Source code for mindspore.nn.layer.lstm

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from mindspore.ops import operations as P
from mindspore.nn.cell import Cell
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from mindspore._checkparam import ParamValidator as validator

[docs]class LSTM(Cell): r""" LSTM (Long Short-Term Memory) layer. Applies a LSTM to the input. There are two pipelines connecting two consecutive cells in a LSTM model; one is cell state pipeline and another is hidden state pipeline. Denote two consecutive time nodes as :math:`t-1` and :math:`t`. Given an input :math:`x_t` at time :math:`t`, an hidden state :math:`h_{t-1}` and an cell state :math:`c_{t-1}` of the layer at time :math:`{t-1}`, the cell state and hidden state at time :math:`t` is computed using an gating mechanism. Input gate :math:`i_t` is designed to protect the cell from perturbation by irrelevant inputs. Forget gate :math:`f_t` affords protection of the cell by forgetting some information in the past, which is stored in :math:`h_{t-1}`. Output gate :math:`o_t` protects other units from perturbation by currently irrelevant memory contents. Candidate cell state :math:`\tilde{c}_t` is calculated with the current input, on which the input gate will be applied. Finally, current cell state :math:`c_{t}` and hidden state :math:`h_{t}` are computed with the calculated gates and cell states. The complete formulation is as follows. .. math:: \begin{array}{ll} \\ i_t = \sigma(W_{ix} x_t + b_{ix} + W_{ih} h_{(t-1)} + b_{ih}) \\ f_t = \sigma(W_{fx} x_t + b_{fx} + W_{fh} h_{(t-1)} + b_{fh}) \\ \tilde{c}_t = \tanh(W_{cx} x_t + b_{cx} + W_{ch} h_{(t-1)} + b_{ch}) \\ o_t = \sigma(W_{ox} x_t + b_{ox} + W_{oh} h_{(t-1)} + b_{oh}) \\ c_t = f_t * c_{(t-1)} + i_t * \tilde{c}_t \\ h_t = o_t * \tanh(c_t) \\ \end{array} Here :math:`\sigma` is the sigmoid function, and :math:`*` is the Hadamard product. :math:`W, b` are learnable weights between the output and the input in the formula. For instance, :math:`W_{ix}, b_{ix}` are the weight and bias used to transform from input :math:`x` to :math:`i`. Details can be found in paper `LONG SHORT-TERM MEMORY <>`_ and `Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling <>`_. Args: input_size (int): Number of features of input. hidden_size (int): Number of features of hidden layer. num_layers (int): Number of layers of stacked LSTM . Default: 1. has_bias (bool): Specifies whether has bias `b_ih` and `b_hh`. Default: True. batch_first (bool): Specifies whether the first dimension of input is batch_size. Default: False. dropout (float): If not 0, append `Dropout` layer on the outputs of each LSTM layer except the last layer. Default 0. The range of dropout is [0.0, 1.0]. bidirectional (bool): Specifies whether this is a bidirectional LSTM. If set True, number of directions will be 2 otherwise number of directions is 1. Default: False. Inputs: - **input** (Tensor) - Tensor of shape (seq_len, batch_size, `input_size`). - **hx** (tuple) - A tuple of two Tensors (h_0, c_0) both of data type mindspore.float32 or mindspore.float16 and shape (num_directions * `num_layers`, batch_size, `hidden_size`). Data type of `hx` should be the same of `input`. Outputs: Tuple, a tuple constains (`output`, (`h_n`, `c_n`)). - **output** (Tensor) - Tensor of shape (seq_len, batch_size, num_directions * `hidden_size`). - **hx_n** (tuple) - A tuple of two Tensor (h_n, c_n) both of shape (num_directions * `num_layers`, batch_size, `hidden_size`). Examples: >>> class LstmNet(nn.Cell): >>> def __init__(self, input_size, hidden_size, num_layers, has_bias, batch_first, bidirectional): >>> super(LstmNet, self).__init__() >>> self.lstm = nn.LSTM(input_size=input_size, >>> hidden_size=hidden_size, >>> num_layers=num_layers, >>> has_bias=has_bias, >>> batch_first=batch_first, >>> bidirectional=bidirectional, >>> dropout=0.0) >>> >>> def construct(self, inp, h0, c0): >>> return self.lstm(inp, (h0, c0)) >>> >>> net = LstmNet(10, 12, 2, has_bias=True, batch_first=True, bidirectional=False) >>> input = Tensor(np.ones([3, 5, 10]).astype(np.float32)) >>> h0 = Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32)) >>> c0 = Tensor(np.ones([1 * 2, 3, 12]).astype(np.float32)) >>> output, (hn, cn) = net(input, h0, c0) """ def __init__(self, input_size, hidden_size, num_layers=1, has_bias=True, batch_first=False, dropout=0, bidirectional=False): super(LSTM, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.has_bias = has_bias self.batch_first = validator.check_type("batch_first", batch_first, [bool]) self.dropout = float(dropout) self.bidirectional = bidirectional if self.batch_first: self.transpose1 = P.Transpose() self.transpose2 = P.Transpose() self.lstm = P.LSTM(input_size=self.input_size, hidden_size=self.hidden_size, num_layers=self.num_layers, has_bias=self.has_bias, bidirectional=self.bidirectional, dropout=self.dropout) num_directions = 2 if self.bidirectional else 1 weight_size = 0 gate_size = 4 * self.hidden_size for layer in range(self.num_layers): input_layer_size = self.input_size if layer == 0 else self.hidden_size * num_directions increment_size = gate_size * input_layer_size increment_size += gate_size * self.hidden_size if self.has_bias: increment_size += 2 * gate_size weight_size += increment_size * num_directions self.weight = Parameter(initializer(0.0, [weight_size, 1, 1]), name='weight') self.fill = P.Fill() self.shape = P.Shape() def construct(self, x, hx): if self.batch_first: x = self.transpose1(x, (1, 0, 2)) h0, c0 = hx output, hn, cn, _ = self.lstm(x, h0, c0, self.weight) if self.batch_first: output = self.transpose2(output, (1, 0, 2)) return (output, (hn, cn))