<ruby id="bdb3f"></ruby>

    <p id="bdb3f"><cite id="bdb3f"></cite></p>

      <p id="bdb3f"><cite id="bdb3f"><th id="bdb3f"></th></cite></p><p id="bdb3f"></p>
        <p id="bdb3f"><cite id="bdb3f"></cite></p>

          <pre id="bdb3f"></pre>
          <pre id="bdb3f"><del id="bdb3f"><thead id="bdb3f"></thead></del></pre>

          <ruby id="bdb3f"><mark id="bdb3f"></mark></ruby><ruby id="bdb3f"></ruby>
          <pre id="bdb3f"><pre id="bdb3f"><mark id="bdb3f"></mark></pre></pre><output id="bdb3f"></output><p id="bdb3f"></p><p id="bdb3f"></p>

          <pre id="bdb3f"><del id="bdb3f"><progress id="bdb3f"></progress></del></pre>

                <ruby id="bdb3f"></ruby>

                合規國際互聯網加速 OSASE為企業客戶提供高速穩定SD-WAN國際加速解決方案。 廣告
                # TensorFlow RNN 單元類 `tf.nn.rnn_cell`模塊包含以下用于在 TensorFlow 中創建不同類型單元的類: | **類** | **描述** | | --- | --- | | BasicRNNCell | 提供 RNN 單元的實現 | | BasicLSTMCell | 提供 LSTM RNN 單元的實現,基于 [http://arxiv.org/abs/1409.2329](http://arxiv.org/abs/1409.2329) | | LSTMCell | 提供 LSTM RNN 單元,基于 [http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf) 和 [https:// research.google.com/pubs/archive/43905.pdf](https://research.google.com/pubs/archive/43905.pdf) | | GRUCell | 提供 GRU RNN 單元,基于 [http://arxiv.org/abs/1406.1078](http://arxiv.org/abs/1406.1078) | | MultiRNNCell | 提供由連續連接的多個簡單單元組成的 RNN 單元 | `tf.contrib.rnn` 模塊提供以下額外的類用于在 TensorFlow 中創建不同類型的單元: | **類** | **描述** | | --- | --- | | LSTMBlockCell | 提供塊 LSTM RNN 單元,基于 [http://arxiv.org/abs/1409.2329](http://arxiv.org/abs/1409.2329) | | LSTMBlockFusedCell | 提供塊融合 LSTM RNN 單元,基于 [http://arxiv.org/abs/1409.2329](http://arxiv.org/abs/1409.2329) | | GLSTMCell | 根據 [https://arxiv.org/abs/1703.10722](https://arxiv.org/abs/1703.10722) 提供組 LSTM 單元 | | GridLSTMCell | 提供網格 LSTM RNN 小區,基于 [http://arxiv.org/abs/1507.01526](http://arxiv.org/abs/1507.01526) | | GRUBlockCell | 提供塊 GRU RNN 單元,基于 [http://arxiv.org/abs/1406.1078](http://arxiv.org/abs/1406.1078) | | BidirectionalGridLSTMCell | 僅在頻率上而不是在時間上提供雙向網格 LSTM | | NASCell | 提供神經架構搜索 RNN 單元,基于 [https://arxiv.org/abs/1611.01578](https://arxiv.org/abs/1611.01578) | | UGRNNCell | 提供更新門 RNN 信元,基于 [https://arxiv.org/abs/1611.09913](https://arxiv.org/abs/1611.09913) |
                  <ruby id="bdb3f"></ruby>

                  <p id="bdb3f"><cite id="bdb3f"></cite></p>

                    <p id="bdb3f"><cite id="bdb3f"><th id="bdb3f"></th></cite></p><p id="bdb3f"></p>
                      <p id="bdb3f"><cite id="bdb3f"></cite></p>

                        <pre id="bdb3f"></pre>
                        <pre id="bdb3f"><del id="bdb3f"><thead id="bdb3f"></thead></del></pre>

                        <ruby id="bdb3f"><mark id="bdb3f"></mark></ruby><ruby id="bdb3f"></ruby>
                        <pre id="bdb3f"><pre id="bdb3f"><mark id="bdb3f"></mark></pre></pre><output id="bdb3f"></output><p id="bdb3f"></p><p id="bdb3f"></p>

                        <pre id="bdb3f"><del id="bdb3f"><progress id="bdb3f"></progress></del></pre>

                              <ruby id="bdb3f"></ruby>

                              哎呀哎呀视频在线观看