from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import math from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import embedding_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 variable_scope as vs from tensorflow.python.ops.math_ops import sigmoid from tensorflow.python.ops.math_ops import tanh from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import nest def _state_size_with_prefix(state_size, prefix=None): """Helper function that enables int or TensorShape shape specification. This function takes a size specification, which can be an integer or a TensorShape, and converts it into a list of integers. One may specify any additional dimensions that precede the final state size specification. Args: state_size: TensorShape or int that specifies the size of a tensor. prefix: optional additional list of dimensions to prepend. Returns: result_state_size: list of dimensions the resulting tensor size. """ result_state_size = tensor_shape.as_shape(state_size).as_list() if prefix is not None: if not isinstance(prefix, list): raise TypeError("prefix of _state_size_with_prefix should be a list.") result_state_size = prefix + result_state_size return result_state_size class RNNCell(object): """Abstract object representing an RNN cell. The definition of cell in this package differs from the definition used in the literature. In the literature, cell refers to an object with a single scalar output. The definition in this package 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 tuple of integers, then it results in a tuple of `len(state_size)` state matrices, each with a column size corresponding to values in `state_size`. 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 add dropouts, projections, or embeddings for inputs. Constructing multi-layer cells is supported by the class `MultiRNNCell`, or by calling the `rnn` ops several times. Every `RNNCell` must have the properties below and and implement `__call__` with the following signature. """ 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 x input_size]`. state: if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size x self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size x 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 x self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`. """ raise NotImplementedError("Abstract method") @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 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 x 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 x s]` for each s in `state_size`. """ state_size = self.state_size if nest.is_sequence(state_size): state_size_flat = nest.flatten(state_size) zeros_flat = [ array_ops.zeros( array_ops.pack(_state_size_with_prefix(s, prefix=[batch_size])), dtype=dtype) for s in state_size_flat] for s, z in zip(state_size_flat, zeros_flat): z.set_shape(_state_size_with_prefix(s, prefix=[None])) zeros = nest.pack_sequence_as(structure=state_size, flat_sequence=zeros_flat) else: zeros_size = _state_size_with_prefix(state_size, prefix=[batch_size]) zeros = array_ops.zeros(array_ops.pack(zeros_size), dtype=dtype) zeros.set_shape(_state_size_with_prefix(state_size, prefix=[None])) return zeros class BasicRNNCell(RNNCell): """The most basic RNN cell.""" def __init__(self, num_units, input_size=None, activation=tanh): if input_size is not None: logging.warn("%s: The input_size parameter is deprecated.", self) self._num_units = num_units self._activation = activation @property def state_size(self): return self._num_units @property def output_size(self): return self._num_units def __call__(self, inputs, state, scope=None): """Most basic RNN: output = new_state = activation(W * input + U * state + B).""" with vs.variable_scope(scope or type(self).__name__): # "BasicRNNCell" output = self._activation(_linear([inputs, state], self._num_units, True)) return output, output class MEMGRUCell(RNNCell): """Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078).""" def __init__(self, num_units, input_size=None, activation=tanh): if input_size is not None: logging.warn("%s: The input_size parameter is deprecated.", self) self._num_units = num_units self._activation = activation @property def state_size(self): return self._num_units @property def output_size(self): return self._num_units def __call__(self, inputs, state, emotion, imemory, scope=None): """Gated recurrent unit (GRU) with nunits cells.""" params = [] if emotion is not None: params.append(emotion) if imemory is not None: params.append(imemory) with vs.variable_scope(scope or 'GRUCell'): # "GRUCell" with vs.variable_scope("Emotion_Imemory"): # Reset gate and update gate. # We start with bias of 1.0 to not reset and not update. _r, _u, _c = array_ops.split(1, 3, _linear(params, 3 * self._num_units, False)) with vs.variable_scope("Gates"): # Reset gate and update gate. # We start with bias of 1.0 to not reset and not update. r, u = array_ops.split(1, 2, _linear([inputs, state], 2 * self._num_units, True, 1.0)) r, u = sigmoid(r+_r), sigmoid(u+_u) with vs.variable_scope("Candidate"): c = self._activation(_c+_linear([inputs, r * state], self._num_units, True)) new_h = u * state + (1 - u) * c return new_h, new_h _LSTMStateTuple = collections.namedtuple("LSTMStateTuple", ("c", "h")) class LSTMStateTuple(_LSTMStateTuple): """Tuple used by LSTM Cells for `state_size`, `zero_state`, and output state. Stores two elements: `(c, h)`, in that order. Only used when `state_is_tuple=True`. """ __slots__ = () @property def dtype(self): (c, h) = self if not c.dtype == h.dtype: raise TypeError("Inconsistent internal state: %s vs %s" % (str(c.dtype), str(h.dtype))) return c.dtype class BasicLSTMCell(RNNCell): """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 LSTMCell that follows. """ def __init__(self, num_units, forget_bias=1.0, input_size=None, state_is_tuple=True, activation=tanh): """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). input_size: Deprecated and unused. 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. """ 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._num_units = num_units self._forget_bias = forget_bias self._state_is_tuple = state_is_tuple self._activation = activation @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 def __call__(self, inputs, state, scope=None): """Long short-term memory cell (LSTM).""" with vs.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: c, h = array_ops.split(1, 2, state) concat = _linear([inputs, h], 4 * self._num_units, True) # i = input_gate, j = new_input, f = forget_gate, o = output_gate i, j, f, o = array_ops.split(1, 4, concat) new_c = (c * sigmoid(f + self._forget_bias) + sigmoid(i) * self._activation(j)) new_h = self._activation(new_c) * sigmoid(o) if self._state_is_tuple: new_state = LSTMStateTuple(new_c, new_h) else: new_state = array_ops.concat(1, [new_c, new_h]) return new_h, new_state def _get_concat_variable(name, shape, dtype, num_shards): """Get a sharded variable concatenated into one tensor.""" sharded_variable = _get_sharded_variable(name, shape, dtype, num_shards) if len(sharded_variable) == 1: return sharded_variable[0] concat_name = name + "/concat" concat_full_name = vs.get_variable_scope().name + "/" + concat_name + ":0" for value in ops.get_collection(ops.GraphKeys.CONCATENATED_VARIABLES): if value.name == concat_full_name: return value concat_variable = array_ops.concat(0, sharded_variable, name=concat_name) ops.add_to_collection(ops.GraphKeys.CONCATENATED_VARIABLES, concat_variable) return concat_variable def _get_sharded_variable(name, shape, dtype, num_shards): """Get a list of sharded variables with the given dtype.""" if num_shards > shape[0]: raise ValueError("Too many shards: shape=%s, num_shards=%d" % (shape, num_shards)) unit_shard_size = int(math.floor(shape[0] / num_shards)) remaining_rows = shape[0] - unit_shard_size * num_shards shards = [] for i in range(num_shards): current_size = unit_shard_size if i < remaining_rows: current_size += 1 shards.append(vs.get_variable(name + "_%d" % i, [current_size] + shape[1:], dtype=dtype)) return shards class LSTMCell(RNNCell): """Long short-term memory unit (LSTM) recurrent network cell. The default non-peephole implementation is based on: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997. 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. """ def __init__(self, num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=True, activation=tanh): """Initialize the parameters for an LSTM cell. Args: num_units: int, The number of units in the LSTM cell input_size: Deprecated and unused. 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: How to split the weight matrix. If >1, the weight matrix is stored across num_unit_shards. num_proj_shards: How to split the projection matrix. If >1, the projection matrix is stored across num_proj_shards. 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. 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. """ 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._num_units = num_units self._use_peepholes = use_peepholes self._cell_clip = cell_clip self._initializer = 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 self._activation = activation 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 def __call__(self, inputs, state, scope=None): """Run one step of LSTM. Args: inputs: input Tensor, 2D, batch x num_units. state: if `state_is_tuple` is False, this must be a state Tensor, `2-D, batch x 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`. scope: VariableScope for the created subgraph; defaults to "LSTMCell". Returns: A tuple containing: - A `2-D, [batch x 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 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]) dtype = inputs.dtype 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]") with vs.variable_scope(scope or type(self).__name__, initializer=self._initializer): # "LSTMCell" concat_w = _get_concat_variable( "W", [input_size.value + num_proj, 4 * self._num_units], dtype, self._num_unit_shards) b = vs.get_variable( "B", shape=[4 * self._num_units], initializer=init_ops.zeros_initializer, dtype=dtype) # i = input_gate, j = new_input, f = forget_gate, o = output_gate cell_inputs = array_ops.concat(1, [inputs, m_prev]) lstm_matrix = nn_ops.bias_add(math_ops.matmul(cell_inputs, concat_w), b) i, j, f, o = array_ops.split(1, 4, lstm_matrix) # Diagonal connections if self._use_peepholes: w_f_diag = vs.get_variable( "W_F_diag", shape=[self._num_units], dtype=dtype) w_i_diag = vs.get_variable( "W_I_diag", shape=[self._num_units], dtype=dtype) w_o_diag = vs.get_variable( "W_O_diag", shape=[self._num_units], dtype=dtype) if self._use_peepholes: c = (sigmoid(f + self._forget_bias + w_f_diag * c_prev) * c_prev + sigmoid(i + 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 + w_o_diag * c) * self._activation(c) else: m = sigmoid(o) * self._activation(c) if self._num_proj is not None: concat_w_proj = _get_concat_variable( "W_P", [self._num_units, self._num_proj], dtype, self._num_proj_shards) m = math_ops.matmul(m, concat_w_proj) 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(1, [c, m])) return m, new_state class OutputProjectionWrapper(RNNCell): """Operator adding an output projection to the given cell. Note: in many cases it may be more efficient to not use this wrapper, but instead concatenate the whole sequence of your outputs in time, do the projection on this batch-concatenated sequence, then split it if needed or directly feed into a softmax. """ def __init__(self, cell, output_size): """Create a cell with output projection. Args: cell: an RNNCell, a projection to output_size is added to it. output_size: integer, the size of the output after projection. Raises: TypeError: if cell is not an RNNCell. ValueError: if output_size is not positive. """ if not isinstance(cell, RNNCell): raise TypeError("The parameter cell is not RNNCell.") if output_size < 1: raise ValueError("Parameter output_size must be > 0: %d." % output_size) self._cell = cell self._output_size = output_size @property def state_size(self): return self._cell.state_size @property def output_size(self): return self._output_size def __call__(self, inputs, state, emotion, imemory, scope=None): """Run the cell and output projection on inputs, starting from state.""" output, res_state, memory = self._cell(inputs, state, emotion, imemory) # Default scope: "OutputProjectionWrapper" with vs.variable_scope(scope or type(self).__name__): projected = _linear(output, self._output_size, True) return projected, res_state, memory class InputProjectionWrapper(RNNCell): """Operator adding an input projection to the given cell. Note: in many cases it may be more efficient to not use this wrapper, but instead concatenate the whole sequence of your inputs in time, do the projection on this batch-concatenated sequence, then split it. """ def __init__(self, cell, num_proj, input_size=None): """Create a cell with input projection. Args: cell: an RNNCell, a projection of inputs is added before it. num_proj: Python integer. The dimension to project to. input_size: Deprecated and unused. Raises: TypeError: if cell is not an RNNCell. """ if input_size is not None: logging.warn("%s: The input_size parameter is deprecated.", self) if not isinstance(cell, RNNCell): raise TypeError("The parameter cell is not RNNCell.") self._cell = cell self._num_proj = num_proj @property def state_size(self): return self._cell.state_size @property def output_size(self): return self._cell.output_size def __call__(self, inputs, state, scope=None): """Run the input projection and then the cell.""" # Default scope: "InputProjectionWrapper" with vs.variable_scope(scope or type(self).__name__): projected = _linear(inputs, self._num_proj, True) return self._cell(projected, state) 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, seed=None): """Create a cell with added input and/or output dropout. Dropout is never used on the state. 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 float 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 float and 1, no output dropout will be added. seed: (optional) integer, the randomness seed. Raises: TypeError: if cell is not an RNNCell. ValueError: if keep_prob is not between 0 and 1. """ if not isinstance(cell, RNNCell): raise TypeError("The parameter cell is not a RNNCell.") if (isinstance(input_keep_prob, float) and not (input_keep_prob >= 0.0 and input_keep_prob <= 1.0)): raise ValueError("Parameter input_keep_prob must be between 0 and 1: %d" % input_keep_prob) if (isinstance(output_keep_prob, float) and not (output_keep_prob >= 0.0 and output_keep_prob <= 1.0)): raise ValueError("Parameter output_keep_prob must be between 0 and 1: %d" % output_keep_prob) self._cell = cell self._input_keep_prob = input_keep_prob self._output_keep_prob = output_keep_prob self._seed = seed @property def state_size(self): return self._cell.state_size @property def output_size(self): return self._cell.output_size def __call__(self, inputs, state, scope=None): """Run the cell with the declared dropouts.""" if (not isinstance(self._input_keep_prob, float) or self._input_keep_prob < 1): inputs = nn_ops.dropout(inputs, self._input_keep_prob, seed=self._seed) output, new_state = self._cell(inputs, state, scope) if (not isinstance(self._output_keep_prob, float) or self._output_keep_prob < 1): output = nn_ops.dropout(output, self._output_keep_prob, seed=self._seed) return output, new_state class EmbeddingWrapper(RNNCell): """Operator adding input embedding to the given cell. Note: in many cases it may be more efficient to not use this wrapper, but instead concatenate the whole sequence of your inputs in time, do the embedding on this batch-concatenated sequence, then split it and feed into your RNN. """ def __init__(self, cell, embedding_classes, embedding_size, initializer=None): """Create a cell with an added input embedding. Args: cell: an RNNCell, an embedding will be put before its inputs. embedding_classes: integer, how many symbols will be embedded. embedding_size: integer, the size of the vectors we embed into. initializer: an initializer to use when creating the embedding; if None, the initializer from variable scope or a default one is used. Raises: TypeError: if cell is not an RNNCell. ValueError: if embedding_classes is not positive. """ if not isinstance(cell, RNNCell): raise TypeError("The parameter cell is not RNNCell.") if embedding_classes <= 0 or embedding_size <= 0: raise ValueError("Both embedding_classes and embedding_size must be > 0: " "%d, %d." % (embedding_classes, embedding_size)) self._cell = cell self._embedding_classes = embedding_classes self._embedding_size = embedding_size self._initializer = initializer @property def state_size(self): return self._cell.state_size @property def output_size(self): return self._cell.output_size def __call__(self, inputs, state, scope=None): """Run the cell on embedded inputs.""" with vs.variable_scope(scope or type(self).__name__): # "EmbeddingWrapper" with ops.device("/cpu:0"): if self._initializer: initializer = self._initializer elif vs.get_variable_scope().initializer: initializer = vs.get_variable_scope().initializer else: # Default initializer for embeddings should have variance=1. sqrt3 = math.sqrt(3) # Uniform(-sqrt(3), sqrt(3)) has variance=1. initializer = init_ops.random_uniform_initializer(-sqrt3, sqrt3) if type(state) is tuple: data_type = state[0].dtype else: data_type = state.dtype embedding = vs.get_variable( "embedding", [self._embedding_classes, self._embedding_size], initializer=initializer, dtype=data_type) embedded = embedding_ops.embedding_lookup( embedding, array_ops.reshape(inputs, [-1])) return self._cell(embedded, state) class MEMMultiRNNCell(RNNCell): """RNN cell composed sequentially of multiple simple 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`. """ if not cells: raise ValueError("Must specify at least one cell for MultiRNNCell.") self._cells = cells 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 __call__(self, inputs, state, emotion, imemory, scope=None): """Run this multi-layer cell on inputs, starting from state.""" if emotion is None: x = [inputs] + [ i for i in state] else: x = [inputs, emotion] + [ i for i in state] if imemory is not None: with vs.variable_scope(scope or 'IMemoryReadGate'): r = sigmoid(_linear(x, imemory.get_shape().with_rank(2)[1], True, 1.0)) with vs.variable_scope(scope or 'MultiRNNCell'): # "MultiRNNCell" 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 if i == 0: if imemory is None: cur_inp, new_state = cell(cur_inp, cur_state, emotion, imemory) else: cur_inp, new_state = cell(cur_inp, cur_state, emotion, r * imemory) else: 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(1, new_states)) new_imemory = imemory if imemory is not None: with vs.variable_scope(scope or 'IMemoryWriteGate'): w = sigmoid(_linear(new_states, imemory.get_shape().with_rank(2)[1], True, 1.0)) new_imemory = w * imemory return cur_inp, new_states, new_imemory class _SlimRNNCell(RNNCell): """A simple wrapper for slim.rnn_cells.""" def __init__(self, cell_fn): """Create a SlimRNNCell from a cell_fn. Args: cell_fn: a function which takes (inputs, state, scope) and produces the outputs and the new_state. Additionally when called with inputs=None and state=None it should return (initial_outputs, initial_state). Raises: TypeError: if cell_fn is not callable ValueError: if cell_fn cannot produce a valid initial state. """ if not callable(cell_fn): raise TypeError("cell_fn %s needs to be callable", cell_fn) self._cell_fn = cell_fn self._cell_name = cell_fn.func.__name__ init_output, init_state = self._cell_fn(None, None) output_shape = init_output.get_shape() state_shape = init_state.get_shape() self._output_size = output_shape.with_rank(2)[1].value self._state_size = state_shape.with_rank(2)[1].value if self._output_size is None: raise ValueError("Initial output created by %s has invalid shape %s" % (self._cell_name, output_shape)) if self._state_size is None: raise ValueError("Initial state created by %s has invalid shape %s" % (self._cell_name, state_shape)) @property def state_size(self): return self._state_size @property def output_size(self): return self._output_size def __call__(self, inputs, state, scope=None): scope = scope or self._cell_name output, state = self._cell_fn(inputs, state, scope=scope) return output, state def _linear(args, output_size, bias, bias_start=0.0, scope=None): """Linear map: sum_i(args[i] * W[i]), where W[i] is a variable. Args: args: a 2D Tensor or a list of 2D, batch x n, Tensors. output_size: int, second dimension of W[i]. bias: boolean, whether to add a bias term or not. bias_start: starting value to initialize the bias; 0 by default. scope: VariableScope for the created subgraph; defaults to "Linear". Returns: A 2D Tensor with shape [batch x output_size] equal to sum_i(args[i] * W[i]), where W[i]s are newly created matrices. Raises: ValueError: if some of the arguments has unspecified or wrong shape. """ if args is None or (nest.is_sequence(args) and not args): raise ValueError("`args` must be specified") if not nest.is_sequence(args): args = [args] # Calculate the total size of arguments on dimension 1. total_arg_size = 0 shapes = [a.get_shape().as_list() for a in args] for shape in shapes: if len(shape) != 2: raise ValueError("Linear is expecting 2D arguments: %s" % str(shapes)) if not shape[1]: raise ValueError("Linear expects shape[1] of arguments: %s" % str(shapes)) else: total_arg_size += shape[1] dtype = [a.dtype for a in args][0] # Now the computation. with vs.variable_scope(scope or "Linear"): matrix = vs.get_variable( "Matrix", [total_arg_size, output_size], dtype=dtype) if len(args) == 1: res = math_ops.matmul(args[0], matrix) else: res = math_ops.matmul(array_ops.concat(1, args), matrix) if not bias: return res bias_term = vs.get_variable( "Bias", [output_size], dtype=dtype, initializer=init_ops.constant_initializer( bias_start, dtype=dtype)) return res + bias_term