| # Copyright 2015 The TensorFlow Authors. All Rights Reserved. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================== |
| """Module implementing RNN Cells. |
| |
| This module provides a number of basic commonly used RNN cells, such as LSTM |
| (Long Short Term Memory) or GRU (Gated Recurrent Unit), and a number of |
| operators that allow adding dropouts, projections, or embeddings for inputs. |
| Constructing multi-layer cells is supported by the class `MultiRNNCell`, or by |
| calling the `rnn` ops several times. |
| """ |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import collections |
| import hashlib |
| import numbers |
| |
| from tensorflow.python.eager import context |
| from tensorflow.python.framework import constant_op |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.framework import ops |
| from tensorflow.python.framework import tensor_shape |
| from tensorflow.python.framework import tensor_util |
| from tensorflow.python.keras import activations |
| from tensorflow.python.keras import initializers |
| from tensorflow.python.keras.utils import tf_utils |
| from tensorflow.python.layers import base as base_layer |
| from tensorflow.python.ops import array_ops |
| from tensorflow.python.ops import clip_ops |
| from tensorflow.python.ops import init_ops |
| from tensorflow.python.ops import math_ops |
| from tensorflow.python.ops import nn_ops |
| from tensorflow.python.ops import partitioned_variables |
| from tensorflow.python.ops import random_ops |
| from tensorflow.python.ops import tensor_array_ops |
| from tensorflow.python.ops import variable_scope as vs |
| from tensorflow.python.ops import variables as tf_variables |
| from tensorflow.python.platform import tf_logging as logging |
| from tensorflow.python.training.checkpointable import base as checkpointable |
| from tensorflow.python.util import nest |
| from tensorflow.python.util.deprecation import deprecated |
| from tensorflow.python.util.tf_export import tf_export |
| |
| |
| _BIAS_VARIABLE_NAME = "bias" |
| _WEIGHTS_VARIABLE_NAME = "kernel" |
| |
| # This can be used with self.assertRaisesRegexp for assert_like_rnncell. |
| ASSERT_LIKE_RNNCELL_ERROR_REGEXP = "is not an RNNCell" |
| |
| |
| def assert_like_rnncell(cell_name, cell): |
| """Raises a TypeError if cell is not like an RNNCell. |
| |
| NOTE: Do not rely on the error message (in particular in tests) which can be |
| subject to change to increase readability. Use |
| ASSERT_LIKE_RNNCELL_ERROR_REGEXP. |
| |
| Args: |
| cell_name: A string to give a meaningful error referencing to the name |
| of the functionargument. |
| cell: The object which should behave like an RNNCell. |
| |
| Raises: |
| TypeError: A human-friendly exception. |
| """ |
| conditions = [ |
| hasattr(cell, "output_size"), |
| hasattr(cell, "state_size"), |
| hasattr(cell, "get_initial_state") or hasattr(cell, "zero_state"), |
| callable(cell), |
| ] |
| errors = [ |
| "'output_size' property is missing", |
| "'state_size' property is missing", |
| "either 'zero_state' or 'get_initial_state' method is required", |
| "is not callable" |
| ] |
| |
| if not all(conditions): |
| |
| errors = [error for error, cond in zip(errors, conditions) if not cond] |
| raise TypeError("The argument {!r} ({}) is not an RNNCell: {}.".format( |
| cell_name, cell, ", ".join(errors))) |
| |
| |
| def _concat(prefix, suffix, static=False): |
| """Concat that enables int, Tensor, or TensorShape values. |
| |
| This function takes a size specification, which can be an integer, a |
| TensorShape, or a Tensor, and converts it into a concatenated Tensor |
| (if static = False) or a list of integers (if static = True). |
| |
| Args: |
| prefix: The prefix; usually the batch size (and/or time step size). |
| (TensorShape, int, or Tensor.) |
| suffix: TensorShape, int, or Tensor. |
| static: If `True`, return a python list with possibly unknown dimensions. |
| Otherwise return a `Tensor`. |
| |
| Returns: |
| shape: the concatenation of prefix and suffix. |
| |
| Raises: |
| ValueError: if `suffix` is not a scalar or vector (or TensorShape). |
| ValueError: if prefix or suffix was `None` and asked for dynamic |
| Tensors out. |
| """ |
| if isinstance(prefix, ops.Tensor): |
| p = prefix |
| p_static = tensor_util.constant_value(prefix) |
| if p.shape.ndims == 0: |
| p = array_ops.expand_dims(p, 0) |
| elif p.shape.ndims != 1: |
| raise ValueError("prefix tensor must be either a scalar or vector, " |
| "but saw tensor: %s" % p) |
| else: |
| p = tensor_shape.as_shape(prefix) |
| p_static = p.as_list() if p.ndims is not None else None |
| p = (constant_op.constant(p.as_list(), dtype=dtypes.int32) |
| if p.is_fully_defined() else None) |
| if isinstance(suffix, ops.Tensor): |
| s = suffix |
| s_static = tensor_util.constant_value(suffix) |
| if s.shape.ndims == 0: |
| s = array_ops.expand_dims(s, 0) |
| elif s.shape.ndims != 1: |
| raise ValueError("suffix tensor must be either a scalar or vector, " |
| "but saw tensor: %s" % s) |
| else: |
| s = tensor_shape.as_shape(suffix) |
| s_static = s.as_list() if s.ndims is not None else None |
| s = (constant_op.constant(s.as_list(), dtype=dtypes.int32) |
| if s.is_fully_defined() else None) |
| |
| if static: |
| shape = tensor_shape.as_shape(p_static).concatenate(s_static) |
| shape = shape.as_list() if shape.ndims is not None else None |
| else: |
| if p is None or s is None: |
| raise ValueError("Provided a prefix or suffix of None: %s and %s" |
| % (prefix, suffix)) |
| shape = array_ops.concat((p, s), 0) |
| return shape |
| |
| |
| def _zero_state_tensors(state_size, batch_size, dtype): |
| """Create tensors of zeros based on state_size, batch_size, and dtype.""" |
| def get_state_shape(s): |
| """Combine s with batch_size to get a proper tensor shape.""" |
| c = _concat(batch_size, s) |
| size = array_ops.zeros(c, dtype=dtype) |
| if not context.executing_eagerly(): |
| c_static = _concat(batch_size, s, static=True) |
| size.set_shape(c_static) |
| return size |
| return nest.map_structure(get_state_shape, state_size) |
| |
| |
| @tf_export("nn.rnn_cell.RNNCell") |
| class RNNCell(base_layer.Layer): |
| """Abstract object representing an RNN cell. |
| |
| Every `RNNCell` must have the properties below and implement `call` with |
| the signature `(output, next_state) = call(input, state)`. The optional |
| third input argument, `scope`, is allowed for backwards compatibility |
| purposes; but should be left off for new subclasses. |
| |
| This definition of cell differs from the definition used in the literature. |
| In the literature, 'cell' refers to an object with a single scalar output. |
| This definition refers to a horizontal array of such units. |
| |
| An RNN cell, in the most abstract setting, is anything that has |
| a state and performs some operation that takes a matrix of inputs. |
| This operation results in an output matrix with `self.output_size` columns. |
| If `self.state_size` is an integer, this operation also results in a new |
| state matrix with `self.state_size` columns. If `self.state_size` is a |
| (possibly nested tuple of) TensorShape object(s), then it should return a |
| matching structure of Tensors having shape `[batch_size].concatenate(s)` |
| for each `s` in `self.batch_size`. |
| """ |
| |
| def __init__(self, trainable=True, name=None, dtype=None, **kwargs): |
| super(RNNCell, self).__init__( |
| trainable=trainable, name=name, dtype=dtype, **kwargs) |
| # Attribute that indicates whether the cell is a TF RNN cell, due the slight |
| # difference between TF and Keras RNN cell. |
| self._is_tf_rnn_cell = True |
| |
| def __call__(self, inputs, state, scope=None): |
| """Run this RNN cell on inputs, starting from the given state. |
| |
| Args: |
| inputs: `2-D` tensor with shape `[batch_size, input_size]`. |
| state: if `self.state_size` is an integer, this should be a `2-D Tensor` |
| with shape `[batch_size, self.state_size]`. Otherwise, if |
| `self.state_size` is a tuple of integers, this should be a tuple |
| with shapes `[batch_size, s] for s in self.state_size`. |
| scope: VariableScope for the created subgraph; defaults to class name. |
| |
| Returns: |
| A pair containing: |
| |
| - Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. |
| - New state: Either a single `2-D` tensor, or a tuple of tensors matching |
| the arity and shapes of `state`. |
| """ |
| if scope is not None: |
| with vs.variable_scope(scope, |
| custom_getter=self._rnn_get_variable) as scope: |
| return super(RNNCell, self).__call__(inputs, state, scope=scope) |
| else: |
| scope_attrname = "rnncell_scope" |
| scope = getattr(self, scope_attrname, None) |
| if scope is None: |
| scope = vs.variable_scope(vs.get_variable_scope(), |
| custom_getter=self._rnn_get_variable) |
| setattr(self, scope_attrname, scope) |
| with scope: |
| return super(RNNCell, self).__call__(inputs, state) |
| |
| def _rnn_get_variable(self, getter, *args, **kwargs): |
| variable = getter(*args, **kwargs) |
| if context.executing_eagerly(): |
| trainable = variable._trainable # pylint: disable=protected-access |
| else: |
| trainable = ( |
| variable in tf_variables.trainable_variables() or |
| (isinstance(variable, tf_variables.PartitionedVariable) and |
| list(variable)[0] in tf_variables.trainable_variables())) |
| if trainable and variable not in self._trainable_weights: |
| self._trainable_weights.append(variable) |
| elif not trainable and variable not in self._non_trainable_weights: |
| self._non_trainable_weights.append(variable) |
| return variable |
| |
| @property |
| def state_size(self): |
| """size(s) of state(s) used by this cell. |
| |
| It can be represented by an Integer, a TensorShape or a tuple of Integers |
| or TensorShapes. |
| """ |
| raise NotImplementedError("Abstract method") |
| |
| @property |
| def output_size(self): |
| """Integer or TensorShape: size of outputs produced by this cell.""" |
| raise NotImplementedError("Abstract method") |
| |
| def build(self, _): |
| # This tells the parent Layer object that it's OK to call |
| # self.add_variable() inside the call() method. |
| pass |
| |
| def get_initial_state(self, inputs=None, batch_size=None, dtype=None): |
| if inputs is not None: |
| # Validate the given batch_size and dtype against inputs if provided. |
| inputs = ops.convert_to_tensor(inputs, name="inputs") |
| if batch_size is not None: |
| if tensor_util.is_tensor(batch_size): |
| static_batch_size = tensor_util.constant_value( |
| batch_size, partial=True) |
| else: |
| static_batch_size = batch_size |
| if inputs.shape[0].value != static_batch_size: |
| raise ValueError( |
| "batch size from input tensor is different from the " |
| "input param. Input tensor batch: {}, batch_size: {}".format( |
| inputs.shape[0].value, batch_size)) |
| |
| if dtype is not None and inputs.dtype != dtype: |
| raise ValueError( |
| "dtype from input tensor is different from the " |
| "input param. Input tensor dtype: {}, dtype: {}".format( |
| inputs.dtype, dtype)) |
| |
| batch_size = inputs.shape[0].value or array_ops.shape(inputs)[0] |
| dtype = inputs.dtype |
| if None in [batch_size, dtype]: |
| raise ValueError( |
| "batch_size and dtype cannot be None while constructing initial " |
| "state: batch_size={}, dtype={}".format(batch_size, dtype)) |
| return self.zero_state(batch_size, dtype) |
| |
| def zero_state(self, batch_size, dtype): |
| """Return zero-filled state tensor(s). |
| |
| Args: |
| batch_size: int, float, or unit Tensor representing the batch size. |
| dtype: the data type to use for the state. |
| |
| Returns: |
| If `state_size` is an int or TensorShape, then the return value is a |
| `N-D` tensor of shape `[batch_size, state_size]` filled with zeros. |
| |
| If `state_size` is a nested list or tuple, then the return value is |
| a nested list or tuple (of the same structure) of `2-D` tensors with |
| the shapes `[batch_size, s]` for each s in `state_size`. |
| """ |
| # Try to use the last cached zero_state. This is done to avoid recreating |
| # zeros, especially when eager execution is enabled. |
| state_size = self.state_size |
| is_eager = context.executing_eagerly() |
| if is_eager and hasattr(self, "_last_zero_state"): |
| (last_state_size, last_batch_size, last_dtype, |
| last_output) = getattr(self, "_last_zero_state") |
| if (last_batch_size == batch_size and |
| last_dtype == dtype and |
| last_state_size == state_size): |
| return last_output |
| with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]): |
| output = _zero_state_tensors(state_size, batch_size, dtype) |
| if is_eager: |
| self._last_zero_state = (state_size, batch_size, dtype, output) |
| return output |
| |
| |
| class LayerRNNCell(RNNCell): |
| """Subclass of RNNCells that act like proper `tf.Layer` objects. |
| |
| For backwards compatibility purposes, most `RNNCell` instances allow their |
| `call` methods to instantiate variables via `tf.get_variable`. The underlying |
| variable scope thus keeps track of any variables, and returning cached |
| versions. This is atypical of `tf.layer` objects, which separate this |
| part of layer building into a `build` method that is only called once. |
| |
| Here we provide a subclass for `RNNCell` objects that act exactly as |
| `Layer` objects do. They must provide a `build` method and their |
| `call` methods do not access Variables `tf.get_variable`. |
| """ |
| |
| def __call__(self, inputs, state, scope=None, *args, **kwargs): |
| """Run this RNN cell on inputs, starting from the given state. |
| |
| Args: |
| inputs: `2-D` tensor with shape `[batch_size, input_size]`. |
| state: if `self.state_size` is an integer, this should be a `2-D Tensor` |
| with shape `[batch_size, self.state_size]`. Otherwise, if |
| `self.state_size` is a tuple of integers, this should be a tuple |
| with shapes `[batch_size, s] for s in self.state_size`. |
| scope: optional cell scope. |
| *args: Additional positional arguments. |
| **kwargs: Additional keyword arguments. |
| |
| Returns: |
| A pair containing: |
| |
| - Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. |
| - New state: Either a single `2-D` tensor, or a tuple of tensors matching |
| the arity and shapes of `state`. |
| """ |
| # Bypass RNNCell's variable capturing semantics for LayerRNNCell. |
| # Instead, it is up to subclasses to provide a proper build |
| # method. See the class docstring for more details. |
| return base_layer.Layer.__call__(self, inputs, state, scope=scope, |
| *args, **kwargs) |
| |
| |
| @tf_export("nn.rnn_cell.BasicRNNCell") |
| class BasicRNNCell(LayerRNNCell): |
| """The most basic RNN cell. |
| |
| Note that this cell is not optimized for performance. Please use |
| `tf.contrib.cudnn_rnn.CudnnRNNTanh` for better performance on GPU. |
| |
| Args: |
| num_units: int, The number of units in the RNN cell. |
| activation: Nonlinearity to use. Default: `tanh`. It could also be string |
| that is within Keras activation function names. |
| reuse: (optional) Python boolean describing whether to reuse variables |
| in an existing scope. If not `True`, and the existing scope already has |
| the given variables, an error is raised. |
| name: String, the name of the layer. Layers with the same name will |
| share weights, but to avoid mistakes we require reuse=True in such |
| cases. |
| dtype: Default dtype of the layer (default of `None` means use the type |
| of the first input). Required when `build` is called before `call`. |
| **kwargs: Dict, keyword named properties for common layer attributes, like |
| `trainable` etc when constructing the cell from configs of get_config(). |
| """ |
| |
| def __init__(self, |
| num_units, |
| activation=None, |
| reuse=None, |
| name=None, |
| dtype=None, |
| **kwargs): |
| super(BasicRNNCell, self).__init__( |
| _reuse=reuse, name=name, dtype=dtype, **kwargs) |
| if context.executing_eagerly() and context.num_gpus() > 0: |
| logging.warn("%s: Note that this cell is not optimized for performance. " |
| "Please use tf.contrib.cudnn_rnn.CudnnRNNTanh for better " |
| "performance on GPU.", self) |
| |
| # Inputs must be 2-dimensional. |
| self.input_spec = base_layer.InputSpec(ndim=2) |
| |
| self._num_units = num_units |
| if activation: |
| self._activation = activations.get(activation) |
| else: |
| self._activation = math_ops.tanh |
| |
| @property |
| def state_size(self): |
| return self._num_units |
| |
| @property |
| def output_size(self): |
| return self._num_units |
| |
| @tf_utils.shape_type_conversion |
| def build(self, inputs_shape): |
| if inputs_shape[-1] is None: |
| raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" |
| % str(inputs_shape)) |
| |
| input_depth = inputs_shape[-1] |
| self._kernel = self.add_variable( |
| _WEIGHTS_VARIABLE_NAME, |
| shape=[input_depth + self._num_units, self._num_units]) |
| self._bias = self.add_variable( |
| _BIAS_VARIABLE_NAME, |
| shape=[self._num_units], |
| initializer=init_ops.zeros_initializer(dtype=self.dtype)) |
| |
| self.built = True |
| |
| def call(self, inputs, state): |
| """Most basic RNN: output = new_state = act(W * input + U * state + B).""" |
| |
| gate_inputs = math_ops.matmul( |
| array_ops.concat([inputs, state], 1), self._kernel) |
| gate_inputs = nn_ops.bias_add(gate_inputs, self._bias) |
| output = self._activation(gate_inputs) |
| return output, output |
| |
| def get_config(self): |
| config = { |
| "num_units": self._num_units, |
| "activation": activations.serialize(self._activation), |
| "reuse": self._reuse, |
| } |
| base_config = super(BasicRNNCell, self).get_config() |
| return dict(list(base_config.items()) + list(config.items())) |
| |
| |
| @tf_export("nn.rnn_cell.GRUCell") |
| class GRUCell(LayerRNNCell): |
| """Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078). |
| |
| Note that this cell is not optimized for performance. Please use |
| `tf.contrib.cudnn_rnn.CudnnGRU` for better performance on GPU, or |
| `tf.contrib.rnn.GRUBlockCellV2` for better performance on CPU. |
| |
| Args: |
| num_units: int, The number of units in the GRU cell. |
| activation: Nonlinearity to use. Default: `tanh`. |
| reuse: (optional) Python boolean describing whether to reuse variables |
| in an existing scope. If not `True`, and the existing scope already has |
| the given variables, an error is raised. |
| kernel_initializer: (optional) The initializer to use for the weight and |
| projection matrices. |
| bias_initializer: (optional) The initializer to use for the bias. |
| name: String, the name of the layer. Layers with the same name will |
| share weights, but to avoid mistakes we require reuse=True in such |
| cases. |
| dtype: Default dtype of the layer (default of `None` means use the type |
| of the first input). Required when `build` is called before `call`. |
| **kwargs: Dict, keyword named properties for common layer attributes, like |
| `trainable` etc when constructing the cell from configs of get_config(). |
| """ |
| |
| def __init__(self, |
| num_units, |
| activation=None, |
| reuse=None, |
| kernel_initializer=None, |
| bias_initializer=None, |
| name=None, |
| dtype=None, |
| **kwargs): |
| super(GRUCell, self).__init__( |
| _reuse=reuse, name=name, dtype=dtype, **kwargs) |
| |
| if context.executing_eagerly() and context.num_gpus() > 0: |
| logging.warn("%s: Note that this cell is not optimized for performance. " |
| "Please use tf.contrib.cudnn_rnn.CudnnGRU for better " |
| "performance on GPU.", self) |
| # Inputs must be 2-dimensional. |
| self.input_spec = base_layer.InputSpec(ndim=2) |
| |
| self._num_units = num_units |
| if activation: |
| self._activation = activations.get(activation) |
| else: |
| self._activation = math_ops.tanh |
| self._kernel_initializer = initializers.get(kernel_initializer) |
| self._bias_initializer = initializers.get(bias_initializer) |
| |
| @property |
| def state_size(self): |
| return self._num_units |
| |
| @property |
| def output_size(self): |
| return self._num_units |
| |
| @tf_utils.shape_type_conversion |
| def build(self, inputs_shape): |
| if inputs_shape[-1] is None: |
| raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" |
| % str(inputs_shape)) |
| |
| input_depth = inputs_shape[-1] |
| self._gate_kernel = self.add_variable( |
| "gates/%s" % _WEIGHTS_VARIABLE_NAME, |
| shape=[input_depth + self._num_units, 2 * self._num_units], |
| initializer=self._kernel_initializer) |
| self._gate_bias = self.add_variable( |
| "gates/%s" % _BIAS_VARIABLE_NAME, |
| shape=[2 * self._num_units], |
| initializer=( |
| self._bias_initializer |
| if self._bias_initializer is not None |
| else init_ops.constant_initializer(1.0, dtype=self.dtype))) |
| self._candidate_kernel = self.add_variable( |
| "candidate/%s" % _WEIGHTS_VARIABLE_NAME, |
| shape=[input_depth + self._num_units, self._num_units], |
| initializer=self._kernel_initializer) |
| self._candidate_bias = self.add_variable( |
| "candidate/%s" % _BIAS_VARIABLE_NAME, |
| shape=[self._num_units], |
| initializer=( |
| self._bias_initializer |
| if self._bias_initializer is not None |
| else init_ops.zeros_initializer(dtype=self.dtype))) |
| |
| self.built = True |
| |
| def call(self, inputs, state): |
| """Gated recurrent unit (GRU) with nunits cells.""" |
| |
| gate_inputs = math_ops.matmul( |
| array_ops.concat([inputs, state], 1), self._gate_kernel) |
| gate_inputs = nn_ops.bias_add(gate_inputs, self._gate_bias) |
| |
| value = math_ops.sigmoid(gate_inputs) |
| r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1) |
| |
| r_state = r * state |
| |
| candidate = math_ops.matmul( |
| array_ops.concat([inputs, r_state], 1), self._candidate_kernel) |
| candidate = nn_ops.bias_add(candidate, self._candidate_bias) |
| |
| c = self._activation(candidate) |
| new_h = u * state + (1 - u) * c |
| return new_h, new_h |
| |
| def get_config(self): |
| config = { |
| "num_units": self._num_units, |
| "kernel_initializer": initializers.serialize(self._kernel_initializer), |
| "bias_initializer": initializers.serialize(self._bias_initializer), |
| "activation": activations.serialize(self._activation), |
| "reuse": self._reuse, |
| } |
| base_config = super(GRUCell, self).get_config() |
| return dict(list(base_config.items()) + list(config.items())) |
| |
| |
| _LSTMStateTuple = collections.namedtuple("LSTMStateTuple", ("c", "h")) |
| |
| |
| @tf_export("nn.rnn_cell.LSTMStateTuple") |
| class LSTMStateTuple(_LSTMStateTuple): |
| """Tuple used by LSTM Cells for `state_size`, `zero_state`, and output state. |
| |
| Stores two elements: `(c, h)`, in that order. Where `c` is the hidden state |
| and `h` is the output. |
| |
| Only used when `state_is_tuple=True`. |
| """ |
| __slots__ = () |
| |
| @property |
| def dtype(self): |
| (c, h) = self |
| if c.dtype != h.dtype: |
| raise TypeError("Inconsistent internal state: %s vs %s" % |
| (str(c.dtype), str(h.dtype))) |
| return c.dtype |
| |
| |
| # TODO(scottzhu): Stop exporting this class in TF 2.0. |
| @tf_export("nn.rnn_cell.BasicLSTMCell") |
| class BasicLSTMCell(LayerRNNCell): |
| """DEPRECATED: Please use @{tf.nn.rnn_cell.LSTMCell} instead. |
| |
| Basic LSTM recurrent network cell. |
| |
| The implementation is based on: http://arxiv.org/abs/1409.2329. |
| |
| We add forget_bias (default: 1) to the biases of the forget gate in order to |
| reduce the scale of forgetting in the beginning of the training. |
| |
| It does not allow cell clipping, a projection layer, and does not |
| use peep-hole connections: it is the basic baseline. |
| |
| For advanced models, please use the full `tf.nn.rnn_cell.LSTMCell` |
| that follows. |
| |
| Note that this cell is not optimized for performance. Please use |
| `tf.contrib.cudnn_rnn.CudnnLSTM` for better performance on GPU, or |
| `tf.contrib.rnn.LSTMBlockCell` and `tf.contrib.rnn.LSTMBlockFusedCell` for |
| better performance on CPU. |
| """ |
| |
| @deprecated(None, "This class is deprecated, please use " |
| "tf.nn.rnn_cell.LSTMCell, which supports all the feature " |
| "this cell currently has. Please replace the existing code " |
| "with tf.nn.rnn_cell.LSTMCell(name='basic_lstm_cell').") |
| def __init__(self, |
| num_units, |
| forget_bias=1.0, |
| state_is_tuple=True, |
| activation=None, |
| reuse=None, |
| name=None, |
| dtype=None, |
| **kwargs): |
| """Initialize the basic LSTM cell. |
| |
| Args: |
| num_units: int, The number of units in the LSTM cell. |
| forget_bias: float, The bias added to forget gates (see above). |
| Must set to `0.0` manually when restoring from CudnnLSTM-trained |
| checkpoints. |
| state_is_tuple: 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. |
| activation: Activation function of the inner states. Default: `tanh`. It |
| could also be string that is within Keras activation function names. |
| reuse: (optional) Python boolean describing whether to reuse variables |
| in an existing scope. If not `True`, and the existing scope already has |
| the given variables, an error is raised. |
| name: String, the name of the layer. Layers with the same name will |
| share weights, but to avoid mistakes we require reuse=True in such |
| cases. |
| dtype: Default dtype of the layer (default of `None` means use the type |
| of the first input). Required when `build` is called before `call`. |
| **kwargs: Dict, keyword named properties for common layer attributes, like |
| `trainable` etc when constructing the cell from configs of get_config(). |
| |
| When restoring from CudnnLSTM-trained checkpoints, must use |
| `CudnnCompatibleLSTMCell` instead. |
| """ |
| super(BasicLSTMCell, self).__init__( |
| _reuse=reuse, name=name, dtype=dtype, **kwargs) |
| 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 context.executing_eagerly() and context.num_gpus() > 0: |
| logging.warn("%s: Note that this cell is not optimized for performance. " |
| "Please use tf.contrib.cudnn_rnn.CudnnLSTM for better " |
| "performance on GPU.", self) |
| |
| # Inputs must be 2-dimensional. |
| self.input_spec = base_layer.InputSpec(ndim=2) |
| |
| self._num_units = num_units |
| self._forget_bias = forget_bias |
| self._state_is_tuple = state_is_tuple |
| if activation: |
| self._activation = activations.get(activation) |
| else: |
| self._activation = math_ops.tanh |
| |
| @property |
| def state_size(self): |
| return (LSTMStateTuple(self._num_units, self._num_units) |
| if self._state_is_tuple else 2 * self._num_units) |
| |
| @property |
| def output_size(self): |
| return self._num_units |
| |
| @tf_utils.shape_type_conversion |
| def build(self, inputs_shape): |
| if inputs_shape[-1] is None: |
| raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" |
| % str(inputs_shape)) |
| |
| input_depth = inputs_shape[-1] |
| h_depth = self._num_units |
| self._kernel = self.add_variable( |
| _WEIGHTS_VARIABLE_NAME, |
| shape=[input_depth + h_depth, 4 * self._num_units]) |
| self._bias = self.add_variable( |
| _BIAS_VARIABLE_NAME, |
| shape=[4 * self._num_units], |
| initializer=init_ops.zeros_initializer(dtype=self.dtype)) |
| |
| self.built = True |
| |
| def call(self, inputs, state): |
| """Long short-term memory cell (LSTM). |
| |
| Args: |
| inputs: `2-D` tensor with shape `[batch_size, input_size]`. |
| state: An `LSTMStateTuple` of state tensors, each shaped |
| `[batch_size, num_units]`, if `state_is_tuple` has been set to |
| `True`. Otherwise, a `Tensor` shaped |
| `[batch_size, 2 * num_units]`. |
| |
| Returns: |
| A pair containing the new hidden state, and the new state (either a |
| `LSTMStateTuple` or a concatenated state, depending on |
| `state_is_tuple`). |
| """ |
| sigmoid = math_ops.sigmoid |
| one = constant_op.constant(1, dtype=dtypes.int32) |
| # Parameters of gates are concatenated into one multiply for efficiency. |
| if self._state_is_tuple: |
| c, h = state |
| else: |
| c, h = array_ops.split(value=state, num_or_size_splits=2, axis=one) |
| |
| gate_inputs = math_ops.matmul( |
| array_ops.concat([inputs, h], 1), self._kernel) |
| gate_inputs = nn_ops.bias_add(gate_inputs, self._bias) |
| |
| # i = input_gate, j = new_input, f = forget_gate, o = output_gate |
| i, j, f, o = array_ops.split( |
| value=gate_inputs, num_or_size_splits=4, axis=one) |
| |
| forget_bias_tensor = constant_op.constant(self._forget_bias, dtype=f.dtype) |
| # Note that using `add` and `multiply` instead of `+` and `*` gives a |
| # performance improvement. So using those at the cost of readability. |
| add = math_ops.add |
| multiply = math_ops.multiply |
| new_c = add(multiply(c, sigmoid(add(f, forget_bias_tensor))), |
| multiply(sigmoid(i), self._activation(j))) |
| new_h = multiply(self._activation(new_c), sigmoid(o)) |
| |
| if self._state_is_tuple: |
| new_state = LSTMStateTuple(new_c, new_h) |
| else: |
| new_state = array_ops.concat([new_c, new_h], 1) |
| return new_h, new_state |
| |
| def get_config(self): |
| config = { |
| "num_units": self._num_units, |
| "forget_bias": self._forget_bias, |
| "state_is_tuple": self._state_is_tuple, |
| "activation": activations.serialize(self._activation), |
| "reuse": self._reuse, |
| } |
| base_config = super(BasicLSTMCell, self).get_config() |
| return dict(list(base_config.items()) + list(config.items())) |
| |
| |
| @tf_export("nn.rnn_cell.LSTMCell") |
| class LSTMCell(LayerRNNCell): |
| """Long short-term memory unit (LSTM) recurrent network cell. |
| |
| The default non-peephole implementation is based on: |
| |
| https://pdfs.semanticscholar.org/1154/0131eae85b2e11d53df7f1360eeb6476e7f4.pdf |
| |
| Felix Gers, Jurgen Schmidhuber, and Fred Cummins. |
| "Learning to forget: Continual prediction with LSTM." IET, 850-855, 1999. |
| |
| The peephole implementation is based on: |
| |
| https://research.google.com/pubs/archive/43905.pdf |
| |
| Hasim Sak, Andrew Senior, and Francoise Beaufays. |
| "Long short-term memory recurrent neural network architectures for |
| large scale acoustic modeling." INTERSPEECH, 2014. |
| |
| The class uses optional peep-hole connections, optional cell clipping, and |
| an optional projection layer. |
| |
| Note that this cell is not optimized for performance. Please use |
| `tf.contrib.cudnn_rnn.CudnnLSTM` for better performance on GPU, or |
| `tf.contrib.rnn.LSTMBlockCell` and `tf.contrib.rnn.LSTMBlockFusedCell` for |
| better performance on CPU. |
| """ |
| |
| def __init__(self, num_units, |
| use_peepholes=False, cell_clip=None, |
| initializer=None, num_proj=None, proj_clip=None, |
| num_unit_shards=None, num_proj_shards=None, |
| forget_bias=1.0, state_is_tuple=True, |
| activation=None, reuse=None, name=None, dtype=None, **kwargs): |
| """Initialize the parameters for an LSTM cell. |
| |
| Args: |
| num_units: int, The number of units in the LSTM cell. |
| use_peepholes: bool, set True to enable diagonal/peephole connections. |
| cell_clip: (optional) A float value, if provided the cell state is clipped |
| by this value prior to the cell output activation. |
| initializer: (optional) The initializer to use for the weight and |
| projection matrices. |
| num_proj: (optional) int, The output dimensionality for the projection |
| matrices. If None, no projection is performed. |
| proj_clip: (optional) A float value. If `num_proj > 0` and `proj_clip` is |
| provided, then the projected values are clipped elementwise to within |
| `[-proj_clip, proj_clip]`. |
| num_unit_shards: Deprecated, will be removed by Jan. 2017. |
| Use a variable_scope partitioner instead. |
| num_proj_shards: Deprecated, will be removed by Jan. 2017. |
| Use a variable_scope partitioner instead. |
| forget_bias: Biases of the forget gate are initialized by default to 1 |
| in order to reduce the scale of forgetting at the beginning of |
| the training. Must set it manually to `0.0` when restoring from |
| CudnnLSTM trained checkpoints. |
| state_is_tuple: 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. This latter behavior will soon be deprecated. |
| activation: Activation function of the inner states. Default: `tanh`. It |
| could also be string that is within Keras activation function names. |
| reuse: (optional) Python boolean describing whether to reuse variables |
| in an existing scope. If not `True`, and the existing scope already has |
| the given variables, an error is raised. |
| name: String, the name of the layer. Layers with the same name will |
| share weights, but to avoid mistakes we require reuse=True in such |
| cases. |
| dtype: Default dtype of the layer (default of `None` means use the type |
| of the first input). Required when `build` is called before `call`. |
| **kwargs: Dict, keyword named properties for common layer attributes, like |
| `trainable` etc when constructing the cell from configs of get_config(). |
| |
| When restoring from CudnnLSTM-trained checkpoints, use |
| `CudnnCompatibleLSTMCell` instead. |
| """ |
| super(LSTMCell, self).__init__( |
| _reuse=reuse, name=name, dtype=dtype, **kwargs) |
| 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 num_unit_shards is not None or num_proj_shards is not None: |
| logging.warn( |
| "%s: The num_unit_shards and proj_unit_shards parameters are " |
| "deprecated and will be removed in Jan 2017. " |
| "Use a variable scope with a partitioner instead.", self) |
| if context.executing_eagerly() and context.num_gpus() > 0: |
| logging.warn("%s: Note that this cell is not optimized for performance. " |
| "Please use tf.contrib.cudnn_rnn.CudnnLSTM for better " |
| "performance on GPU.", self) |
| |
| # Inputs must be 2-dimensional. |
| self.input_spec = base_layer.InputSpec(ndim=2) |
| |
| self._num_units = num_units |
| self._use_peepholes = use_peepholes |
| self._cell_clip = cell_clip |
| self._initializer = initializers.get(initializer) |
| self._num_proj = num_proj |
| self._proj_clip = proj_clip |
| self._num_unit_shards = num_unit_shards |
| self._num_proj_shards = num_proj_shards |
| self._forget_bias = forget_bias |
| self._state_is_tuple = state_is_tuple |
| if activation: |
| self._activation = activations.get(activation) |
| else: |
| self._activation = math_ops.tanh |
| |
| if num_proj: |
| self._state_size = ( |
| LSTMStateTuple(num_units, num_proj) |
| if state_is_tuple else num_units + num_proj) |
| self._output_size = num_proj |
| else: |
| self._state_size = ( |
| LSTMStateTuple(num_units, num_units) |
| if state_is_tuple else 2 * num_units) |
| self._output_size = num_units |
| |
| @property |
| def state_size(self): |
| return self._state_size |
| |
| @property |
| def output_size(self): |
| return self._output_size |
| |
| @tf_utils.shape_type_conversion |
| def build(self, inputs_shape): |
| if inputs_shape[-1] is None: |
| raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" |
| % str(inputs_shape)) |
| |
| input_depth = inputs_shape[-1] |
| h_depth = self._num_units if self._num_proj is None else self._num_proj |
| maybe_partitioner = ( |
| partitioned_variables.fixed_size_partitioner(self._num_unit_shards) |
| if self._num_unit_shards is not None |
| else None) |
| self._kernel = self.add_variable( |
| _WEIGHTS_VARIABLE_NAME, |
| shape=[input_depth + h_depth, 4 * self._num_units], |
| initializer=self._initializer, |
| partitioner=maybe_partitioner) |
| if self.dtype is None: |
| initializer = init_ops.zeros_initializer |
| else: |
| initializer = init_ops.zeros_initializer(dtype=self.dtype) |
| self._bias = self.add_variable( |
| _BIAS_VARIABLE_NAME, |
| shape=[4 * self._num_units], |
| initializer=initializer) |
| if self._use_peepholes: |
| self._w_f_diag = self.add_variable("w_f_diag", shape=[self._num_units], |
| initializer=self._initializer) |
| self._w_i_diag = self.add_variable("w_i_diag", shape=[self._num_units], |
| initializer=self._initializer) |
| self._w_o_diag = self.add_variable("w_o_diag", shape=[self._num_units], |
| initializer=self._initializer) |
| |
| if self._num_proj is not None: |
| maybe_proj_partitioner = ( |
| partitioned_variables.fixed_size_partitioner(self._num_proj_shards) |
| if self._num_proj_shards is not None |
| else None) |
| self._proj_kernel = self.add_variable( |
| "projection/%s" % _WEIGHTS_VARIABLE_NAME, |
| shape=[self._num_units, self._num_proj], |
| initializer=self._initializer, |
| partitioner=maybe_proj_partitioner) |
| |
| self.built = True |
| |
| def call(self, inputs, state): |
| """Run one step of LSTM. |
| |
| Args: |
| inputs: input Tensor, must be 2-D, `[batch, input_size]`. |
| state: if `state_is_tuple` is False, this must be a state Tensor, |
| `2-D, [batch, state_size]`. If `state_is_tuple` is True, this must be a |
| tuple of state Tensors, both `2-D`, with column sizes `c_state` and |
| `m_state`. |
| |
| Returns: |
| A tuple containing: |
| |
| - A `2-D, [batch, output_dim]`, Tensor representing the output of the |
| LSTM after reading `inputs` when previous state was `state`. |
| Here output_dim is: |
| num_proj if num_proj was set, |
| num_units otherwise. |
| - Tensor(s) representing the new state of LSTM after reading `inputs` when |
| the previous state was `state`. Same type and shape(s) as `state`. |
| |
| Raises: |
| ValueError: If input size cannot be inferred from inputs via |
| static shape inference. |
| """ |
| num_proj = self._num_units if self._num_proj is None else self._num_proj |
| sigmoid = math_ops.sigmoid |
| |
| if self._state_is_tuple: |
| (c_prev, m_prev) = state |
| else: |
| c_prev = array_ops.slice(state, [0, 0], [-1, self._num_units]) |
| m_prev = array_ops.slice(state, [0, self._num_units], [-1, num_proj]) |
| |
| input_size = inputs.get_shape().with_rank(2)[1] |
| if input_size.value is None: |
| raise ValueError("Could not infer input size from inputs.get_shape()[-1]") |
| |
| # i = input_gate, j = new_input, f = forget_gate, o = output_gate |
| lstm_matrix = math_ops.matmul( |
| array_ops.concat([inputs, m_prev], 1), self._kernel) |
| lstm_matrix = nn_ops.bias_add(lstm_matrix, self._bias) |
| |
| i, j, f, o = array_ops.split( |
| value=lstm_matrix, num_or_size_splits=4, axis=1) |
| # Diagonal connections |
| if self._use_peepholes: |
| c = (sigmoid(f + self._forget_bias + self._w_f_diag * c_prev) * c_prev + |
| sigmoid(i + self._w_i_diag * c_prev) * self._activation(j)) |
| else: |
| c = (sigmoid(f + self._forget_bias) * c_prev + sigmoid(i) * |
| self._activation(j)) |
| |
| if self._cell_clip is not None: |
| # pylint: disable=invalid-unary-operand-type |
| c = clip_ops.clip_by_value(c, -self._cell_clip, self._cell_clip) |
| # pylint: enable=invalid-unary-operand-type |
| if self._use_peepholes: |
| m = sigmoid(o + self._w_o_diag * c) * self._activation(c) |
| else: |
| m = sigmoid(o) * self._activation(c) |
| |
| if self._num_proj is not None: |
| m = math_ops.matmul(m, self._proj_kernel) |
| |
| if self._proj_clip is not None: |
| # pylint: disable=invalid-unary-operand-type |
| m = clip_ops.clip_by_value(m, -self._proj_clip, self._proj_clip) |
| # pylint: enable=invalid-unary-operand-type |
| |
| new_state = (LSTMStateTuple(c, m) if self._state_is_tuple else |
| array_ops.concat([c, m], 1)) |
| return m, new_state |
| |
| def get_config(self): |
| config = { |
| "num_units": self._num_units, |
| "use_peepholes": self._use_peepholes, |
| "cell_clip": self._cell_clip, |
| "initializer": initializers.serialize(self._initializer), |
| "num_proj": self._num_proj, |
| "proj_clip": self._proj_clip, |
| "num_unit_shards": self._num_unit_shards, |
| "num_proj_shards": self._num_proj_shards, |
| "forget_bias": self._forget_bias, |
| "state_is_tuple": self._state_is_tuple, |
| "activation": activations.serialize(self._activation), |
| "reuse": self._reuse, |
| } |
| base_config = super(LSTMCell, self).get_config() |
| return dict(list(base_config.items()) + list(config.items())) |
| |
| |
| def _enumerated_map_structure_up_to(shallow_structure, map_fn, *args, **kwargs): |
| ix = [0] |
| def enumerated_fn(*inner_args, **inner_kwargs): |
| r = map_fn(ix[0], *inner_args, **inner_kwargs) |
| ix[0] += 1 |
| return r |
| return nest.map_structure_up_to(shallow_structure, |
| enumerated_fn, *args, **kwargs) |
| |
| |
| def _default_dropout_state_filter_visitor(substate): |
| if isinstance(substate, LSTMStateTuple): |
| # Do not perform dropout on the memory state. |
| return LSTMStateTuple(c=False, h=True) |
| elif isinstance(substate, tensor_array_ops.TensorArray): |
| return False |
| return True |
| |
| |
| @tf_export("nn.rnn_cell.DropoutWrapper") |
| class DropoutWrapper(RNNCell): |
| """Operator adding dropout to inputs and outputs of the given cell.""" |
| |
| def __init__(self, cell, input_keep_prob=1.0, output_keep_prob=1.0, |
| state_keep_prob=1.0, variational_recurrent=False, |
| input_size=None, dtype=None, seed=None, |
| dropout_state_filter_visitor=None): |
| """Create a cell with added input, state, and/or output dropout. |
| |
| If `variational_recurrent` is set to `True` (**NOT** the default behavior), |
| then the same dropout mask is applied at every step, as described in: |
| |
| Y. Gal, Z Ghahramani. "A Theoretically Grounded Application of Dropout in |
| Recurrent Neural Networks". https://arxiv.org/abs/1512.05287 |
| |
| Otherwise a different dropout mask is applied at every time step. |
| |
| Note, by default (unless a custom `dropout_state_filter` is provided), |
| the memory state (`c` component of any `LSTMStateTuple`) passing through |
| a `DropoutWrapper` is never modified. This behavior is described in the |
| above article. |
| |
| Args: |
| cell: an RNNCell, a projection to output_size is added to it. |
| input_keep_prob: unit Tensor or float between 0 and 1, input keep |
| probability; if it is constant and 1, no input dropout will be added. |
| output_keep_prob: unit Tensor or float between 0 and 1, output keep |
| probability; if it is constant and 1, no output dropout will be added. |
| state_keep_prob: unit Tensor or float between 0 and 1, output keep |
| probability; if it is constant and 1, no output dropout will be added. |
| State dropout is performed on the outgoing states of the cell. |
| **Note** the state components to which dropout is applied when |
| `state_keep_prob` is in `(0, 1)` are also determined by |
| the argument `dropout_state_filter_visitor` (e.g. by default dropout |
| is never applied to the `c` component of an `LSTMStateTuple`). |
| variational_recurrent: Python bool. If `True`, then the same |
| dropout pattern is applied across all time steps per run call. |
| If this parameter is set, `input_size` **must** be provided. |
| input_size: (optional) (possibly nested tuple of) `TensorShape` objects |
| containing the depth(s) of the input tensors expected to be passed in to |
| the `DropoutWrapper`. Required and used **iff** |
| `variational_recurrent = True` and `input_keep_prob < 1`. |
| dtype: (optional) The `dtype` of the input, state, and output tensors. |
| Required and used **iff** `variational_recurrent = True`. |
| seed: (optional) integer, the randomness seed. |
| dropout_state_filter_visitor: (optional), default: (see below). Function |
| that takes any hierarchical level of the state and returns |
| a scalar or depth=1 structure of Python booleans describing |
| which terms in the state should be dropped out. In addition, if the |
| function returns `True`, dropout is applied across this sublevel. If |
| the function returns `False`, dropout is not applied across this entire |
| sublevel. |
| Default behavior: perform dropout on all terms except the memory (`c`) |
| state of `LSTMCellState` objects, and don't try to apply dropout to |
| `TensorArray` objects: |
| ``` |
| def dropout_state_filter_visitor(s): |
| if isinstance(s, LSTMCellState): |
| # Never perform dropout on the c state. |
| return LSTMCellState(c=False, h=True) |
| elif isinstance(s, TensorArray): |
| return False |
| return True |
| ``` |
| |
| Raises: |
| TypeError: if `cell` is not an `RNNCell`, or `keep_state_fn` is provided |
| but not `callable`. |
| ValueError: if any of the keep_probs are not between 0 and 1. |
| """ |
| super(DropoutWrapper, self).__init__() |
| assert_like_rnncell("cell", cell) |
| |
| if (dropout_state_filter_visitor is not None |
| and not callable(dropout_state_filter_visitor)): |
| raise TypeError("dropout_state_filter_visitor must be callable") |
| self._dropout_state_filter = ( |
| dropout_state_filter_visitor or _default_dropout_state_filter_visitor) |
| with ops.name_scope("DropoutWrapperInit"): |
| def tensor_and_const_value(v): |
| tensor_value = ops.convert_to_tensor(v) |
| const_value = tensor_util.constant_value(tensor_value) |
| return (tensor_value, const_value) |
| for prob, attr in [(input_keep_prob, "input_keep_prob"), |
| (state_keep_prob, "state_keep_prob"), |
| (output_keep_prob, "output_keep_prob")]: |
| tensor_prob, const_prob = tensor_and_const_value(prob) |
| if const_prob is not None: |
| if const_prob < 0 or const_prob > 1: |
| raise ValueError("Parameter %s must be between 0 and 1: %d" |
| % (attr, const_prob)) |
| setattr(self, "_%s" % attr, float(const_prob)) |
| else: |
| setattr(self, "_%s" % attr, tensor_prob) |
| |
| # Set cell, variational_recurrent, seed before running the code below |
| self._cell = cell |
| if isinstance(cell, checkpointable.CheckpointableBase): |
| self._track_checkpointable(self._cell, name="cell") |
| self._variational_recurrent = variational_recurrent |
| self._seed = seed |
| |
| self._recurrent_input_noise = None |
| self._recurrent_state_noise = None |
| self._recurrent_output_noise = None |
| |
| if variational_recurrent: |
| if dtype is None: |
| raise ValueError( |
| "When variational_recurrent=True, dtype must be provided") |
| |
| def convert_to_batch_shape(s): |
| # Prepend a 1 for the batch dimension; for recurrent |
| # variational dropout we use the same dropout mask for all |
| # batch elements. |
| return array_ops.concat( |
| ([1], tensor_shape.TensorShape(s).as_list()), 0) |
| |
| def batch_noise(s, inner_seed): |
| shape = convert_to_batch_shape(s) |
| return random_ops.random_uniform(shape, seed=inner_seed, dtype=dtype) |
| |
| if (not isinstance(self._input_keep_prob, numbers.Real) or |
| self._input_keep_prob < 1.0): |
| if input_size is None: |
| raise ValueError( |
| "When variational_recurrent=True and input_keep_prob < 1.0 or " |
| "is unknown, input_size must be provided") |
| self._recurrent_input_noise = _enumerated_map_structure_up_to( |
| input_size, |
| lambda i, s: batch_noise(s, inner_seed=self._gen_seed("input", i)), |
| input_size) |
| self._recurrent_state_noise = _enumerated_map_structure_up_to( |
| cell.state_size, |
| lambda i, s: batch_noise(s, inner_seed=self._gen_seed("state", i)), |
| cell.state_size) |
| self._recurrent_output_noise = _enumerated_map_structure_up_to( |
| cell.output_size, |
| lambda i, s: batch_noise(s, inner_seed=self._gen_seed("output", i)), |
| cell.output_size) |
| |
| def _gen_seed(self, salt_prefix, index): |
| if self._seed is None: |
| return None |
| salt = "%s_%d" % (salt_prefix, index) |
| string = (str(self._seed) + salt).encode("utf-8") |
| return int(hashlib.md5(string).hexdigest()[:8], 16) & 0x7FFFFFFF |
| |
| @property |
| def wrapped_cell(self): |
| return self._cell |
| |
| @property |
| def state_size(self): |
| return self._cell.state_size |
| |
| @property |
| def output_size(self): |
| return self._cell.output_size |
| |
| def zero_state(self, batch_size, dtype): |
| with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]): |
| return self._cell.zero_state(batch_size, dtype) |
| |
| def _variational_recurrent_dropout_value( |
| self, index, value, noise, keep_prob): |
| """Performs dropout given the pre-calculated noise tensor.""" |
| # uniform [keep_prob, 1.0 + keep_prob) |
| random_tensor = keep_prob + noise |
| |
| # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob) |
| binary_tensor = math_ops.floor(random_tensor) |
| ret = math_ops.div(value, keep_prob) * binary_tensor |
| ret.set_shape(value.get_shape()) |
| return ret |
| |
| def _dropout(self, values, salt_prefix, recurrent_noise, keep_prob, |
| shallow_filtered_substructure=None): |
| """Decides whether to perform standard dropout or recurrent dropout.""" |
| |
| if shallow_filtered_substructure is None: |
| # Put something so we traverse the entire structure; inside the |
| # dropout function we check to see if leafs of this are bool or not. |
| shallow_filtered_substructure = values |
| |
| if not self._variational_recurrent: |
| def dropout(i, do_dropout, v): |
| if not isinstance(do_dropout, bool) or do_dropout: |
| return nn_ops.dropout( |
| v, keep_prob=keep_prob, seed=self._gen_seed(salt_prefix, i)) |
| else: |
| return v |
| return _enumerated_map_structure_up_to( |
| shallow_filtered_substructure, dropout, |
| *[shallow_filtered_substructure, values]) |
| else: |
| def dropout(i, do_dropout, v, n): |
| if not isinstance(do_dropout, bool) or do_dropout: |
| return self._variational_recurrent_dropout_value(i, v, n, keep_prob) |
| else: |
| return v |
| return _enumerated_map_structure_up_to( |
| shallow_filtered_substructure, dropout, |
| *[shallow_filtered_substructure, values, recurrent_noise]) |
| |
| def __call__(self, inputs, state, scope=None): |
| """Run the cell with the declared dropouts.""" |
| def _should_dropout(p): |
| return (not isinstance(p, float)) or p < 1 |
| |
| if _should_dropout(self._input_keep_prob): |
| inputs = self._dropout(inputs, "input", |
| self._recurrent_input_noise, |
| self._input_keep_prob) |
| output, new_state = self._cell(inputs, state, scope=scope) |
| if _should_dropout(self._state_keep_prob): |
| # Identify which subsets of the state to perform dropout on and |
| # which ones to keep. |
| shallow_filtered_substructure = nest.get_traverse_shallow_structure( |
| self._dropout_state_filter, new_state) |
| new_state = self._dropout(new_state, "state", |
| self._recurrent_state_noise, |
| self._state_keep_prob, |
| shallow_filtered_substructure) |
| if _should_dropout(self._output_keep_prob): |
| output = self._dropout(output, "output", |
| self._recurrent_output_noise, |
| self._output_keep_prob) |
| return output, new_state |
| |
| |
| @tf_export("nn.rnn_cell.ResidualWrapper") |
| class ResidualWrapper(RNNCell): |
| """RNNCell wrapper that ensures cell inputs are added to the outputs.""" |
| |
| def __init__(self, cell, residual_fn=None): |
| """Constructs a `ResidualWrapper` for `cell`. |
| |
| Args: |
| cell: An instance of `RNNCell`. |
| residual_fn: (Optional) The function to map raw cell inputs and raw cell |
| outputs to the actual cell outputs of the residual network. |
| Defaults to calling nest.map_structure on (lambda i, o: i + o), inputs |
| and outputs. |
| """ |
| super(ResidualWrapper, self).__init__() |
| self._cell = cell |
| if isinstance(cell, checkpointable.CheckpointableBase): |
| self._track_checkpointable(self._cell, name="cell") |
| self._residual_fn = residual_fn |
| |
| @property |
| def state_size(self): |
| return self._cell.state_size |
| |
| @property |
| def output_size(self): |
| return self._cell.output_size |
| |
| def zero_state(self, batch_size, dtype): |
| with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]): |
| return self._cell.zero_state(batch_size, dtype) |
| |
| def __call__(self, inputs, state, scope=None): |
| """Run the cell and then apply the residual_fn on its inputs to its outputs. |
| |
| Args: |
| inputs: cell inputs. |
| state: cell state. |
| scope: optional cell scope. |
| |
| Returns: |
| Tuple of cell outputs and new state. |
| |
| Raises: |
| TypeError: If cell inputs and outputs have different structure (type). |
| ValueError: If cell inputs and outputs have different structure (value). |
| """ |
| outputs, new_state = self._cell(inputs, state, scope=scope) |
| # Ensure shapes match |
| def assert_shape_match(inp, out): |
| inp.get_shape().assert_is_compatible_with(out.get_shape()) |
| def default_residual_fn(inputs, outputs): |
| nest.assert_same_structure(inputs, outputs) |
| nest.map_structure(assert_shape_match, inputs, outputs) |
| return nest.map_structure(lambda inp, out: inp + out, inputs, outputs) |
| res_outputs = (self._residual_fn or default_residual_fn)(inputs, outputs) |
| return (res_outputs, new_state) |
| |
| |
| @tf_export("nn.rnn_cell.DeviceWrapper") |
| class DeviceWrapper(RNNCell): |
| """Operator that ensures an RNNCell runs on a particular device.""" |
| |
| def __init__(self, cell, device): |
| """Construct a `DeviceWrapper` for `cell` with device `device`. |
| |
| Ensures the wrapped `cell` is called with `tf.device(device)`. |
| |
| Args: |
| cell: An instance of `RNNCell`. |
| device: A device string or function, for passing to `tf.device`. |
| """ |
| super(DeviceWrapper, self).__init__() |
| self._cell = cell |
| if isinstance(cell, checkpointable.CheckpointableBase): |
| self._track_checkpointable(self._cell, name="cell") |
| self._device = device |
| |
| @property |
| def state_size(self): |
| return self._cell.state_size |
| |
| @property |
| def output_size(self): |
| return self._cell.output_size |
| |
| def zero_state(self, batch_size, dtype): |
| with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]): |
| with ops.device(self._device): |
| return self._cell.zero_state(batch_size, dtype) |
| |
| def __call__(self, inputs, state, scope=None): |
| """Run the cell on specified device.""" |
| with ops.device(self._device): |
| return self._cell(inputs, state, scope=scope) |
| |
| |
| @tf_export("nn.rnn_cell.MultiRNNCell") |
| class MultiRNNCell(RNNCell): |
| """RNN cell composed sequentially of multiple simple cells. |
| |
| Example: |
| |
| ```python |
| num_units = [128, 64] |
| cells = [BasicLSTMCell(num_units=n) for n in num_units] |
| stacked_rnn_cell = MultiRNNCell(cells) |
| ``` |
| """ |
| |
| def __init__(self, cells, state_is_tuple=True): |
| """Create a RNN cell composed sequentially of a number of RNNCells. |
| |
| Args: |
| cells: list of RNNCells that will be composed in this order. |
| state_is_tuple: If True, accepted and returned states are n-tuples, where |
| `n = len(cells)`. If False, the states are all |
| concatenated along the column axis. This latter behavior will soon be |
| deprecated. |
| |
| Raises: |
| ValueError: if cells is empty (not allowed), or at least one of the cells |
| returns a state tuple but the flag `state_is_tuple` is `False`. |
| """ |
| super(MultiRNNCell, self).__init__() |
| if not cells: |
| raise ValueError("Must specify at least one cell for MultiRNNCell.") |
| if not nest.is_sequence(cells): |
| raise TypeError( |
| "cells must be a list or tuple, but saw: %s." % cells) |
| |
| if len(set([id(cell) for cell in cells])) < len(cells): |
| logging.log_first_n(logging.WARN, |
| "At least two cells provided to MultiRNNCell " |
| "are the same object and will share weights.", 1) |
| |
| self._cells = cells |
| for cell_number, cell in enumerate(self._cells): |
| # Add Checkpointable dependencies on these cells so their variables get |
| # saved with this object when using object-based saving. |
| if isinstance(cell, checkpointable.CheckpointableBase): |
| # TODO(allenl): Track down non-Checkpointable callers. |
| self._track_checkpointable(cell, name="cell-%d" % (cell_number,)) |
| self._state_is_tuple = state_is_tuple |
| if not state_is_tuple: |
| if any(nest.is_sequence(c.state_size) for c in self._cells): |
| raise ValueError("Some cells return tuples of states, but the flag " |
| "state_is_tuple is not set. State sizes are: %s" |
| % str([c.state_size for c in self._cells])) |
| |
| @property |
| def state_size(self): |
| if self._state_is_tuple: |
| return tuple(cell.state_size for cell in self._cells) |
| else: |
| return sum([cell.state_size for cell in self._cells]) |
| |
| @property |
| def output_size(self): |
| return self._cells[-1].output_size |
| |
| def zero_state(self, batch_size, dtype): |
| with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]): |
| if self._state_is_tuple: |
| return tuple(cell.zero_state(batch_size, dtype) for cell in self._cells) |
| else: |
| # We know here that state_size of each cell is not a tuple and |
| # presumably does not contain TensorArrays or anything else fancy |
| return super(MultiRNNCell, self).zero_state(batch_size, dtype) |
| |
| def call(self, inputs, state): |
| """Run this multi-layer cell on inputs, starting from state.""" |
| cur_state_pos = 0 |
| cur_inp = inputs |
| new_states = [] |
| for i, cell in enumerate(self._cells): |
| with vs.variable_scope("cell_%d" % i): |
| if self._state_is_tuple: |
| if not nest.is_sequence(state): |
| raise ValueError( |
| "Expected state to be a tuple of length %d, but received: %s" % |
| (len(self.state_size), state)) |
| cur_state = state[i] |
| else: |
| cur_state = array_ops.slice(state, [0, cur_state_pos], |
| [-1, cell.state_size]) |
| cur_state_pos += cell.state_size |
| cur_inp, new_state = cell(cur_inp, cur_state) |
| new_states.append(new_state) |
| |
| new_states = (tuple(new_states) if self._state_is_tuple else |
| array_ops.concat(new_states, 1)) |
| |
| return cur_inp, new_states |
