53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
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import paddle.fluid.dygraph as dg
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import paddle.fluid.layers as layers
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class DynamicGRU(dg.Layer):
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def __init__(self,
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size,
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param_attr=None,
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bias_attr=None,
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is_reverse=False,
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gate_activation='sigmoid',
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candidate_activation='tanh',
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h_0=None,
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origin_mode=False,
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init_size=None):
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super(DynamicGRU, self).__init__()
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self.gru_unit = dg.GRUUnit(
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size * 3,
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param_attr=param_attr,
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bias_attr=bias_attr,
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activation=candidate_activation,
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gate_activation=gate_activation,
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origin_mode=origin_mode)
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self.size = size
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self.h_0 = h_0
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self.is_reverse = is_reverse
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def forward(self, inputs):
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"""
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Dynamic GRU block.
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Args:
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input (Variable): Shape(B, T, C), dtype: float32. The input value.
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Returns:
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output (Variable), Shape(B, T, C), the result compute by GRU.
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"""
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hidden = self.h_0
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res = []
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for i in range(inputs.shape[1]):
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if self.is_reverse:
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i = inputs.shape[1] - 1 - i
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input_ = inputs[:, i:i + 1, :]
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input_ = layers.reshape(
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input_, [-1, input_.shape[2]], inplace=False)
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hidden, reset, gate = self.gru_unit(input_, hidden)
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hidden_ = layers.reshape(
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hidden, [-1, 1, hidden.shape[1]], inplace=False)
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res.append(hidden_)
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if self.is_reverse:
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res = res[::-1]
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res = layers.concat(res, axis=1)
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return res
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