40 lines
1.6 KiB
Python
40 lines
1.6 KiB
Python
import math
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import paddle.fluid.dygraph as dg
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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class PreNet(dg.Layer):
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def __init__(self, input_size, hidden_size, output_size, dropout_rate=0.2):
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"""
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:param input_size: dimension of input
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:param hidden_size: dimension of hidden unit
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:param output_size: dimension of output
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"""
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super(PreNet, self).__init__()
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self.input_size = input_size
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self.hidden_size = hidden_size
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self.output_size = output_size
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self.dropout_rate = dropout_rate
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k = math.sqrt(1 / input_size)
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self.linear1 = dg.Linear(input_size, hidden_size,
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param_attr=fluid.ParamAttr(initializer = fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(initializer = fluid.initializer.Uniform(low=-k, high=k)))
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k = math.sqrt(1 / hidden_size)
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self.linear2 = dg.Linear(hidden_size, output_size,
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param_attr=fluid.ParamAttr(initializer = fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(initializer = fluid.initializer.Uniform(low=-k, high=k)))
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def forward(self, x):
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"""
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Pre Net before passing through the network.
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Args:
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x (Variable): Shape(B, T, C), dtype: float32. The input value.
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Returns:
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x (Variable), Shape(B, T, C), the result after pernet.
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"""
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x = layers.dropout(layers.relu(self.linear1(x)), self.dropout_rate)
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x = layers.dropout(layers.relu(self.linear2(x)), self.dropout_rate)
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return x
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