Table Of Contents
Table Of Contents

Source code for mxnet.gluon.contrib.rnn.rnn_cell

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# coding: utf-8
"""Definition of various recurrent neural network cells."""
__all__ = ['VariationalDropoutCell', 'LSTMPCell']

from ...rnn import BidirectionalCell, SequentialRNNCell, ModifierCell, HybridRecurrentCell
from ...rnn.rnn_cell import _format_sequence, _get_begin_state, _mask_sequence_variable_length
from ... import tensor_types

[docs]class VariationalDropoutCell(ModifierCell): """ Applies Variational Dropout on base cell. (https://arxiv.org/pdf/1512.05287.pdf, \ https://www.stat.berkeley.edu/~tsmoon/files/Conference/asru2015.pdf). Variational dropout uses the same dropout mask across time-steps. It can be applied to RNN inputs, outputs, and states. The masks for them are not shared. The dropout mask is initialized when stepping forward for the first time and will remain the same until .reset() is called. Thus, if using the cell and stepping manually without calling .unroll(), the .reset() should be called after each sequence. Parameters ---------- base_cell : RecurrentCell The cell on which to perform variational dropout. drop_inputs : float, default 0. The dropout rate for inputs. Won't apply dropout if it equals 0. drop_states : float, default 0. The dropout rate for state inputs on the first state channel. Won't apply dropout if it equals 0. drop_outputs : float, default 0. The dropout rate for outputs. Won't apply dropout if it equals 0. """
[docs] def __init__(self, base_cell, drop_inputs=0., drop_states=0., drop_outputs=0.): assert not drop_states or not isinstance(base_cell, BidirectionalCell), \ "BidirectionalCell doesn't support variational state dropout. " \ "Please add VariationalDropoutCell to the cells underneath instead." assert not drop_states \ or not isinstance(base_cell, SequentialRNNCell) or not base_cell._bidirectional, \ "Bidirectional SequentialRNNCell doesn't support variational state dropout. " \ "Please add VariationalDropoutCell to the cells underneath instead." super(VariationalDropoutCell, self).__init__(base_cell) self.drop_inputs = drop_inputs self.drop_states = drop_states self.drop_outputs = drop_outputs self.drop_inputs_mask = None self.drop_states_mask = None self.drop_outputs_mask = None
def _alias(self): return 'vardrop' def reset(self): super(VariationalDropoutCell, self).reset() self.drop_inputs_mask = None self.drop_states_mask = None self.drop_outputs_mask = None def _initialize_input_masks(self, F, inputs, states): if self.drop_states and self.drop_states_mask is None: self.drop_states_mask = F.Dropout(F.ones_like(states[0]), p=self.drop_states) if self.drop_inputs and self.drop_inputs_mask is None: self.drop_inputs_mask = F.Dropout(F.ones_like(inputs), p=self.drop_inputs) def _initialize_output_mask(self, F, output): if self.drop_outputs and self.drop_outputs_mask is None: self.drop_outputs_mask = F.Dropout(F.ones_like(output), p=self.drop_outputs) def hybrid_forward(self, F, inputs, states): cell = self.base_cell self._initialize_input_masks(F, inputs, states) if self.drop_states: states = list(states) # state dropout only needs to be applied on h, which is always the first state. states[0] = states[0] * self.drop_states_mask if self.drop_inputs: inputs = inputs * self.drop_inputs_mask next_output, next_states = cell(inputs, states) self._initialize_output_mask(F, next_output) if self.drop_outputs: next_output = next_output * self.drop_outputs_mask return next_output, next_states def __repr__(self): s = '{name}(p_out = {drop_outputs}, p_state = {drop_states})' return s.format(name=self.__class__.__name__, **self.__dict__) def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None, valid_length=None): """Unrolls an RNN cell across time steps. Parameters ---------- length : int Number of steps to unroll. inputs : Symbol, list of Symbol, or None If `inputs` is a single Symbol (usually the output of Embedding symbol), it should have shape (batch_size, length, ...) if `layout` is 'NTC', or (length, batch_size, ...) if `layout` is 'TNC'. If `inputs` is a list of symbols (usually output of previous unroll), they should all have shape (batch_size, ...). begin_state : nested list of Symbol, optional Input states created by `begin_state()` or output state of another cell. Created from `begin_state()` if `None`. layout : str, optional `layout` of input symbol. Only used if inputs is a single Symbol. merge_outputs : bool, optional If `False`, returns outputs as a list of Symbols. If `True`, concatenates output across time steps and returns a single symbol with shape (batch_size, length, ...) if layout is 'NTC', or (length, batch_size, ...) if layout is 'TNC'. If `None`, output whatever is faster. valid_length : Symbol, NDArray or None `valid_length` specifies the length of the sequences in the batch without padding. This option is especially useful for building sequence-to-sequence models where the input and output sequences would potentially be padded. If `valid_length` is None, all sequences are assumed to have the same length. If `valid_length` is a Symbol or NDArray, it should have shape (batch_size,). The ith element will be the length of the ith sequence in the batch. The last valid state will be return and the padded outputs will be masked with 0. Note that `valid_length` must be smaller or equal to `length`. Returns ------- outputs : list of Symbol or Symbol Symbol (if `merge_outputs` is True) or list of Symbols (if `merge_outputs` is False) corresponding to the output from the RNN from this unrolling. states : list of Symbol The new state of this RNN after this unrolling. The type of this symbol is same as the output of `begin_state()`. """ # Dropout on inputs and outputs can be performed on the whole sequence # only when state dropout is not present. if self.drop_states: return super(VariationalDropoutCell, self).unroll(length, inputs, begin_state, layout, merge_outputs, valid_length=valid_length) self.reset() inputs, axis, F, batch_size = _format_sequence(length, inputs, layout, True) states = _get_begin_state(self, F, begin_state, inputs, batch_size) if self.drop_inputs: inputs = F.Dropout(inputs, p=self.drop_inputs, axes=(axis,)) outputs, states = self.base_cell.unroll(length, inputs, states, layout, merge_outputs=True, valid_length=valid_length) if self.drop_outputs: outputs = F.Dropout(outputs, p=self.drop_outputs, axes=(axis,)) merge_outputs = isinstance(outputs, tensor_types) if merge_outputs is None else \ merge_outputs outputs, _, _, _ = _format_sequence(length, outputs, layout, merge_outputs) if valid_length is not None: outputs = _mask_sequence_variable_length(F, outputs, length, valid_length, axis, merge_outputs) return outputs, states
[docs]class LSTMPCell(HybridRecurrentCell): r"""Long-Short Term Memory Projected (LSTMP) network cell. (https://arxiv.org/abs/1402.1128) Each call computes the following function: .. math:: \begin{array}{ll} i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{ri} r_{(t-1)} + b_{ri}) \\ f_t = sigmoid(W_{if} x_t + b_{if} + W_{rf} r_{(t-1)} + b_{rf}) \\ g_t = \tanh(W_{ig} x_t + b_{ig} + W_{rc} r_{(t-1)} + b_{rg}) \\ o_t = sigmoid(W_{io} x_t + b_{io} + W_{ro} r_{(t-1)} + b_{ro}) \\ c_t = f_t * c_{(t-1)} + i_t * g_t \\ h_t = o_t * \tanh(c_t) \\ r_t = W_{hr} h_t \end{array} where :math:`r_t` is the projected recurrent activation at time `t`, :math:`h_t` is the hidden state at time `t`, :math:`c_t` is the cell state at time `t`, :math:`x_t` is the input at time `t`, and :math:`i_t`, :math:`f_t`, :math:`g_t`, :math:`o_t` are the input, forget, cell, and out gates, respectively. Parameters ---------- hidden_size : int Number of units in cell state symbol. projection_size : int Number of units in output symbol. i2h_weight_initializer : str or Initializer Initializer for the input weights matrix, used for the linear transformation of the inputs. h2h_weight_initializer : str or Initializer Initializer for the recurrent weights matrix, used for the linear transformation of the hidden state. h2r_weight_initializer : str or Initializer Initializer for the projection weights matrix, used for the linear transformation of the recurrent state. i2h_bias_initializer : str or Initializer, default 'lstmbias' Initializer for the bias vector. By default, bias for the forget gate is initialized to 1 while all other biases are initialized to zero. h2h_bias_initializer : str or Initializer Initializer for the bias vector. prefix : str, default ``'lstmp_``' Prefix for name of `Block`s (and name of weight if params is `None`). params : Parameter or None Container for weight sharing between cells. Created if `None`. Inputs: - **data**: input tensor with shape `(batch_size, input_size)`. - **states**: a list of two initial recurrent state tensors, with shape `(batch_size, projection_size)` and `(batch_size, hidden_size)` respectively. Outputs: - **out**: output tensor with shape `(batch_size, num_hidden)`. - **next_states**: a list of two output recurrent state tensors. Each has the same shape as `states`. """
[docs] def __init__(self, hidden_size, projection_size, i2h_weight_initializer=None, h2h_weight_initializer=None, h2r_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', input_size=0, prefix=None, params=None): super(LSTMPCell, self).__init__(prefix=prefix, params=params) self._hidden_size = hidden_size self._input_size = input_size self._projection_size = projection_size self.i2h_weight = self.params.get('i2h_weight', shape=(4*hidden_size, input_size), init=i2h_weight_initializer, allow_deferred_init=True) self.h2h_weight = self.params.get('h2h_weight', shape=(4*hidden_size, projection_size), init=h2h_weight_initializer, allow_deferred_init=True) self.h2r_weight = self.params.get('h2r_weight', shape=(projection_size, hidden_size), init=h2r_weight_initializer, allow_deferred_init=True) self.i2h_bias = self.params.get('i2h_bias', shape=(4*hidden_size,), init=i2h_bias_initializer, allow_deferred_init=True) self.h2h_bias = self.params.get('h2h_bias', shape=(4*hidden_size,), init=h2h_bias_initializer, allow_deferred_init=True)
def state_info(self, batch_size=0): return [{'shape': (batch_size, self._projection_size), '__layout__': 'NC'}, {'shape': (batch_size, self._hidden_size), '__layout__': 'NC'}] def _alias(self): return 'lstmp' def __repr__(self): s = '{name}({mapping})' shape = self.i2h_weight.shape proj_shape = self.h2r_weight.shape mapping = '{0} -> {1} -> {2}'.format(shape[1] if shape[1] else None, shape[0], proj_shape[0]) return s.format(name=self.__class__.__name__, mapping=mapping, **self.__dict__) # pylint: disable= arguments-differ def hybrid_forward(self, F, inputs, states, i2h_weight, h2h_weight, h2r_weight, i2h_bias, h2h_bias): prefix = 't%d_'%self._counter i2h = F.FullyConnected(data=inputs, weight=i2h_weight, bias=i2h_bias, num_hidden=self._hidden_size*4, name=prefix+'i2h') h2h = F.FullyConnected(data=states[0], weight=h2h_weight, bias=h2h_bias, num_hidden=self._hidden_size*4, name=prefix+'h2h') gates = i2h + h2h slice_gates = F.SliceChannel(gates, num_outputs=4, name=prefix+'slice') in_gate = F.Activation(slice_gates[0], act_type="sigmoid", name=prefix+'i') forget_gate = F.Activation(slice_gates[1], act_type="sigmoid", name=prefix+'f') in_transform = F.Activation(slice_gates[2], act_type="tanh", name=prefix+'c') out_gate = F.Activation(slice_gates[3], act_type="sigmoid", name=prefix+'o') next_c = F._internal._plus(forget_gate * states[1], in_gate * in_transform, name=prefix+'state') hidden = F._internal._mul(out_gate, F.Activation(next_c, act_type="tanh"), name=prefix+'hidden') next_r = F.FullyConnected(data=hidden, num_hidden=self._projection_size, weight=h2r_weight, no_bias=True, name=prefix+'out') return next_r, [next_r, next_c]
# pylint: enable= arguments-differ