seq2seq.ipynb

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        "<a href=\"https://colab.research.google.com/github/hao1zhao/Model/blob/main/seq2seq.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "!pip install git+https://github.com/d2l-ai/d2l-zh@release  # installing d2l\n",
        "!pip install matplotlib_inline\n",
        "!pip install matplotlib==3.0.0"
      ]
    },
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        "\n",
        "\n",
        "$$\\mathbf{c}_{t'} = \\sum_{t=1}^T \\alpha(\\mathbf{s}_{t' - 1}, \\mathbf{h}_t) \\mathbf{h}_t,$$\n",
        "\n",
        "\n"
      ]
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      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from d2l import torch as d2l"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "class AttentionDecoder(d2l.Decoder):\n",
        "    def __init__(self, **kwargs):\n",
        "        super(AttentionDecoder, self).__init__(**kwargs)\n",
        "\n",
        "    @property\n",
        "    def attention_weights(self):\n",
        "        raise NotImplementedError"
      ]
    },
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      "source": [
        "class Seq2SeqAttentionDecoder(AttentionDecoder):\n",
        "    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers,\n",
        "                 dropout=0, **kwargs):\n",
        "        super(Seq2SeqAttentionDecoder, self).__init__(**kwargs)\n",
        "        self.attention = d2l.AdditiveAttention(\n",
        "            num_hiddens, num_hiddens, num_hiddens, dropout)\n",
        "        self.embedding = nn.Embedding(vocab_size, embed_size)\n",
        "        self.rnn = nn.GRU(\n",
        "            embed_size + num_hiddens, num_hiddens, num_layers,\n",
        "            dropout=dropout)\n",
        "        self.dense = nn.Linear(num_hiddens, vocab_size)\n",
        "\n",
        "    def init_state(self, enc_outputs, enc_valid_lens, *args):\n",
        "        # outputs:(batch_size，num_steps，num_hiddens).\n",
        "        # hidden_state:(num_layers，batch_size，num_hiddens)\n",
        "        outputs, hidden_state = enc_outputs\n",
        "        return (outputs.permute(1, 0, 2), hidden_state, enc_valid_lens)\n",
        "\n",
        "    def forward(self, X, state):\n",
        "        # enc_outputs:(batch_size,num_steps,num_hiddens).\n",
        "        # hidden_state:(num_layers,batch_size,\n",
        "        # num_hiddens)\n",
        "        enc_outputs, hidden_state, enc_valid_lens = state\n",
        "        # outputshape(num_steps,batch_size,embed_size)\n",
        "        X = self.embedding(X).permute(1, 0, 2)\n",
        "        outputs, self._attention_weights = [], []\n",
        "        for x in X:\n",
        "            # query(batch_size,1,num_hiddens)\n",
        "            query = torch.unsqueeze(hidden_state[-1], dim=1)\n",
        "            # context(batch_size,1,num_hiddens)\n",
        "            context = self.attention(\n",
        "                query, enc_outputs, enc_outputs, enc_valid_lens)\n",
        "            x = torch.cat((context, torch.unsqueeze(x, dim=1)), dim=-1)\n",
        "            # x(1,batch_size,embed_size+num_hiddens)\n",
        "            out, hidden_state = self.rnn(x.permute(1, 0, 2), hidden_state)\n",
        "            outputs.append(out)\n",
        "            self._attention_weights.append(self.attention.attention_weights)\n",
        "        # outputs:(num_steps,batch_size,vocab_size)\n",
        "        outputs = self.dense(torch.cat(outputs, dim=0))\n",
        "        return outputs.permute(1, 0, 2), [enc_outputs, hidden_state,\n",
        "                                          enc_valid_lens]\n",
        "\n",
        "    @property\n",
        "    def attention_weights(self):\n",
        "        return self._attention_weights"
      ]
    },
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              "(torch.Size([4, 7, 10]), 3, torch.Size([4, 7, 16]), 2, torch.Size([4, 16]))"
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      "source": [
        "encoder = d2l.Seq2SeqEncoder(vocab_size=10, embed_size=8, num_hiddens=16,\n",
        "                             num_layers=2)\n",
        "encoder.eval()\n",
        "decoder = Seq2SeqAttentionDecoder(vocab_size=10, embed_size=8, num_hiddens=16,\n",
        "                                  num_layers=2)\n",
        "decoder.eval()\n",
        "X = torch.zeros((4, 7), dtype=torch.long)  # (batch_size,num_steps)\n",
        "state = decoder.init_state(encoder(X), None)\n",
        "output, state = decoder(X, state)\n",
        "output.shape, len(state), state[0].shape, len(state[1]), state[1][0].shape"
      ]
    },
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          "text": [
            "loss 0.020, 5482.1 tokens/sec on cuda:0\n"
          ]
        },
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      "source": [
        "embed_size, num_hiddens, num_layers, dropout = 32, 32, 2, 0.1\n",
        "batch_size, num_steps = 64, 10\n",
        "lr, num_epochs, device = 0.005, 250, d2l.try_gpu()\n",
        "\n",
        "train_iter, src_vocab, tgt_vocab = d2l.load_data_nmt(batch_size, num_steps)\n",
        "encoder = d2l.Seq2SeqEncoder(\n",
        "    len(src_vocab), embed_size, num_hiddens, num_layers, dropout)\n",
        "decoder = Seq2SeqAttentionDecoder(\n",
        "    len(tgt_vocab), embed_size, num_hiddens, num_layers, dropout)\n",
        "net = d2l.EncoderDecoder(encoder, decoder)\n",
        "d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)"
      ]
    },
    {
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      "execution_count": null,
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "go . => va !,  bleu 1.000\n",
            "i lost . => j'ai perdu .,  bleu 1.000\n",
            "he's calm . => il est riche .,  bleu 0.658\n",
            "i'm home . => je suis chez moi .,  bleu 1.000\n"
          ]
        }
      ],
      "source": [
        "#BLEU score\n",
        "engs = ['go .', \"i lost .\", 'he\\'s calm .', 'i\\'m home .']\n",
        "fras = ['va !', 'j\\'ai perdu .', 'il est calme .', 'je suis chez moi .']\n",
        "for eng, fra in zip(engs, fras):\n",
        "    translation, dec_attention_weight_seq = d2l.predict_seq2seq(\n",
        "        net, eng, src_vocab, tgt_vocab, num_steps, device, True)\n",
        "    print(f'{eng} => {translation}, ',\n",
        "          f'bleu {d2l.bleu(translation, fra, k=2):.3f}')"
      ]
    },
    {
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        "attention_weights = torch.cat([step[0][0][0] for step in dec_attention_weight_seq], 0).reshape((\n",
        "    1, 1, -1, num_steps))"
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      ],
      "source": [
        "d2l.show_heatmaps(\n",
        "    attention_weights[:, :, :, :len(engs[-1].split()) + 1].cpu(),\n",
        "    xlabel='Key positions', ylabel='Query positions')"
      ]
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