# -*- coding: utf-8 -*-
import inspect
import tensorflow as tf
from .. import _logging as logging
from .core import *
__all__ = [
'RNNLayer',
'BiRNNLayer',
'ConvRNNCell',
'BasicConvLSTMCell',
'ConvLSTMLayer',
'advanced_indexing_op',
'retrieve_seq_length_op',
'retrieve_seq_length_op2',
'retrieve_seq_length_op3',
'target_mask_op',
'DynamicRNNLayer',
'BiDynamicRNNLayer',
'Seq2Seq',
]
[docs]class RNNLayer(Layer):
"""
The :class:`RNNLayer` class is a fixed length recurrent layer for implementing vanilla RNN,
LSTM, GRU and etc.
Parameters
----------
layer : :class:`Layer`
Previous layer.
cell_fn : TensorFlow cell function
A TensorFlow core RNN cell
- See `RNN Cells in TensorFlow <https://www.tensorflow.org/api_docs/python/>`__
- Note TF1.0+ and TF1.0- are different
cell_init_args : dictionary
The arguments for the cell function.
n_hidden : int
The number of hidden units in the layer.
initializer : initializer
The initializer for initializing the model parameters.
n_steps : int
The fixed sequence length.
initial_state : None or RNN State
If None, `initial_state` is zero state.
return_last : boolean
Whether return last output or all outputs in each step.
- 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 stack more RNNs on this layer, set to False.
return_seq_2d : boolean
Only consider this argument when `return_last` is `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 : str
A unique layer name.
Attributes
----------
outputs : Tensor
The output of this layer.
final_state : Tensor or StateTuple
The finial state of this layer.
- When `state_is_tuple` is `False`, it is the final hidden and cell states, `states.get_shape() = [?, 2 * n_hidden]`.
- When `state_is_tuple` is `True`, it stores two elements: `(c, h)`.
- In practice, you can get the final state after each iteration during training, then feed it to the initial state of next iteration.
initial_state : Tensor or StateTuple
The initial state of this layer.
- In practice, you can set your state at the begining of each epoch or iteration according to your training procedure.
batch_size : int or Tensor
It is an integer, if it is able to compute the `batch_size`; otherwise, tensor for dynamic batch size.
Examples
--------
- For synced sequence input and output, see `PTB example <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_ptb_lstm_state_is_tuple.py>`__
- For encoding see below.
>>> batch_size = 32
>>> num_steps = 5
>>> vocab_size = 3000
>>> hidden_size = 256
>>> keep_prob = 0.8
>>> is_train = True
>>> input_data = tf.placeholder(tf.int32, [batch_size, num_steps])
>>> net = tl.layers.EmbeddingInputlayer(inputs=input_data, vocabulary_size=vocab_size,
... embedding_size=hidden_size, name='embed')
>>> net = tl.layers.DropoutLayer(net, keep=keep_prob, is_fix=True, is_train=is_train, name='drop1')
>>> net = tl.layers.RNNLayer(net, cell_fn=tf.contrib.rnn.BasicLSTMCell,
... n_hidden=hidden_size, n_steps=num_steps, return_last=False, name='lstm1')
>>> net = tl.layers.DropoutLayer(net, keep=keep_prob, is_fix=True, is_train=is_train, name='drop2')
>>> net = tl.layers.RNNLayer(net, cell_fn=tf.contrib.rnn.BasicLSTMCell,
... n_hidden=hidden_size, n_steps=num_steps, return_last=True, name='lstm2')
>>> net = tl.layers.DropoutLayer(net, keep=keep_prob, is_fix=True, is_train=is_train, name='drop3')
>>> net = tl.layers.DenseLayer(net, n_units=vocab_size, name='output')
- For CNN+LSTM
>>> image_size = 100
>>> batch_size = 10
>>> num_steps = 5
>>> x = tf.placeholder(tf.float32, shape=[batch_size, image_size, image_size, 1])
>>> net = tl.layers.InputLayer(x, name='in')
>>> net = tl.layers.Conv2d(net, 32, (5, 5), (2, 2), tf.nn.relu, name='cnn1')
>>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), name='pool1')
>>> net = tl.layers.Conv2d(net, 10, (5, 5), (2, 2), tf.nn.relu, name='cnn2')
>>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), name='pool2')
>>> net = tl.layers.FlattenLayer(net, name='flatten')
>>> net = tl.layers.ReshapeLayer(net, shape=[-1, num_steps, int(net.outputs._shape[-1])])
>>> rnn = tl.layers.RNNLayer(net, cell_fn=tf.contrib.rnn.BasicLSTMCell, n_hidden=200, n_steps=num_steps, return_last=False, return_seq_2d=True, name='rnn')
>>> net = tl.layers.DenseLayer(rnn, 3, name='out')
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,
prev_layer,
cell_fn,
cell_init_args=None,
n_hidden=100,
initializer=tf.random_uniform_initializer(-0.1, 0.1),
n_steps=5,
initial_state=None,
return_last=False,
return_seq_2d=False,
name='rnn',
):
if cell_init_args is None:
cell_init_args = {}
Layer.__init__(self, prev_layer=prev_layer, 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 Exception:
logging.warning('pop state_is_tuple fails.')
self.inputs = prev_layer.outputs
logging.info("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 Exception:
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
logging.info(" RNN batch_size (concurrent processes): %d" % batch_size)
else:
from tensorflow.python.ops import array_ops
batch_size = array_ops.shape(self.inputs)[0]
logging.info(" 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 = []
if 'reuse' in inspect.getargspec(cell_fn.__init__).args:
self.cell = cell = cell_fn(num_units=n_hidden, reuse=tf.get_variable_scope().reuse, **cell_init_args)
else:
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=LayersConfig.tf_dtype) #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)
logging.info(" 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 Exception: # 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 Exception: # 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)
self.all_layers.append(self.outputs)
self.all_params.extend(rnn_variables)
[docs]class BiRNNLayer(Layer):
"""
The :class:`BiRNNLayer` class is a fixed length Bidirectional recurrent layer.
Parameters
----------
layer : :class:`Layer`
Previous layer.
cell_fn : TensorFlow cell function
A TensorFlow core RNN cell.
- See `RNN Cells in TensorFlow <https://www.tensorflow.org/api_docs/python/>`__.
- Note TF1.0+ and TF1.0- are different.
cell_init_args : dictionary or None
The arguments for the cell function.
n_hidden : int
The number of hidden units in the layer.
initializer : initializer
The initializer for initializing the model parameters.
n_steps : int
The fixed 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 or int
The input and output keep probability (input_keep_prob, output_keep_prob).
If one int, input and output keep probability are the same.
n_layer : int
The number of RNN layers, default is 1.
return_last : boolean
Whether return last output or all outputs in each step.
- 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 stack more RNNs on this layer, set to False.
return_seq_2d : boolean
Only consider this argument when `return_last` is `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 : str
A unique layer name.
Attributes
----------
outputs : tensor
The output of this layer.
fw(bw)_final_state : tensor or StateTuple
The finial state of this layer.
- When `state_is_tuple` is `False`, it is the final hidden and cell states, `states.get_shape() = [?, 2 * n_hidden]`.
- When `state_is_tuple` is `True`, it stores two elements: `(c, h)`.
- In practice, 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 : tensor or StateTuple
The initial state of this layer.
- In practice, you can set your state at the begining of each epoch or iteration according to your training procedure.
batch_size : int or tensor
It is an integer, if it is able to compute the `batch_size`; otherwise, tensor for dynamic batch size.
Notes
-----
Input dimension should be rank 3 : [batch_size, n_steps, n_features]. If not, 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,
prev_layer,
cell_fn,
cell_init_args=None,
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',
):
if cell_init_args is None:
cell_init_args = {'state_is_tuple': True} # 'use_peepholes': True,
Layer.__init__(self, prev_layer=prev_layer, 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 Exception:
logging.warning("pop state_is_tuple fails.")
self.inputs = prev_layer.outputs
logging.info("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
logging.info(" 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]
logging.info(" 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 Exception:
raise Exception("RNN : Input dimension should be rank 3 : [batch_size, n_steps, n_features]")
with tf.variable_scope(name, initializer=initializer) as vs:
rnn_creator = lambda: cell_fn(num_units=n_hidden, **cell_init_args)
# Apply dropout
if dropout:
if isinstance(dropout, (tuple, list)): # 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 Exception:
DropoutWrapper_fn = tf.nn.rnn_cell.DropoutWrapper
cell_creator = lambda is_last=True: \
DropoutWrapper_fn(rnn_creator(),
input_keep_prob=in_keep_prob,
output_keep_prob=out_keep_prob if is_last else 1.0)
else:
cell_creator = rnn_creator
self.fw_cell = cell_creator()
self.bw_cell = cell_creator()
# Apply multiple layers
if n_layer > 1:
try: # TF1.0
MultiRNNCell_fn = tf.contrib.rnn.MultiRNNCell
except Exception:
MultiRNNCell_fn = tf.nn.rnn_cell.MultiRNNCell
if dropout:
try:
self.fw_cell = MultiRNNCell_fn([cell_creator(is_last=i == n_layer - 1) for i in range(n_layer)], state_is_tuple=True)
self.bw_cell = MultiRNNCell_fn([cell_creator(is_last=i == n_layer - 1) for i in range(n_layer)], state_is_tuple=True)
except Exception:
self.fw_cell = MultiRNNCell_fn([cell_creator(is_last=i == n_layer - 1) for i in range(n_layer)])
self.bw_cell = MultiRNNCell_fn([cell_creator(is_last=i == n_layer - 1) for i in range(n_layer)])
else:
try:
self.fw_cell = MultiRNNCell_fn([cell_creator() for _ in range(n_layer)], state_is_tuple=True)
self.bw_cell = MultiRNNCell_fn([cell_creator() for _ in range(n_layer)], state_is_tuple=True)
except Exception:
self.fw_cell = MultiRNNCell_fn([cell_creator() for _ in range(n_layer)])
self.bw_cell = MultiRNNCell_fn([cell_creator() for _ in range(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=LayersConfig.tf_dtype) # 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=LayersConfig.tf_dtype) # 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 Exception: # TF0.12
list_rnn_inputs = tf.unpack(self.inputs, axis=1)
try: # TF1.0
bidirectional_rnn_fn = tf.contrib.rnn.static_bidirectional_rnn
except Exception:
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:
raise Exception("Do not support return_last at the moment.")
# 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 Exception: # 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 Exception: # 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)
logging.info(" 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.append(self.outputs)
self.all_params.extend(rnn_variables)
[docs]class ConvRNNCell(object):
"""Abstract object representing an Convolutional RNN Cell."""
def __call__(self, inputs, state, scope=None):
"""Run this RNN cell on inputs, starting from the given state."""
raise NotImplementedError("Abstract method")
@property
def state_size(self):
"""size(s) of state(s) used by this cell."""
raise NotImplementedError("Abstract method")
@property
def output_size(self):
"""Integer or TensorShape: size of outputs produced by this cell."""
raise NotImplementedError("Abstract method")
def zero_state(self, batch_size, dtype=LayersConfig.tf_dtype):
"""Return zero-filled state tensor(s).
Args:
batch_size: int, float, or unit Tensor representing the batch size.
Returns:
tensor of shape '[batch_size x shape[0] x shape[1] x num_features]
filled with zeros
"""
shape = self.shape
num_features = self.num_features
# TODO : TypeError: 'NoneType' object is not subscriptable
zeros = tf.zeros([batch_size, shape[0], shape[1], num_features * 2], dtype=dtype)
return zeros
[docs]class BasicConvLSTMCell(ConvRNNCell):
"""Basic Conv LSTM recurrent network cell.
Parameters
-----------
shape : tuple of int
The height and width of the cell.
filter_size : tuple of int
The height and width of the filter
num_features : int
The hidden size of the cell
forget_bias : float
The bias added to forget gates (see above).
input_size : int
Deprecated and unused.
state_is_tuple : boolen
If True, accepted and returned states are 2-tuples of the `c_state` and `m_state`.
If False, they are concatenated along the column axis. The latter behavior will soon be deprecated.
act : activation function
The activation function of this layer, tanh as default.
"""
def __init__(self, shape, filter_size, num_features, forget_bias=1.0, input_size=None, state_is_tuple=False, act=tf.nn.tanh):
"""Initialize the basic Conv LSTM cell."""
# if not state_is_tuple:
# logging.warn("%s: Using a concatenated state is slower and will soon be "
# "deprecated. Use state_is_tuple=True.", self)
if input_size is not None:
logging.warn("%s: The input_size parameter is deprecated.", self)
self.shape = shape
self.filter_size = filter_size
self.num_features = num_features
self._forget_bias = forget_bias
self._state_is_tuple = state_is_tuple
self._activation = act
@property
def state_size(self):
"""State size of the LSTMStateTuple."""
return (LSTMStateTuple(self._num_units, self._num_units) if self._state_is_tuple else 2 * self._num_units)
@property
def output_size(self):
"""Number of units in outputs."""
return self._num_units
def __call__(self, inputs, state, scope=None):
"""Long short-term memory cell (LSTM)."""
with tf.variable_scope(scope or type(self).__name__): # "BasicLSTMCell"
# Parameters of gates are concatenated into one multiply for efficiency.
if self._state_is_tuple:
c, h = state
else:
# print state
# c, h = tf.split(3, 2, state)
c, h = tf.split(state, 2, 3)
concat = _conv_linear([inputs, h], self.filter_size, self.num_features * 4, True)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
# i, j, f, o = tf.split(3, 4, concat)
i, j, f, o = tf.split(concat, 4, 3)
new_c = (c * tf.nn.sigmoid(f + self._forget_bias) + tf.nn.sigmoid(i) * self._activation(j))
new_h = self._activation(new_c) * tf.nn.sigmoid(o)
if self._state_is_tuple:
new_state = LSTMStateTuple(new_c, new_h)
else:
new_state = tf.concat([new_c, new_h], 3)
return new_h, new_state
def _conv_linear(args, filter_size, num_features, bias, bias_start=0.0, scope=None):
"""convolution:
Parameters
----------
args : tensor
4D Tensor or a list of 4D, batch x n, Tensors.
filter_size : tuple of int
Filter height and width.
num_features : int
Nnumber of features.
bias_start : float
Starting value to initialize the bias; 0 by default.
scope : VariableScope
For the created subgraph; defaults to "Linear".
Returns
--------
- A 4D Tensor with shape [batch h w num_features]
Raises
-------
- ValueError : if some of the arguments has unspecified or wrong shape.
"""
# Calculate the total size of arguments on dimension 1.
total_arg_size_depth = 0
shapes = [a.get_shape().as_list() for a in args]
for shape in shapes:
if len(shape) != 4:
raise ValueError("Linear is expecting 4D arguments: %s" % str(shapes))
if not shape[3]:
raise ValueError("Linear expects shape[4] of arguments: %s" % str(shapes))
else:
total_arg_size_depth += shape[3]
dtype = [a.dtype for a in args][0]
# Now the computation.
with tf.variable_scope(scope or "Conv"):
matrix = tf.get_variable("Matrix", [filter_size[0], filter_size[1], total_arg_size_depth, num_features], dtype=dtype)
if len(args) == 1:
res = tf.nn.conv2d(args[0], matrix, strides=[1, 1, 1, 1], padding='SAME')
else:
res = tf.nn.conv2d(tf.concat(args, 3), matrix, strides=[1, 1, 1, 1], padding='SAME')
if not bias:
return res
bias_term = tf.get_variable("Bias", [num_features], dtype=dtype, initializer=tf.constant_initializer(bias_start, dtype=dtype))
return res + bias_term
[docs]class ConvLSTMLayer(Layer):
"""A fixed length Convolutional LSTM layer.
See this `paper <https://arxiv.org/abs/1506.04214>`__ .
Parameters
----------
layer : :class:`Layer`
Previous layer
cell_shape : tuple of int
The shape of each cell width * height
filter_size : tuple of int
The size of filter width * height
cell_fn : a convolutional RNN cell
Cell function like :class:`BasicConvLSTMCell`
feature_map : int
The number of feature map in the layer.
initializer : initializer
The initializer for initializing the parameters.
n_steps : int
The sequence length.
initial_state : None or ConvLSTM State
If None, `initial_state` is zero state.
return_last : boolean
Whether return last output or all outputs in each step.
- 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 stack more RNNs on this layer, set to False.
return_seq_2d : boolean
Only consider this argument when `return_last` is `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 : str
A unique layer name.
Attributes
----------
outputs : tensor
The output of this RNN. return_last = False, outputs = all cell_output, which is the hidden state.
cell_output.get_shape() = (?, h, w, c])
final_state : tensor or StateTuple
The finial state of this layer.
- When state_is_tuple = False, it is the final hidden and cell states,
- When state_is_tuple = True, You can get the final state after each iteration during training, then feed it to the initial state of next iteration.
initial_state : tensor or StateTuple
It is the initial state of this ConvLSTM layer, you can use it to initialize
your state at the beginning 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 ``?``.
"""
def __init__(
self,
prev_layer,
cell_shape=None,
feature_map=1,
filter_size=(3, 3),
cell_fn=BasicConvLSTMCell,
initializer=tf.random_uniform_initializer(-0.1, 0.1),
n_steps=5,
initial_state=None,
return_last=False,
return_seq_2d=False,
name='convlstm',
):
Layer.__init__(self, prev_layer=prev_layer, name=name)
self.inputs = prev_layer.outputs
logging.info("ConvLSTMLayer %s: feature_map:%d, n_steps:%d, "
"in_dim:%d %s, cell_fn:%s " % (self.name, feature_map, 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 5 [batch_size, n_steps(max), h, w, c]
try:
self.inputs.get_shape().with_rank(5)
except Exception:
raise Exception("RNN : Input dimension should be rank 5 : [batch_size, n_steps, input_x, " "input_y, feature_map]")
fixed_batch_size = self.inputs.get_shape().with_rank_at_least(1)[0]
if fixed_batch_size.value:
batch_size = fixed_batch_size.value
logging.info(" RNN batch_size (concurrent processes): %d" % batch_size)
else:
from tensorflow.python.ops import array_ops
batch_size = array_ops.shape(self.inputs)[0]
logging.info(" non specified batch_size, uses a tensor instead.")
self.batch_size = batch_size
outputs = []
self.cell = cell = cell_fn(shape=cell_shape, filter_size=filter_size, num_features=feature_map)
if initial_state is None:
self.initial_state = cell.zero_state(batch_size, dtype=LayersConfig.tf_dtype) # 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)
logging.info(" 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
# 4D Tensor [n_example, h, w, c]
self.outputs = tf.reshape(tf.concat(outputs, 1), [-1, cell_shape[0] * cell_shape[1] * feature_map])
else:
# <akara>: stack more RNN layer after that
# 5D Tensor [n_example/n_steps, n_steps, h, w, c]
self.outputs = tf.reshape(tf.concat(outputs, 1), [-1, n_steps, cell_shape[0], cell_shape[1], feature_map])
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)
self.all_layers.append(self.outputs)
self.all_params.extend(rnn_variables)
# Advanced Ops for Dynamic RNN
[docs]def advanced_indexing_op(inputs, 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
-----------
inputs : tensor for data
With shape of [batch_size, n_step(max), n_features]
index : tensor for indexing
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(inputs)[0]
# max_length = int(inputs.get_shape()[1]) # for fixed length rnn, length is given
max_length = tf.shape(inputs)[1] # for dynamic_rnn, length is unknown
dim_size = int(inputs.get_shape()[2])
index = tf.range(0, batch_size) * max_length + (index - 1)
flat = tf.reshape(inputs, [-1, dim_size])
relevant = tf.gather(flat, index)
return relevant
[docs]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]]]
>>> print(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
[docs]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)
[docs]def retrieve_seq_length_op3(data, pad_val=0): # HangSheng: return tensor for sequence length, if input is tf.string
"""Return tensor for sequence length, if input is ``tf.string``.
"""
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))
[docs]def target_mask_op(data, pad_val=0): # HangSheng: return tensor for mask,if input is tf.string
"""Return tensor for mask, if input is ``tf.string``.
"""
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))
[docs]class DynamicRNNLayer(Layer):
"""
The :class:`DynamicRNNLayer` class is a dynamic recurrent layer, see ``tf.nn.dynamic_rnn``.
Parameters
----------
layer : :class:`Layer`
Previous layer
cell_fn : TensorFlow cell function
A TensorFlow core RNN cell
- See `RNN Cells in TensorFlow <https://www.tensorflow.org/api_docs/python/>`__
- Note TF1.0+ and TF1.0- are different
cell_init_args : dictionary or None
The arguments for the cell function.
n_hidden : int
The number of hidden units in the layer.
initializer : initializer
The initializer for initializing the parameters.
sequence_length : 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 or int
The input and output keep probability (input_keep_prob, output_keep_prob).
- If one int, input and output keep probability are the same.
n_layer : int
The number of RNN layers, default is 1.
return_last : boolean or None
Whether return last output or all outputs in each step.
- 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 stack more RNNs on this layer, set to False.
return_seq_2d : boolean
Only consider this argument when `return_last` is `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.
dynamic_rnn_init_args : dictionary
The arguments for ``tf.nn.dynamic_rnn``.
name : str
A unique layer name.
Attributes
------------
outputs : tensor
The output of this layer.
final_state : tensor or StateTuple
The finial state of this layer.
- When `state_is_tuple` is `False`, it is the final hidden and cell states, `states.get_shape() = [?, 2 * n_hidden]`.
- When `state_is_tuple` is `True`, it stores two elements: `(c, h)`.
- In practice, you can get the final state after each iteration during training, then feed it to the initial state of next iteration.
initial_state : tensor or StateTuple
The initial state of this layer.
- In practice, you can set your state at the begining of each epoch or iteration according to your training procedure.
batch_size : int or tensor
It is an integer, if it is able to compute the `batch_size`; otherwise, tensor for dynamic batch size.
sequence_length : a tensor or array
The sequence lengths computed by Advanced Opt or the given sequence lengths, [batch_size]
Notes
-----
Input dimension should be rank 3 : [batch_size, n_steps(max), n_features], if no, please see :class:`ReshapeLayer`.
Examples
--------
Synced sequence input and output, for loss function see ``tl.cost.cross_entropy_seq_with_mask``.
>>> input_seqs = tf.placeholder(dtype=tf.int64, shape=[batch_size, None], name="input")
>>> net = tl.layers.EmbeddingInputlayer(
... inputs=input_seqs,
... vocabulary_size=vocab_size,
... embedding_size=embedding_size,
... name='embedding')
>>> net = tl.layers.DynamicRNNLayer(net,
... cell_fn=tf.contrib.rnn.BasicLSTMCell, # for TF0.2 use tf.nn.rnn_cell.BasicLSTMCell,
... n_hidden=embedding_size,
... dropout=(0.7 if is_train else None),
... sequence_length=tl.layers.retrieve_seq_length_op2(input_seqs),
... return_last=False, # for encoder, set to True
... return_seq_2d=True, # stack denselayer or compute cost after it
... name='dynamicrnn')
... net = tl.layers.DenseLayer(net, n_units=vocab_size, 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,
prev_layer,
cell_fn, #tf.nn.rnn_cell.LSTMCell,
cell_init_args=None,
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=None,
return_seq_2d=False,
dynamic_rnn_init_args=None,
name='dyrnn',
):
if dynamic_rnn_init_args is None:
dynamic_rnn_init_args = {}
if cell_init_args is None:
cell_init_args = {'state_is_tuple': True}
if return_last is None:
return_last = True
Layer.__init__(self, prev_layer=prev_layer, 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 Exception:
logging.warning("pop state_is_tuple fails.")
self.inputs = prev_layer.outputs
logging.info("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 Exception:
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
logging.info(" batch_size (concurrent processes): %d" % batch_size)
else:
from tensorflow.python.ops import array_ops
batch_size = array_ops.shape(self.inputs)[0]
logging.info(" 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
rnn_creator = lambda: cell_fn(num_units=n_hidden, **cell_init_args)
# Apply dropout
if dropout:
if isinstance(dropout, (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 Exception:
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)
cell_creator = lambda is_last=True: \
DropoutWrapper_fn(rnn_creator(),
input_keep_prob=in_keep_prob,
output_keep_prob=out_keep_prob if is_last else 1.0)
else:
cell_creator = rnn_creator
self.cell = cell_creator()
# Apply multiple layers
if n_layer > 1:
try:
MultiRNNCell_fn = tf.contrib.rnn.MultiRNNCell
except Exception:
MultiRNNCell_fn = tf.nn.rnn_cell.MultiRNNCell
# cell_instance_fn2=cell_instance_fn # HanSheng
if dropout:
try:
# cell_instance_fn=lambda: MultiRNNCell_fn([cell_instance_fn2() for _ in range(n_layer)], state_is_tuple=True) # HanSheng
self.cell = MultiRNNCell_fn([cell_creator(is_last=i == n_layer - 1) for i in range(n_layer)], state_is_tuple=True)
except Exception: # when GRU
# cell_instance_fn=lambda: MultiRNNCell_fn([cell_instance_fn2() for _ in range(n_layer)]) # HanSheng
self.cell = MultiRNNCell_fn([cell_creator(is_last=i == n_layer - 1) for i in range(n_layer)])
else:
try:
self.cell = MultiRNNCell_fn([cell_creator() for _ in range(n_layer)], state_is_tuple=True)
except Exception: # when GRU
self.cell = MultiRNNCell_fn([cell_creator() for _ in range(n_layer)])
# self.cell=cell_instance_fn() # HanSheng
# Initialize initial_state
if initial_state is None:
self.initial_state = self.cell.zero_state(batch_size, dtype=LayersConfig.tf_dtype) # 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 Exception: # 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)
# logging.info(" 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 Exception: # 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 Exception: # 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.append(self.outputs)
self.all_params.extend(rnn_variables)
[docs]class BiDynamicRNNLayer(Layer):
"""
The :class:`BiDynamicRNNLayer` class is a RNN layer, you can implement vanilla RNN,
LSTM and GRU with it.
Parameters
----------
layer : :class:`Layer`
Previous layer.
cell_fn : TensorFlow cell function
A TensorFlow core RNN cell
- See `RNN Cells in TensorFlow <https://www.tensorflow.org/api_docs/python/>`__.
- Note TF1.0+ and TF1.0- are different.
cell_init_args : dictionary
The arguments for the cell initializer.
n_hidden : int
The number of hidden units in the layer.
initializer : initializer
The initializer for initializing the parameters.
sequence_length : 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 or int
The input and output keep probability (input_keep_prob, output_keep_prob).
- If one int, input and output keep probability are the same.
n_layer : int
The number of RNN layers, default is 1.
return_last : boolean
Whether return last output or all outputs in each step.
- 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 stack more RNNs on this layer, set to False.
return_seq_2d : boolean
Only consider this argument when `return_last` is `False`
- If True, return 2D Tensor [n_example, 2 * n_hidden], for stacking DenseLayer after it.
- If False, return 3D Tensor [n_example/n_steps, n_steps, 2 * n_hidden], for stacking multiple RNN after it.
dynamic_rnn_init_args : dictionary
The arguments for ``tf.nn.bidirectional_dynamic_rnn``.
name : str
A unique layer name.
Attributes
-----------------------
outputs : tensor
The output of this layer. (?, 2 * n_hidden)
fw(bw)_final_state : tensor or StateTuple
The finial state of this layer.
- When `state_is_tuple` is `False`, it is the final hidden and cell states, `states.get_shape() = [?, 2 * n_hidden]`.
- When `state_is_tuple` is `True`, it stores two elements: `(c, h)`.
- In practice, 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 : tensor or StateTuple
The initial state of this layer.
- In practice, you can set your state at the begining of each epoch or iteration according to your training procedure.
batch_size : int or tensor
It is an integer, if it is able to compute the `batch_size`; otherwise, tensor for dynamic batch size.
sequence_length : a tensor or array
The sequence lengths computed by Advanced Opt or the given sequence lengths, [batch_size].
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,
prev_layer,
cell_fn, #tf.nn.rnn_cell.LSTMCell,
cell_init_args=None,
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=None,
name='bi_dyrnn_layer',
):
if cell_init_args is None:
cell_init_args = {'state_is_tuple': True}
if dynamic_rnn_init_args is None:
dynamic_rnn_init_args = {}
Layer.__init__(self, prev_layer=prev_layer, 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 Exception:
logging.warning("pop state_is_tuple fails.")
self.inputs = prev_layer.outputs
logging.info("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 Exception:
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
logging.info(" batch_size (concurrent processes): %d" % batch_size)
else:
from tensorflow.python.ops import array_ops
batch_size = array_ops.shape(self.inputs)[0]
logging.info(" 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
rnn_creator = lambda: cell_fn(num_units=n_hidden, **cell_init_args)
# Apply dropout
if dropout:
if isinstance(dropout, (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 Exception:
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)
cell_creator = lambda is_last=True: \
DropoutWrapper_fn(rnn_creator(),
input_keep_prob=in_keep_prob,
output_keep_prob=out_keep_prob if is_last else 1.0)
else:
cell_creator = rnn_creator
# if dropout:
# self.fw_cell = DropoutWrapper_fn(self.fw_cell, input_keep_prob=1.0, output_keep_prob=out_keep_prob)
# self.bw_cell = DropoutWrapper_fn(self.bw_cell, input_keep_prob=1.0, output_keep_prob=out_keep_prob)
# self.fw_cell=cell_instance_fn()
# self.bw_cell=cell_instance_fn()
# Initial state of RNN
self.fw_initial_state = fw_initial_state
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 Exception: # TF0.12
sequence_length = retrieve_seq_length_op(self.inputs if isinstance(self.inputs, tf.Tensor) else tf.pack(self.inputs))
if n_layer > 1:
if dropout:
self.fw_cell = [cell_creator(is_last=i == n_layer - 1) for i in range(n_layer)]
self.bw_cell = [cell_creator(is_last=i == n_layer - 1) for i in range(n_layer)]
else:
self.fw_cell = [cell_creator() for _ in range(n_layer)]
self.bw_cell = [cell_creator() for _ in range(n_layer)]
from tensorflow.contrib.rnn import stack_bidirectional_dynamic_rnn
outputs, states_fw, states_bw = stack_bidirectional_dynamic_rnn(
cells_fw=self.fw_cell,
cells_bw=self.bw_cell,
inputs=self.inputs,
sequence_length=sequence_length,
initial_states_fw=self.fw_initial_state,
initial_states_bw=self.bw_initial_state,
dtype=LayersConfig.tf_dtype,
**dynamic_rnn_init_args)
else:
self.fw_cell = cell_creator()
self.bw_cell = cell_creator()
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,
dtype=LayersConfig.tf_dtype,
**dynamic_rnn_init_args)
rnn_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
logging.info(" n_params : %d" % (len(rnn_variables)))
# Manage the outputs
try: # TF1.0
outputs = tf.concat(outputs, 2)
except Exception: # TF0.12
outputs = tf.concat(2, outputs)
if return_last:
# [batch_size, 2 * n_hidden]
raise NotImplementedError("Return last is not implemented yet.")
# 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 Exception: # 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 Exception: # TF0.12
self.outputs = tf.reshape(tf.concat(1, outputs), [batch_size, 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.append(self.outputs)
self.all_params.extend(rnn_variables)
[docs]class Seq2Seq(Layer):
"""
The :class:`Seq2Seq` class is a simple :class:`DynamicRNNLayer` based Seq2seq layer without using `tl.contrib.seq2seq <https://www.tensorflow.org/api_guides/python/contrib.seq2seq>`__.
See `Model <https://camo.githubusercontent.com/9e88497fcdec5a9c716e0de5bc4b6d1793c6e23f/687474703a2f2f73757269796164656570616e2e6769746875622e696f2f696d672f736571327365712f73657132736571322e706e67>`__
and `Sequence to Sequence Learning with Neural Networks <https://arxiv.org/abs/1409.3215>`__.
- Please check this example `Chatbot in 200 lines of code <https://github.com/zsdonghao/seq2seq-chatbot>`__.
- The Author recommends users to read the source code of :class:`DynamicRNNLayer` and :class:`Seq2Seq`.
Parameters
----------
net_encode_in : :class:`Layer`
Encode sequences, [batch_size, None, n_features].
net_decode_in : :class:`Layer`
Decode sequences, [batch_size, None, n_features].
cell_fn : TensorFlow cell function
A TensorFlow core RNN cell
- see `RNN Cells in TensorFlow <https://www.tensorflow.org/api_docs/python/>`__
- Note TF1.0+ and TF1.0- are different
cell_init_args : dictionary or None
The arguments for the cell initializer.
n_hidden : int
The number of hidden units in the layer.
initializer : initializer
The initializer for 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_encode : None or RNN state
If None, `initial_state_encode` is zero state, it can be set by placeholder or other RNN.
initial_state_decode : None or RNN state
If None, `initial_state_decode` is the final state of the RNN encoder, it can be set by placeholder or other RNN.
dropout : tuple of float or int
The input and output keep probability (input_keep_prob, output_keep_prob).
- If one int, input and output keep probability are the same.
n_layer : int
The number of RNN layers, default is 1.
return_seq_2d : boolean
Only consider this argument when `return_last` is `False`
- If True, return 2D Tensor [n_example, 2 * n_hidden], for stacking DenseLayer after it.
- If False, return 3D Tensor [n_example/n_steps, n_steps, 2 * n_hidden], for stacking multiple RNN after it.
name : str
A unique layer name.
Attributes
------------
outputs : tensor
The output of RNN decoder.
initial_state_encode : tensor or StateTuple
Initial state of RNN encoder.
initial_state_decode : tensor or StateTuple
Initial state of RNN decoder.
final_state_encode : tensor or StateTuple
Final state of RNN encoder.
final_state_decode : tensor or StateTuple
Final state of RNN decoder.
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>', '<PAD_ID>']``
- target_mask : ``[1, 1, 1, 1, 0]``
- related functions : tl.prepro <pad_sequences, precess_sequences, sequences_add_start_id, sequences_get_mask>
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_encode = 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)
"""
def __init__(
self,
net_encode_in,
net_decode_in,
cell_fn, #tf.nn.rnn_cell.LSTMCell,
cell_init_args=None,
n_hidden=256,
initializer=tf.random_uniform_initializer(-0.1, 0.1),
encode_sequence_length=None,
decode_sequence_length=None,
initial_state_encode=None,
initial_state_decode=None,
dropout=None,
n_layer=1,
return_seq_2d=False,
name='seq2seq',
):
if cell_init_args is None:
cell_init_args = {'state_is_tuple': True}
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 Exception:
logging.warning("pop state_is_tuple fails.")
# self.inputs = layer.outputs
logging.info("[*] 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):
# 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,
initializer=initializer,
initial_state=initial_state_encode,
dropout=dropout,
n_layer=n_layer,
sequence_length=encode_sequence_length,
return_last=False,
return_seq_2d=True,
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,
initializer=initializer,
initial_state=(network_encode.final_state if initial_state_decode is None else initial_state_decode),
dropout=dropout,
n_layer=n_layer,
sequence_length=decode_sequence_length,
return_last=False,
return_seq_2d=return_seq_2d,
name='decode')
self.outputs = network_decode.outputs
# rnn_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
# Initial state
self.initial_state_encode = network_encode.initial_state
self.initial_state_decode = network_decode.initial_state
# Final state
self.final_state_encode = network_encode.final_state
self.final_state_decode = network_decode.final_state
# self.sequence_length = sequence_length
self.all_layers = list(network_encode.all_layers)
self.all_params = list(network_encode.all_params)
self.all_drop = dict(network_encode.all_drop)
self.all_layers.extend(list(network_decode.all_layers))
self.all_params.extend(list(network_decode.all_params))
self.all_drop.update(dict(network_decode.all_drop))
self.all_layers.append(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)