#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
import tensorlayer as tl
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
from tensorlayer.layers.core import Layer
# from tensorflow.python.ops import array_ops
# from tensorflow.python.util.tf_inspect import getfullargspec
# from tensorflow.contrib.rnn import stack_bidirectional_dynamic_rnn
# from tensorflow.python.ops.rnn_cell import LSTMStateTuple
# from tensorlayer.layers.core import LayersConfig
# from tensorlayer.layers.core import TF_GRAPHKEYS_VARIABLES
# TODO: uncomment
__all__ = [
'RNN',
'BiRNN',
# 'ConvRNNCell',
# 'BasicConvLSTMCell',
# 'ConvLSTM',
'retrieve_seq_length_op',
'retrieve_seq_length_op2',
'retrieve_seq_length_op3',
# 'target_mask_op',
# 'Seq2Seq',
]
[文档]class RNN(Layer):
"""
The :class:`RNN` class is a fixed length recurrent layer for implementing simple RNN,
LSTM, GRU and etc.
Parameters
----------
cell : TensorFlow cell function
A RNN cell implemented by tf.keras
- E.g. tf.keras.layers.SimpleRNNCell, tf.keras.layers.LSTMCell, tf.keras.layers.GRUCell
- Note TF2.0+, TF1.0+ and TF1.0- are different
return_last_output : boolean
Whether return last output or all outputs in a sequence.
- 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
In a dynamic model, `return_last_output` can be updated when it is called in customised forward().
By default, `False`.
return_seq_2d : boolean
Only consider this argument when `return_last_output` is `False`
- If True, return 2D Tensor [batch_size * n_steps, n_hidden], for stacking Dense layer after it.
- If False, return 3D Tensor [batch_size, n_steps, n_hidden], for stacking multiple RNN after it.
In a dynamic model, `return_seq_2d` can be updated when it is called in customised forward().
By default, `False`.
return_last_state: boolean
Whether to return the last state of the RNN cell. The state is a list of Tensor.
For simple RNN and GRU, last_state = [last_output]; For LSTM, last_state = [last_output, last_cell_state]
- If True, the layer will return outputs and the final state of the cell.
- If False, the layer will return outputs only.
In a dynamic model, `return_last_state` can be updated when it is called in customised forward().
By default, `False`.
in_channels: int
Optional, the number of channels of the previous layer which is normally the size of embedding.
If given, the layer will be built when init.
If None, it will be automatically detected when the layer is forwarded for the first time.
name : str
A unique layer name.
Examples
--------
For synced sequence input and output, see `PTB example <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_ptb_lstm.py>`__
A simple regression model below.
>>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size])
>>> rnn_out, lstm_state = tl.layers.RNN(
>>> cell=tf.keras.layers.LSTMCell(units=hidden_size, dropout=0.1),
>>> in_channels=embedding_size,
>>> return_last_output=True, return_last_state=True, name='lstmrnn'
>>> )(inputs)
>>> outputs = tl.layers.Dense(n_units=1)(rnn_out)
>>> rnn_model = tl.models.Model(inputs=inputs, outputs=[outputs, rnn_state[0], rnn_state[1]], name='rnn_model')
>>> # If LSTMCell is applied, the rnn_state is [h, c] where h the hidden state and c the cell state of LSTM.
A stacked RNN model.
>>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size])
>>> rnn_out1 = tl.layers.RNN(
>>> cell=tf.keras.layers.SimpleRNNCell(units=hidden_size, dropout=0.1),
>>> return_last_output=False, return_seq_2d=False, return_last_state=False
>>> )(inputs)
>>> rnn_out2 = tl.layers.RNN(
>>> cell=tf.keras.layers.SimpleRNNCell(units=hidden_size, dropout=0.1),
>>> return_last_output=True, return_last_state=False
>>> )(rnn_out1)
>>> outputs = tl.layers.Dense(n_units=1)(rnn_out2)
>>> rnn_model = tl.models.Model(inputs=inputs, outputs=outputs)
Notes
-----
Input dimension should be rank 3 : [batch_size, n_steps, n_features], if no, please see layer :class:`Reshape`.
"""
def __init__(
self,
cell,
return_last_output=False,
return_seq_2d=False,
return_last_state=True,
in_channels=None,
name=None, # 'rnn'
):
super(RNN, self).__init__(name=name)
self.cell = cell
self.return_last_output = return_last_output
self.return_seq_2d = return_seq_2d
self.return_last_state = return_last_state
if in_channels is not None:
self.build((None, None, in_channels))
self._built = True
logging.info("RNN %s: cell: %s, n_units: %s" % (self.name, self.cell.__class__.__name__, self.cell.units))
def __repr__(self):
s = ('{classname}(cell={cellname}, n_units={n_units}')
s += ', name=\'{name}\''
s += ')'
return s.format(
classname=self.__class__.__name__, cellname=self.cell.__class__.__name__, n_units=self.cell.units,
**self.__dict__
)
def build(self, inputs_shape):
"""
Parameters
----------
inputs_shape : tuple
the shape of inputs tensor
"""
# Input dimension should be rank 3 [batch_size, n_steps(max), n_features]
if len(inputs_shape) != 3:
raise Exception("RNN : Input dimension should be rank 3 : [batch_size, n_steps, n_features]")
with tf.name_scope(self.name) as scope:
self.cell.build(tuple(inputs_shape))
if self._trainable_weights is None:
self._trainable_weights = list()
for var in self.cell.trainable_variables:
self._trainable_weights.append(var)
# @tf.function
def forward(self, inputs, initial_state=None, **kwargs):
"""
Parameters
----------
inputs : input tensor
The input of a network
initial_state : None or list of Tensor (RNN State)
If None, `initial_state` is zero state.
**kwargs: dict
Some attributes can be updated during forwarding
such as `return_last_output`, `return_seq_2d`, `return_last_state`.
"""
if kwargs:
for attr in kwargs:
if attr in self.__dict__:
setattr(self, attr, kwargs[attr])
if self.return_last_output:
outputs = [-1]
else:
outputs = list()
states = initial_state if initial_state is not None else self.cell.get_initial_state(inputs)
if not isinstance(states, list):
states = [states]
total_steps = inputs.get_shape().as_list()[1]
self.cell.reset_dropout_mask()
self.cell.reset_recurrent_dropout_mask()
for time_step in range(total_steps):
cell_output, states = self.cell.call(inputs[:, time_step, :], states, training=self.is_train)
if self.return_last_output:
outputs[-1] = cell_output
else:
outputs.append(cell_output)
if self.return_last_output:
outputs = outputs[-1]
else:
if self.return_seq_2d:
# PTB tutorial: stack dense layer after that, or compute the cost from the output
# 2D Tensor [batch_size * n_steps, n_hidden]
outputs = tf.reshape(tf.concat(outputs, 1), [-1, self.cell.units])
else:
# <akara>: stack more RNN layer after that
# 3D Tensor [batch_size, n_steps, n_hidden]
outputs = tf.reshape(tf.concat(outputs, 1), [-1, total_steps, self.cell.units])
if self.return_last_state:
return outputs, states
else:
return outputs
# TODO: write tl.layers.SimpleRNN, tl.layers.GRU, tl.layers.LSTM
[文档]class BiRNN(Layer):
"""
The :class:`BiRNN` class is a fixed length Bidirectional recurrent layer.
Parameters
----------
fw_cell : TensorFlow cell function for forward direction
A RNN cell implemented by tf.keras, e.g. tf.keras.layers.SimpleRNNCell, tf.keras.layers.LSTMCell, tf.keras.layers.GRUCell.
Note TF2.0+, TF1.0+ and TF1.0- are different
bw_cell: TensorFlow cell function for backward direction similar with `fw_cell`
return_seq_2d : boolean.
If True, return 2D Tensor [batch_size * n_steps, n_hidden], for stacking Dense layer after it.
If False, return 3D Tensor [batch_size, n_steps, n_hidden], for stacking multiple RNN after it.
In a dynamic model, `return_seq_2d` can be updated when it is called in customised forward().
By default, `False`.
return_last_state: boolean
Whether to return the last state of the two cells. The state is a list of Tensor.
- If True, the layer will return outputs, the final state of `fw_cell` and the final state of `bw_cell`.
- If False, the layer will return outputs only.
In a dynamic model, `return_last_state` can be updated when it is called in customised forward().
By default, `False`.
in_channels: int
Optional, the number of channels of the previous layer which is normally the size of embedding.
If given, the layer will be built when init.
If None, it will be automatically detected when the layer is forwarded for the first time.
name : str
A unique layer name.
Examples
--------
A simple regression model below.
>>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size])
>>> # the fw_cell and bw_cell can be different
>>> rnnlayer = tl.layers.BiRNN(
>>> fw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size, dropout=0.1),
>>> bw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size + 1, dropout=0.1),
>>> return_seq_2d=True, return_last_state=True
>>> )
>>> # if return_last_state=True, the final state of the two cells will be returned together with the outputs
>>> # if return_last_state=False, only the outputs will be returned
>>> rnn_out, rnn_fw_state, rnn_bw_state = rnnlayer(inputs)
>>> # if the BiRNN is followed by a Dense, return_seq_2d should be True.
>>> # if the BiRNN is followed by other RNN, return_seq_2d can be False.
>>> dense = tl.layers.Dense(n_units=1)(rnn_out)
>>> outputs = tl.layers.Reshape([-1, num_steps])(dense)
>>> rnn_model = tl.models.Model(inputs=inputs, outputs=[outputs, rnn_out, rnn_fw_state[0], rnn_bw_state[0]])
A stacked BiRNN model.
>>> inputs = tl.layers.Input([batch_size, num_steps, embedding_size])
>>> rnn_out1 = tl.layers.BiRNN(
>>> fw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size, dropout=0.1),
>>> bw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size + 1, dropout=0.1),
>>> return_seq_2d=False, return_last_state=False
>>> )(inputs)
>>> rnn_out2 = tl.layers.BiRNN(
>>> fw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size, dropout=0.1),
>>> bw_cell=tf.keras.layers.SimpleRNNCell(units=hidden_size + 1, dropout=0.1),
>>> return_seq_2d=True, return_last_state=False
>>> )(rnn_out1)
>>> dense = tl.layers.Dense(n_units=1)(rnn_out2)
>>> outputs = tl.layers.Reshape([-1, num_steps])(dense)
>>> rnn_model = tl.models.Model(inputs=inputs, outputs=outputs)
Notes
-----
Input dimension should be rank 3 : [batch_size, n_steps, n_features]. If not, please see layer :class:`Reshape`.
"""
def __init__(
self,
fw_cell,
bw_cell,
return_seq_2d=False,
return_last_state=False,
in_channels=None,
name=None, # 'birnn'
):
super(BiRNN, self).__init__(name)
self.fw_cell = fw_cell
self.bw_cell = bw_cell
self.return_seq_2d = return_seq_2d
self.return_last_state = return_last_state
if in_channels is not None:
self.build((None, None, in_channels))
self._built = True
logging.info(
"BiRNN %s: fw_cell: %s, fw_n_units: %s, bw_cell: %s, bw_n_units： %s" % (
self.name, self.fw_cell.__class__.__name__, self.fw_cell.units, self.bw_cell.__class__.__name__,
self.bw_cell.units
)
)
def __repr__(self):
s = (
'{classname}(fw_cell={fw_cellname}, fw_n_units={fw_n_units}'
', bw_cell={bw_cellname}, bw_n_units={bw_n_units}'
)
s += ', name=\'{name}\''
s += ')'
return s.format(
classname=self.__class__.__name__, fw_cellname=self.fw_cell.__class__.__name__,
fw_n_units=self.fw_cell.units, bw_cellname=self.bw_cell.__class__.__name__, bw_n_units=self.bw_cell.units,
**self.__dict__
)
def build(self, inputs_shape):
"""
Parameters
----------
inputs_shape : tuple
the shape of inputs tensor
"""
# Input dimension should be rank 3 [batch_size, n_steps(max), n_features]
if len(inputs_shape) != 3:
raise Exception("RNN : Input dimension should be rank 3 : [batch_size, n_steps, n_features]")
with tf.name_scope(self.name) as scope:
self.fw_cell.build(tuple(inputs_shape))
self.bw_cell.build(tuple(inputs_shape))
if self._trainable_weights is None:
self._trainable_weights = list()
for var in self.fw_cell.trainable_variables:
self._trainable_weights.append(var)
for var in self.bw_cell.trainable_variables:
self._trainable_weights.append(var)
# @tf.function
def forward(self, inputs, fw_initial_state=None, bw_initial_state=None, **kwargs):
"""
Parameters
----------
inputs : input tensor
The input of a network
fw_initial_state : None or list of Tensor (RNN State)
If None, `fw_initial_state` is zero state.
bw_initial_state : None or list of Tensor (RNN State)
If None, `bw_initial_state` is zero state.
**kwargs: dict
Some attributes can be updated during forwarding
such as `return_last_output`, `return_seq_2d`, `return_last_state`.
"""
if kwargs:
for attr in kwargs:
if attr in self.__dict__:
setattr(self, attr, kwargs[attr])
fw_outputs = list()
bw_outputs = list()
fw_states = fw_initial_state if fw_initial_state is not None else self.fw_cell.get_initial_state(inputs)
bw_states = bw_initial_state if bw_initial_state is not None else self.bw_cell.get_initial_state(inputs)
if not isinstance(fw_states, list):
fw_states = [fw_states]
if not isinstance(bw_states, list):
bw_states = [bw_states]
total_steps = inputs.get_shape().as_list()[1]
self.fw_cell.reset_dropout_mask()
self.fw_cell.reset_recurrent_dropout_mask()
self.bw_cell.reset_dropout_mask()
self.bw_cell.reset_recurrent_dropout_mask()
for time_step in range(total_steps):
fw_cell_output, fw_states = self.fw_cell.call(inputs[:, time_step, :], fw_states, training=self.is_train)
bw_cell_output, bw_states = self.bw_cell.call(
inputs[:, -time_step - 1, :], bw_states, training=self.is_train
)
fw_outputs.append(fw_cell_output)
bw_outputs.append(bw_cell_output)
if self.return_seq_2d:
# PTB tutorial: stack dense layer after that, or compute the cost from the output
# 2D Tensor [batch_size * n_steps, n_hidden]
fw_outputs = tf.reshape(tf.concat(fw_outputs, 1), [-1, self.fw_cell.units])
bw_outputs = tf.reshape(tf.concat(bw_outputs, 1), [-1, self.bw_cell.units])
else:
# <akara>: stack more RNN layer after that
# 3D Tensor [batch_size, n_steps, n_hidden]
fw_outputs = tf.reshape(tf.concat(fw_outputs, 1), [-1, total_steps, self.fw_cell.units])
bw_outputs = tf.reshape(tf.concat(bw_outputs, 1), [-1, total_steps, self.bw_cell.units])
outputs = tf.concat([fw_outputs, bw_outputs], -1)
if self.return_last_state:
return outputs, fw_states, bw_states
else:
return outputs
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
"""
dtype = LayersConfig.tf_dtype
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
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.compat.v1.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.compat.v1.variable_scope(scope or "Conv"):
matrix = tf.compat.v1.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.compat.v1.get_variable(
"Bias", [num_features], dtype=dtype,
initializer=tf.compat.v1.initializers.constant(bias_start, dtype=dtype)
)
return res + bias_term
class ConvLSTM(Layer):
"""A fixed length Convolutional LSTM layer.
See this `paper <https://arxiv.org/abs/1506.04214>`__ .
Parameters
----------
prev_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_output` 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_output = 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 ``?``.
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
cell_shape=None,
feature_map=1,
filter_size=(3, 3),
cell_fn=BasicConvLSTMCell,
initializer=tf.compat.v1.initializers.random_uniform(-0.1, 0.1),
n_steps=5,
initial_state=None,
return_last=False,
return_seq_2d=False,
name='convlstm',
):
super(ConvLSTM, self).__init__(prev_layer=prev_layer, name=name)
logging.info(
"ConvLSTM %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:
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)
else:
self.initial_state = initial_state
state = self.initial_state
# with tf.variable_scope("model", reuse=None, initializer=initializer):
with tf.compat.v1.variable_scope(name, initializer=initializer) as vs:
for time_step in range(n_steps):
if time_step > 0: tf.compat.v1.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.compat.v1.get_collection(tf.compat.v1.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._add_layers(self.outputs)
self._add_params(rnn_variables)
# @tf.function
[文档]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
-----------
Single feature
>>> data = [[[1],[2],[0],[0],[0]],
>>> [[1],[2],[3],[0],[0]],
>>> [[1],[2],[6],[1],[0]]]
>>> data = tf.convert_to_tensor(data, dtype=tf.float32)
>>> length = tl.layers.retrieve_seq_length_op(data)
[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]]]
>>> data = tf.convert_to_tensor(data, dtype=tf.float32)
>>> length = tl.layers.retrieve_seq_length_op(data)
[4 3 4]
References
------------
Borrow from `TFlearn <https://github.com/tflearn/tflearn/blob/master/tflearn/layers/recurrent.py>`__.
"""
with tf.name_scope('GetLength'):
used = tf.sign(tf.reduce_max(input_tensor=tf.abs(data), axis=2))
length = tf.reduce_sum(input_tensor=used, axis=1)
return tf.cast(length, tf.int32)
# @tf.function
[文档]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]]
>>> data = tf.convert_to_tensor(data, dtype=tf.float32)
>>> length = tl.layers.retrieve_seq_length_op2(data)
[2 3 4]
"""
return tf.reduce_sum(input_tensor=tf.cast(tf.greater(data, tf.zeros_like(data)), tf.int32), axis=1)
# @tf.function
[文档]def retrieve_seq_length_op3(data, pad_val=0):
"""An op to compute the length of a sequence, the data shape can be [batch_size, n_step(max)] or
[batch_size, n_step(max), n_features].
If the data has type of tf.string and pad_val is assigned as empty string (''), this op will compute the
length of the string sequence.
Parameters
-----------
data : tensor
[batch_size, n_step(max)] or [batch_size, n_step(max), n_features] with zero padding on the right hand side.
pad_val:
By default 0. If the data is tf.string, please assign this as empty string ('')
Examples
-----------
>>> data = [[[1],[2],[0],[0],[0]],
>>> [[1],[2],[3],[0],[0]],
>>> [[1],[2],[6],[1],[0]]]
>>> data = tf.convert_to_tensor(data, dtype=tf.float32)
>>> length = tl.layers.retrieve_seq_length_op3(data)
[2, 3, 4]
>>> 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]]]
>>> data = tf.convert_to_tensor(data, dtype=tf.float32)
>>> length = tl.layers.retrieve_seq_length_op3(data)
[4, 3, 4]
>>> data = [[1,2,0,0,0],
>>> [1,2,3,0,0],
>>> [1,2,6,1,0]]
>>> data = tf.convert_to_tensor(data, dtype=tf.float32)
>>> length = tl.layers.retrieve_seq_length_op3(data)
[2, 3, 4]
>>> data = [['hello','world','','',''],
>>> ['hello','world','tensorlayer','',''],
>>> ['hello','world','tensorlayer','2.0','']]
>>> data = tf.convert_to_tensor(data, dtype=tf.string)
>>> length = tl.layers.retrieve_seq_length_op3(data, pad_val='')
[2, 3, 4]
"""
data_shape_size = data.get_shape().ndims
if data_shape_size == 3:
return tf.reduce_sum(
input_tensor=tf.cast(tf.reduce_any(input_tensor=tf.not_equal(data, pad_val), axis=2), dtype=tf.int32),
axis=1
)
elif data_shape_size == 2:
return tf.reduce_sum(input_tensor=tf.cast(tf.not_equal(data, pad_val), dtype=tf.int32), axis=1)
elif data_shape_size == 1:
raise ValueError("retrieve_seq_length_op3: data has wrong shape! Shape got ", data.get_shape().as_list())
else:
raise ValueError(
"retrieve_seq_length_op3: handling data with num of dims %s hasn't been implemented!" % (data_shape_size)
)
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(input_tensor=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))
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/tensorlayer/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_output` 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 = EmbeddingInput(
... inputs = encode_seqs,
... vocabulary_size = 10000,
... embedding_size = 200,
... name = 'seq_embedding')
>>> vs.reuse_variables()
>>> net_decode = EmbeddingInput(
... 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 = Dense(net, n_units=10000, act=None, 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.compat.v1.initializers.random_uniform(-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',
):
super(Seq2Seq,
self).__init__(prev_layer=[net_encode_in, net_decode_in], cell_init_args=cell_init_args, name=name)
if self.cell_init_args:
self.cell_init_args['state_is_tuple'] = True # 'use_peepholes': True,
if cell_fn is None:
raise ValueError("cell_fn cannot be set to None")
if 'GRU' in cell_fn.__name__:
try:
cell_init_args.pop('state_is_tuple')
except Exception:
logging.warning("pop state_is_tuple fails.")
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.compat.v1.variable_scope(name):
# tl.layers.set_name_reuse(reuse)
# network = InputLayer(self.inputs, name=name+'/input')
network_encode = DynamicRNN(
net_encode_in, cell_fn=cell_fn, cell_init_args=self.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 = DynamicRNN(
net_decode_in, cell_fn=cell_fn, cell_init_args=self.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._add_layers(network_encode.all_layers)
self._add_params(network_encode.all_params)
self._add_dropout_layers(network_encode.all_drop)
self._add_layers(network_decode.all_layers)
self._add_params(network_decode.all_params)
self._add_dropout_layers(network_decode.all_drop)
self._add_layers(self.outputs)