of {}.'.format(
            list(_CELL_TYPES.keys())))
  cell = cell_or_type(num_units=num_units)
  if num_layers > 1:
    cell = contrib_rnn.MultiRNNCell(
        [cell] * num_layers, state_is_tuple=True)
  return cell 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def cell_create(self,scope_name):
         with tf.variable_scope(scope_name):
             if self.cell_type == 'tanh':
                 cells = rnn.MultiRNNCell([rnn.BasicRNNCell(self.n_hidden[i]) for i in range(self.n_layers)], state_is_tuple=True)
             elif self.cell_type == 'LSTM': 
                 cells = rnn.MultiRNNCell([rnn.BasicLSTMCell(self.n_hidden[i]) for i in range(self.n_layers)], state_is_tuple=True)
             elif self.cell_type == 'GRU':
                 cells = rnn.MultiRNNCell([rnn.GRUCell(self.n_hidden[i]) for i in range(self.n_layers)], state_is_tuple=True)
             elif self.cell_type == 'LSTMP':
                 cells = rnn.MultiRNNCell([rnn.LSTMCell(self.n_hidden[i]) for i in range(self.n_layers)], state_is_tuple=True)
             cells = rnn.DropoutWrapper(cells, input_keep_prob=self.dropout_ph,output_keep_prob=self.dropout_ph) 
         return cells 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def getLayeredCell(layer_size, num_units, input_keep_prob,
        output_keep_prob=1.0):
    return rnn.MultiRNNCell([rnn.DropoutWrapper(rnn.BasicLSTMCell(num_units),
        input_keep_prob, output_keep_prob) for i in range(layer_size)]) 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def test_multi_rnn():
    """
    Test a stacked LSTM with nested tuple state.
    """
    def make_cell():
        return MultiRNNCell([LSTMCell(16), LSTMCell(32)])

    run_ac_test(partial(RNNCellAC, make_cell=make_cell)) 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def define_rnn_cell(cell_class, num_units, num_layers=1, keep_prob=1.0,
                    input_keep_prob=None, output_keep_prob=None):
    if input_keep_prob is None:
        input_keep_prob = keep_prob
    if output_keep_prob is None:
        output_keep_prob = keep_prob

    cells = []
    for _ in range(num_layers):
        if cell_class == 'GRU':
            cell = GRUCell(num_units=num_units)
        elif cell_class == 'LSTM':
            cell = LSTMCell(num_units=num_units)
        else:
            cell = RNNCell(num_units=num_units)

        if keep_prob < 1.0:
            cell = DropoutWrapper(cell=cell, input_keep_prob=input_keep_prob, output_keep_prob=output_keep_prob)
        cells.append(cell)

    if len(cells) > 1:
        final_cell = MultiRNNCell(cells)
    else:
        final_cell = cells[0]

    return final_cell 

# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import MultiRNNCell [as 別名]
def _make_encoder(self):
        """Create the encoder"""
        inputs = layers.embed_sequence(
            self.X,
            vocab_size=self.vocab_size,
            embed_dim=self.embed_dim,
            scope='embed')

        # Project to correct dimensions
        # b/c the bidirectional RNN's forward and backward
        # outputs are concatenated, the size will be 2x,
        # so halve the hidden sizes here to compensate
        inputs = tf.layers.dense(inputs, self.hidden_size//2)

        cell_fw = rnn.MultiRNNCell([
            self._make_cell(self.hidden_size//2) for _ in range(self.depth)
        ])
        cell_bw = rnn.MultiRNNCell([
            self._make_cell(self.hidden_size//2) for _ in range(self.depth)
        ])
        encoder_outputs, encoder_final_state = tf.nn.bidirectional_dynamic_rnn(
            cell_fw=cell_fw, cell_bw=cell_bw, sequence_length=self.sequence_length,
            inputs=inputs, dtype=tf.float32)

        # Concat forward and backward outputs
        encoder_outputs = tf.concat(encoder_outputs, 2)

        # Concat forward and backward layer states
        encoder_fw_states, encoder_bw_states = encoder_final_state
        encoder_final_state = []
        for fw, bw in zip(encoder_fw_states, encoder_bw_states):
            c = tf.concat([fw.c, bw.c], 1)
            h = tf.concat([fw.h, bw.h], 1)
            encoder_final_state.append(rnn.LSTMStateTuple(c=c, h=h))
        return encoder_outputs, encoder_final_state