diff --git a/paddle2.0_docs/seq2seq_with_attention/seq2seq_with_attention.ipynb b/paddle2.0_docs/seq2seq_with_attention/seq2seq_with_attention.ipynb index d9d19165485d121763d04ad54ca1870ac37c7a26..ba818cdc1a55dbaead4d3998fc23ac815dcfeb20 100644 --- a/paddle2.0_docs/seq2seq_with_attention/seq2seq_with_attention.ipynb +++ b/paddle2.0_docs/seq2seq_with_attention/seq2seq_with_attention.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -54,26 +54,32 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "--2020-09-04 14:06:10-- https://www.manythings.org/anki/cmn-eng.zip\n", - "Resolving www.manythings.org (www.manythings.org)... 104.24.108.196, 104.24.109.196, 172.67.173.198, ...\n", - "Connecting to www.manythings.org (www.manythings.org)|104.24.108.196|:443... connected.\n", - "HTTP request sent, awaiting response... 416 Requested Range Not Satisfiable\n", + "--2020-09-04 16:13:35-- https://www.manythings.org/anki/cmn-eng.zip\n", + "Resolving www.manythings.org (www.manythings.org)... 104.24.109.196, 172.67.173.198, 2606:4700:3037::6818:6cc4, ...\n", + "Connecting to www.manythings.org (www.manythings.org)|104.24.109.196|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1030722 (1007K) [application/zip]\n", + "Saving to: ‘cmn-eng.zip’\n", "\n", - " The file is already fully retrieved; nothing to do.\n", + "cmn-eng.zip 100%[===================>] 1007K 520KB/s in 1.9s \n", "\n", - "Archive: cmn-eng.zip\n" + "2020-09-04 16:13:38 (520 KB/s) - ‘cmn-eng.zip’ saved [1030722/1030722]\n", + "\n", + "Archive: cmn-eng.zip\n", + " inflating: cmn.txt \n", + " inflating: _about.txt \n" ] } ], "source": [ - "!wget -c https://www.manythings.org/anki/cmn-eng.zip && unzip -f cmn-eng.zip" + "!wget -c https://www.manythings.org/anki/cmn-eng.zip && unzip cmn-eng.zip" ] }, { @@ -108,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -139,7 +145,6 @@ } ], "source": [ - "\n", "lines = open('cmn.txt', encoding='utf-8').read().strip().split('\\n')\n", "words_re = re.compile(r'\\w+')\n", "\n", @@ -155,8 +160,7 @@ " if len(x[0]) < MAX_LEN and len(x[1]) < MAX_LEN and \\\n", " x[0][0] in ('i', 'you', 'he', 'she', 'we', 'they'):\n", " filtered_pairs.append(x)\n", - "\n", - " \n", + " \n", "print(len(filtered_pairs))\n", "for x in filtered_pairs[:10]: print(x) " ] @@ -177,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -193,14 +197,11 @@ "en_vocab = {}\n", "cn_vocab = {}\n", "\n", - "# create special token for unkown, begin of sentence, end of sentence\n", + "# create special token for pad, begin of sentence, end of sentence\n", "en_vocab[''], en_vocab[''], en_vocab[''] = 0, 1, 2\n", "cn_vocab[''], cn_vocab[''], cn_vocab[''] = 0, 1, 2\n", "\n", - "#print(en_vocab, cn_vocab)\n", - "\n", "en_idx, cn_idx = 3, 3\n", - "\n", "for en, cn in filtered_pairs:\n", " for w in en: \n", " if w not in en_vocab: \n", @@ -229,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -243,7 +244,6 @@ } ], "source": [ - "# create padded datasets\n", "padded_en_sents = []\n", "padded_cn_sents = []\n", "padded_cn_label_sents = []\n", @@ -262,7 +262,6 @@ "train_cn_sents = np.array(padded_cn_sents)\n", "train_cn_label_sents = np.array(padded_cn_label_sents)\n", "\n", - "\n", "print(train_en_sents.shape)\n", "print(train_cn_sents.shape)\n", "print(train_cn_label_sents.shape)" @@ -280,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -289,7 +288,7 @@ "num_encoder_lstm_layers = 1\n", "en_vocab_size = len(list(en_vocab))\n", "cn_vocab_size = len(list(cn_vocab))\n", - "epochs = 30\n", + "epochs = 20\n", "batch_size = 16" ] }, @@ -301,12 +300,12 @@ "\n", "在编码器的部分，我们通过查找完Embedding之后接一个LSTM的方式构建一个对源语言编码的网络。飞桨的RNN系列的API，除了LSTM之外，还提供了SimleRNN, GRU供使用，同时，还可以使用反向RNN，双向RNN，多层RNN等形式。也可以通过`dropout`参数设置是否对多层RNN的中间层进行`dropout`处理，来防止过拟合。\n", "\n", - "除了使用序列到序列的RNN操作之外，也可以通过SimpleRNN, GRUCell, LSTMCell等API更灵活的创建单步的RNN计算，甚至通过集成RNNCellBase来实现自己的RNN计算单元。" + "除了使用序列到序列的RNN操作之外，也可以通过SimpleRNN, GRUCell, LSTMCell等API更灵活的创建单步的RNN计算，甚至通过继承RNNCellBase来实现自己的RNN计算单元。" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -340,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -350,11 +349,9 @@ " def __init__(self):\n", " super(AttentionDecoder, self).__init__()\n", " self.emb = paddle.nn.Embedding(cn_vocab_size, embedding_size)\n", - " \n", - " # the lstm layer for to generate target sentence representation\n", " self.lstm = paddle.nn.LSTM(input_size=embedding_size + hidden_size, \n", " hidden_size=hidden_size)\n", - " \n", + "\n", " # for computing attention weights\n", " self.attention_linear1 = paddle.nn.Linear(hidden_size * 2, hidden_size)\n", " self.attention_linear2 = paddle.nn.Linear(hidden_size, 1)\n", @@ -362,7 +359,6 @@ " # for computing output logits\n", " self.outlinear =paddle.nn.Linear(hidden_size, cn_vocab_size)\n", "\n", - "\n", " def forward(self, x, previous_hidden, previous_cell, encoder_outputs):\n", " x = self.emb(x)\n", " \n", @@ -376,7 +372,6 @@ " attention_logits = self.attention_linear2(attention_hidden)\n", " attention_logits = paddle.squeeze(attention_logits)\n", "\n", - " \n", " attention_weights = F.softmax(attention_logits) \n", " attention_weights = paddle.expand_as(paddle.unsqueeze(attention_weights, -1), \n", " encoder_outputs)\n", @@ -418,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -426,95 +421,65 @@ "output_type": "stream", "text": [ "epoch:0\n", - "iter 0, loss:[7.618719]\n", - "iter 200, loss:[2.9712436]\n", + "iter 0, loss:[7.6194725]\n", + "iter 200, loss:[3.4147663]\n", "epoch:1\n", - "iter 0, loss:[2.926154]\n", - "iter 200, loss:[2.8847036]\n", + "iter 0, loss:[3.0931656]\n", + "iter 200, loss:[2.7543137]\n", "epoch:2\n", - "iter 0, loss:[2.9981458]\n", - "iter 200, loss:[3.099761]\n", + "iter 0, loss:[2.8413522]\n", + "iter 200, loss:[2.340513]\n", "epoch:3\n", - "iter 0, loss:[2.6152773]\n", - "iter 200, loss:[2.5736806]\n", + "iter 0, loss:[2.597812]\n", + "iter 200, loss:[2.5552855]\n", "epoch:4\n", - "iter 0, loss:[2.418916]\n", - "iter 200, loss:[2.0204105]\n", + "iter 0, loss:[2.0783448]\n", + "iter 200, loss:[2.4544785]\n", "epoch:5\n", - "iter 0, loss:[2.0660372]\n", - "iter 200, loss:[1.997014]\n", + "iter 0, loss:[1.8709135]\n", + "iter 200, loss:[1.8736631]\n", "epoch:6\n", - "iter 0, loss:[1.7394348]\n", - "iter 200, loss:[1.9713217]\n", + "iter 0, loss:[1.9589291]\n", + "iter 200, loss:[2.119414]\n", "epoch:7\n", - "iter 0, loss:[2.2450879]\n", - "iter 200, loss:[1.8005365]\n", + "iter 0, loss:[1.5829577]\n", + "iter 200, loss:[1.6002902]\n", "epoch:8\n", - "iter 0, loss:[1.7562586]\n", - "iter 200, loss:[1.8237668]\n", + "iter 0, loss:[1.6022769]\n", + "iter 200, loss:[1.52694]\n", "epoch:9\n", - "iter 0, loss:[1.3632518]\n", - "iter 200, loss:[1.6413273]\n", + "iter 0, loss:[1.3616685]\n", + "iter 200, loss:[1.5420443]\n", "epoch:10\n", - "iter 0, loss:[1.0960134]\n", - "iter 200, loss:[1.4547268]\n", + "iter 0, loss:[1.0397792]\n", + "iter 200, loss:[1.2458231]\n", "epoch:11\n", - "iter 0, loss:[1.4081496]\n", - "iter 200, loss:[1.4078153]\n", + "iter 0, loss:[1.2107158]\n", + "iter 200, loss:[1.426417]\n", "epoch:12\n", - "iter 0, loss:[1.1659987]\n", - "iter 200, loss:[1.1858114]\n", + "iter 0, loss:[1.1840894]\n", + "iter 200, loss:[1.0999664]\n", "epoch:13\n", - "iter 0, loss:[1.3759178]\n", - "iter 200, loss:[1.2046292]\n", + "iter 0, loss:[1.0968472]\n", + "iter 200, loss:[0.8149167]\n", "epoch:14\n", - "iter 0, loss:[0.8987882]\n", - "iter 200, loss:[1.1897587]\n", + "iter 0, loss:[0.95585203]\n", + "iter 200, loss:[1.0070628]\n", "epoch:15\n", - "iter 0, loss:[0.83738756]\n", - "iter 200, loss:[0.78109366]\n", + "iter 0, loss:[0.89463925]\n", + "iter 200, loss:[0.8288595]\n", "epoch:16\n", - "iter 0, loss:[0.84268856]\n", - "iter 200, loss:[0.9557387]\n", + "iter 0, loss:[0.5672495]\n", + "iter 200, loss:[0.7317069]\n", "epoch:17\n", - "iter 0, loss:[0.643647]\n", - "iter 200, loss:[0.9286504]\n", + "iter 0, loss:[0.76785177]\n", + "iter 200, loss:[0.5319323]\n", "epoch:18\n", - "iter 0, loss:[0.5729206]\n", - "iter 200, loss:[0.6324647]\n", + "iter 0, loss:[0.5250005]\n", + "iter 200, loss:[0.4182841]\n", "epoch:19\n", - "iter 0, loss:[0.6614718]\n", - "iter 200, loss:[0.5292754]\n", - "epoch:20\n", - "iter 0, loss:[0.45713213]\n", - "iter 200, loss:[0.6192503]\n", - "epoch:21\n", - "iter 0, loss:[0.36670336]\n", - "iter 200, loss:[0.41927388]\n", - "epoch:22\n", - "iter 0, loss:[0.3294798]\n", - "iter 200, loss:[0.4599006]\n", - "epoch:23\n", - "iter 0, loss:[0.29158494]\n", - "iter 200, loss:[0.27783182]\n", - "epoch:24\n", - "iter 0, loss:[0.24686475]\n", - "iter 200, loss:[0.34916434]\n", - "epoch:25\n", - "iter 0, loss:[0.26881775]\n", - "iter 200, loss:[0.2400788]\n", - "epoch:26\n", - "iter 0, loss:[0.20649]\n", - "iter 200, loss:[0.212987]\n", - "epoch:27\n", - "iter 0, loss:[0.12560298]\n", - "iter 200, loss:[0.17958683]\n", - "epoch:28\n", - "iter 0, loss:[0.13129365]\n", - "iter 200, loss:[0.14788578]\n", - "epoch:29\n", - "iter 0, loss:[0.07885154]\n", - "iter 200, loss:[0.14729765]\n" + "iter 0, loss:[0.52320284]\n", + "iter 200, loss:[0.47618982]\n" ] } ], @@ -542,7 +507,7 @@ " x_cn_data = train_cn_sents_shuffled[(batch_size*iteration):(batch_size*(iteration+1))]\n", " x_cn_label_data = train_cn_label_sents_shuffled[(batch_size*iteration):(batch_size*(iteration+1))]\n", "\n", - " # shape: (batch, num_layer(=1 here) * num_of_direction(=1 here) * hidden_size)\n", + " # shape: (batch, num_layer(=1 here) * num_of_direction(=1 here), hidden_size)\n", " hidden = paddle.zeros([batch_size, 1, hidden_size])\n", " cell = paddle.zeros([batch_size, 1, hidden_size])\n", "\n", @@ -573,48 +538,49 @@ "source": [ "# 使用模型进行机器翻译\n", "\n", + "根据你所使用的计算设备的不同，上面的训练过程可能需要不等的时间。（在一台Mac笔记本上，大约耗时15~20分钟）\n", "完成上面的模型训练之后，我们可以得到一个能够从英文翻译成中文的机器翻译模型。接下来我们通过一个greedy search来实现使用该模型完成实际的机器翻译。（实际的任务中，你可能需要用beam search算法来提升效果）" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "he is poor\n", - "true: 他很穷。\n", - "pred: 他很穷。\n", - "i lent him a cd\n", - "true: 我借给他一盘CD。\n", - "pred: 我借给他一盘CD。\n", - "i m not so brave\n", - "true: 我没那么勇敢。\n", - "pred: 我没那么勇敢。\n", - "he goes to bed at eight o clock\n", - "true: 他八點上床睡覺。\n", - "pred: 他八點鐘也會遲到。\n", - "i know how old you are\n", - "true: 我知道你多大了。\n", - "pred: 我知道你多大了。\n", - "i m a detective\n", - "true: 我是个侦探。\n", - "pred: 我是个侦探。\n", - "i am the fastest runner\n", - "true: 我是跑得最快的人。\n", - "pred: 我是最快的跑者。\n", - "he got down the book from the shelf\n", - "true: 他從架上拿下書。\n", - "pred: 他從架上拿下書。\n", - "he arrived at the station at seven\n", - "true: 他7点到了火车站。\n", - "pred: 他7点到了火车站。\n", - "he fell down on the floor\n", - "true: 他摔倒在地。\n", - "pred: 他摔倒在地。\n" + "i agree with him\n", + "true: 我同意他。\n", + "pred: 我同意他。\n", + "i think i ll take a bath tonight\n", + "true: 我想我今晚會洗澡。\n", + "pred: 我想我今晚會洗澡。\n", + "he asked for a drink of water\n", + "true: 他要了水喝。\n", + "pred: 他喝了一杯水。\n", + "i began running\n", + "true: 我開始跑。\n", + "pred: 我開始跑。\n", + "i m sick\n", + "true: 我生病了。\n", + "pred: 我生病了。\n", + "you had better go to the dentist s\n", + "true: 你最好去看牙醫。\n", + "pred: 你最好去看牙醫。\n", + "we went for a walk in the forest\n", + "true: 我们去了林中散步。\n", + "pred: 我們去公园散步。\n", + "you ve arrived very early\n", + "true: 你來得很早。\n", + "pred: 你去早个。\n", + "he pretended not to be listening\n", + "true: 他裝作沒在聽。\n", + "pred: 他假装聽到它。\n", + "he always wanted to study japanese\n", + "true: 他一直想學日語。\n", + "pred: 他一直想學日語。\n" ] } ], @@ -640,7 +606,6 @@ "decoded_sent = []\n", "for i in range(MAX_LEN + 2):\n", " logits, (hidden, cell) = atten_decoder(word, hidden, cell, en_repr)\n", - "\n", " word = paddle.argmax(logits, axis=1)\n", " decoded_sent.append(word.numpy())\n", " word = paddle.unsqueeze(word, axis=-1)\n",