convolution.py 文件源码

def forward(self, x):
        """
        :param x: (length, dim)
        :return: (hidden_dim, )
        """
        if self.padding_size > 0:
            # (padding_size + length + padding_size, dim)
            x = temporal_padding_2d(x, (self.padding_size, self.padding_size))
        safe_x = temporal_padding_2d(x, (0, self.kernel_size - x.shape[0]))
        # If Kernel Size is greater than sentence length, padding at the end of sentence
        x = ifelse(T.gt(self.kernel_size - x.shape[0], 0),
                   safe_x,
                   x)
        conv_result = self.forward_conv(x)
        pooling_result = get_pooling(conv_result, self.pooling)
        dropout_out = dropout_from_layer(pooling_result, self.dropout)
        return self.act.activate(dropout_out + self.b)