{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "NXTSugt6ieXh" }, "source": [ "## 训练 CBoW 模型\n", "\n", "本笔记本是 [AI for Beginners Curriculum](http://aka.ms/ai-beginners) 的一部分\n", "\n", "在这个例子中，我们将学习如何训练 CBoW 语言模型，以获得我们自己的 Word2Vec 嵌入空间。我们将使用 AG News 数据集作为文本来源。\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "id": "hvf7izZpieXk" }, "outputs": [], "source": [ "from tensorflow import keras\n", "import tensorflow as tf\n", "import tensorflow_datasets as tfds\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "我们将从加载数据集开始：\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 299, "referenced_widgets": [ "a6a32befb28542228cde3d444d6411f6", "9f5a040885564d41934f6c458761bf33", "a7651b06cb974b52a35e34e8f96c226c", "ee267b7dcf05457b8e3f545df150f09f", "55cc8f6b3b0c49ddbc4bfe526906ccbf", "9d4d315121e9440c8578a62fbe88e415", "a1f8b53e8a1d4ebd8ed0116219490877", "074db709c0a14cd4acfe13ed54e92cbc", "713f10e7274e4f0484d34759b5505842", "5ac5967b468d4af59cea0693ce9a8217", "7916d209cbe04da2912830b16e5f747c", "1a467f2a5e4b421db16e4fd4329f9bc1", "40ac80ed694940c191002b92e30a9f39", "4d62b43b387b45c89471f9820c041718", "ee572078162448bd89bd2c52fbe39aa7", "2bddf650279242d7a259db4209de3253", "9bba163f10a4461ca232b54d79c9b74d", "f91bb89f9f144f8e97f8f0c97f7d9f55", "bec242f072394c4cabc6f39e43350603", "74a70de4caa94d358606308df816725b", "be1b974e61b44ecd807a77a94f6f7991", "72693d5e2c034fb9ad03a32a8eb2999f", "3832f856d7644da2aedc8dc843732269", "3e31ac504d4242b8be208b68219bf064", "bb16a77431264209935f8e747918e430", "b1c32e56d326473db36bcda7abca7010", "7b38647b718d4cb58c20480e6387d749", "490cee33a6c14ead8209f36ca7dc1351", "5eee10ee14ff47f9bea181870bb973e3", "15d88a4607524d07be1f3b91345243ba", "a585d2e5ac5240679587990dbf53dfd2", "bbce51f4b75a4f999f4b3c170083e724", "25c18271a4594c1fa8c694d50dd356a7", "3edc08cbd6774e7485d42bdd13164ed6", "1cf5cb0c39cc4c5ca66be45895aa1860", "b70cac0930eb4d2da24a9f5b042f4a9e", "b38d4f6271234adfb41e8309a115e95b", "d7ef0d4ec19749c3bee8a0be8ad2d468", "bfb1e75c5bc744f28544515c660a0b9b", "62aa64e5318d445f844b8083ae6c40f4", "7ab6c716b4a04052bf048d0ede312365", "94db6867a26a4c988f549d20b3cb51f3", "30ffd5f13a524b0eabd5d2f20885ce50", "5022afdb2026474081a3f54cb4c81351", "52d3dd9cf2994e6da25a10ea42be4beb", "82ee379245d64fc39d1ed9a2586e20a2", "24283dc34e944591877888871a5e584b", "027dbec8c35d4ec3ab43ed2878a32eb9", "b459e5715d3b44eeb379108510261336", "b58f7dc6368b42d0a387e47bce4ce88e", "2252a0aeca7c4b7784370704181f1628", "dc49356d1ba943ad87b88ee6e451e7fb", "7095c6398b0c433db8c4284620c9e335", "19ece8654d8149ac87538fd162bd1aeb", "e630a16615414ceeba5868d162f55a20", "13ee77a308634d928662b651ad2bb9e7", "260fcdf1d0404d149732d566b6ccbbab", "f3ad889117ba43b783e34a82113b325c", "4dace863d2be4961ae72c729405da6cc", "2bbd772ad6284273b3cf97c6afeda6e0", "ca245734f2f54c4e805e761d23652eca", "bcedd81ebcef4d9ca31eea1ae4ab795d", "532f40fc7a1e4826b4495be24ee0f8ed", "b26304339073463b9f0ba2cce4835d13", "abaa80c91f5642649996c844ceb0fcd1", "8655ea4b7b6c4399adaf6e04613869ea", "fc94257ae5094ce0b04695ad29bdf72b", "30224d6b4c274faf85dbd4d2c1892aa7", "295d430b24444986a46a9382c5d5f80d", "9a4eedfb4c6a466ba6f6f21ce76a64bb", "9e28f7897bf142aebd4d374559320812", "0798ebda763a40bc86235a40dfc1adec", "bcd9ea70684742b6991d4e2c7556efa6", "44e94cb4f240446da537579caa8e6d2f", "8591f95a707d4214a17e9f187df6e1c4", "75dd999664ac40f18168f6e1870a878e", "91d15913f17040da828ece1c3b5fa6c6" ] }, "id": "pWPCrm2jieXl", "outputId": "7ffa325f-d5d2-4044-d318-0a521f4f5c98" }, "outputs": [], "source": [ "ds_train, ds_test = tfds.load('ag_news_subset').values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CBoW 模型\n", "\n", "CBoW 学习通过 $2N$ 个相邻的单词来预测一个单词。例如，当 $N=1$ 时，我们可以从句子 *I like to train networks* 中得到以下配对：(like, I)、(I, like)、(to, like)、(like, to)、(train, to)、(to, train)、(networks, train)、(train, networks)。这里，第一个单词是作为输入的相邻单词，第二个单词是我们要预测的单词。\n", "\n", "为了构建一个预测下一个单词的网络，我们需要将相邻单词作为输入，并输出单词编号。CBoW 网络的架构如下：\n", "\n", "* 输入单词会通过嵌入层（embedding layer）。这个嵌入层将成为我们的 Word2Vec 嵌入，因此我们会将其单独定义为 `embedder` 变量。在这个例子中，我们将使用嵌入维度为 30，尽管你可能想尝试更高的维度（真实的 Word2Vec 通常有 300 维）。\n", "* 嵌入向量随后会传递到一个全连接层（dense layer），用于预测输出单词。因此，这一层会有 `vocab_size` 个神经元。\n", "\n", "Keras 中的嵌入层会自动将数值输入转换为独热编码（one-hot encoding），因此我们不需要单独对输入单词进行独热编码。我们通过指定 `input_length=1` 来表明我们只需要输入序列中的一个单词——通常嵌入层是为处理更长的序列设计的。\n", "\n", "对于输出，如果我们使用 `sparse_categorical_crossentropy` 作为损失函数，我们只需要提供单词编号作为期望结果，而不需要进行独热编码。\n", "\n", "我们将 `vocab_size` 设置为 5000，以减少计算量。同时，我们还会定义一个稍后会用到的向量化工具（vectorizer）。\n" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6PHiH8oRieXl", "outputId": "0259a0d5-b5f1-4bc9-d632-73c31893fa3f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_1\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " embedding_1 (Embedding) (None, 1, 30) 150000 \n", " \n", " dense_1 (Dense) (None, 1, 5000) 155000 \n", " \n", "=================================================================\n", "Total params: 305,000\n", "Trainable params: 305,000\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "vocab_size = 5000\n", "\n", "vectorizer = keras.layers.experimental.preprocessing.TextVectorization(max_tokens=vocab_size,input_shape=(1,))\n", "embedder = keras.layers.Embedding(vocab_size,30,input_length=1)\n", "\n", "model = keras.Sequential([\n", " embedder,\n", " keras.layers.Dense(vocab_size,activation='softmax')\n", "])\n", "\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "让我们初始化向量化器并获取词汇表：\n" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "id": "rWnylDAIieXn" }, "outputs": [], "source": [ "def extract_text(x):\n", " return x['title']+' '+x['description']\n", "\n", "vectorizer.adapt(ds_train.take(500).map(extract_text))\n", "vocab = vectorizer.get_vocabulary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 准备训练数据\n", "\n", "现在让我们编写一个主函数，用于从文本中计算CBoW词对。这个函数将允许我们指定窗口大小，并返回一组词对——输入词和输出词。请注意，这个函数既可以用于单词，也可以用于向量/张量——这将使我们能够在传递给`to_cbow`函数之前对文本进行编码。\n" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x-dsXygOieXn", "outputId": "11828ef5-5961-4909-f777-ff7b9b93adbd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[['like', 'I'], ['to', 'I'], ['I', 'like'], ['to', 'like'], ['train', 'like'], ['I', 'to'], ['like', 'to'], ['train', 'to'], ['networks', 'to'], ['like', 'train'], ['to', 'train'], ['networks', 'train'], ['to', 'networks'], ['train', 'networks']]\n", "[[, ], [, ], [, ], [, ], [, ], [, ], [, ], [, ], [, ], [, ], [, ], [, ], [, ], [, ]]\n" ] } ], "source": [ "def to_cbow(sent,window_size=2):\n", " res = []\n", " for i,x in enumerate(sent):\n", " for j in range(max(0,i-window_size),min(i+window_size+1,len(sent))):\n", " if i!=j:\n", " res.append([sent[j],x])\n", " return res\n", "\n", "print(to_cbow(['I','like','to','train','networks']))\n", "print(to_cbow(vectorizer('I like to train networks')))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "让我们准备训练数据集。我们将遍历所有新闻，调用 `to_cbow` 获取单词对列表，并将这些对添加到 `X` 和 `Y` 中。为了节省时间，我们只考虑前 10k 条新闻——如果你有更多时间等待，并希望获得更好的嵌入，可以轻松去掉这个限制 :)\n" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "id": "54b-Gd9TieXo" }, "outputs": [], "source": [ "X = []\n", "Y = []\n", "for i,x in zip(range(10000),ds_train.map(extract_text).as_numpy_iterator()):\n", " for w1, w2 in to_cbow(vectorizer(x),window_size=1):\n", " X.append(tf.expand_dims(w1,0))\n", " Y.append(tf.expand_dims(w2,0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "我们还将把这些数据转换为一个数据集，并将其分批用于训练：\n" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "id": "AbLUcojlieXo" }, "outputs": [], "source": [ "ds = tf.data.Dataset.from_tensor_slices((X,Y)).batch(256)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "现在让我们进行实际训练。我们将使用`SGD`优化器，并设置较高的学习率。你也可以尝试使用其他优化器，比如`Adam`。我们将从训练200个周期开始——如果你希望获得更低的损失，可以重新运行此单元格。\n" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xAcGAQtVieXp", "outputId": "bbab8c44-de25-49b9-ec3f-07db878a0818" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/200\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n", " super(SGD, self).__init__(name, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2156/2156 [==============================] - 7s 3ms/step - loss: 5.6134\n", "Epoch 2/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.5431\n", "Epoch 3/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.5029\n", "Epoch 4/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.4754\n", "Epoch 5/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.4548\n", "Epoch 6/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.4382\n", "Epoch 7/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.4243\n", "Epoch 8/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.4123\n", "Epoch 9/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.4019\n", "Epoch 10/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3926\n", "Epoch 11/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3843\n", "Epoch 12/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3767\n", "Epoch 13/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3697\n", "Epoch 14/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3632\n", "Epoch 15/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3571\n", "Epoch 16/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3513\n", "Epoch 17/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3459\n", "Epoch 18/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3408\n", "Epoch 19/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3359\n", "Epoch 20/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3312\n", "Epoch 21/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3266\n", "Epoch 22/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3223\n", "Epoch 23/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3181\n", "Epoch 24/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3140\n", "Epoch 25/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3101\n", "Epoch 26/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3062\n", "Epoch 27/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.3025\n", "Epoch 28/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2989\n", "Epoch 29/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2953\n", "Epoch 30/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2919\n", "Epoch 31/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2885\n", "Epoch 32/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2852\n", "Epoch 33/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2819\n", "Epoch 34/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2787\n", "Epoch 35/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2756\n", "Epoch 36/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2725\n", "Epoch 37/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2695\n", "Epoch 38/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2665\n", "Epoch 39/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2636\n", "Epoch 40/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2607\n", "Epoch 41/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2578\n", "Epoch 42/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2550\n", "Epoch 43/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2523\n", "Epoch 44/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2495\n", "Epoch 45/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2468\n", "Epoch 46/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2442\n", "Epoch 47/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2416\n", "Epoch 48/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2390\n", "Epoch 49/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2364\n", "Epoch 50/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2339\n", "Epoch 51/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2314\n", "Epoch 52/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2290\n", "Epoch 53/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2266\n", "Epoch 54/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2242\n", "Epoch 55/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2218\n", "Epoch 56/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2195\n", "Epoch 57/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2172\n", "Epoch 58/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2149\n", "Epoch 59/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2126\n", "Epoch 60/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2104\n", "Epoch 61/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2082\n", "Epoch 62/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2060\n", "Epoch 63/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2038\n", "Epoch 64/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.2017\n", "Epoch 65/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1996\n", "Epoch 66/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1975\n", "Epoch 67/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1954\n", "Epoch 68/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1933\n", "Epoch 69/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1913\n", "Epoch 70/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1893\n", "Epoch 71/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1873\n", "Epoch 72/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1853\n", "Epoch 73/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1833\n", "Epoch 74/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1814\n", "Epoch 75/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1795\n", "Epoch 76/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1775\n", "Epoch 77/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1756\n", "Epoch 78/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1737\n", "Epoch 79/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1719\n", "Epoch 80/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1700\n", "Epoch 81/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1682\n", "Epoch 82/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1663\n", "Epoch 83/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.1645\n", "Epoch 84/200\n", "2156/2156 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3ms/step - loss: 5.0990\n", "Epoch 126/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0977\n", "Epoch 127/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0963\n", "Epoch 128/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0950\n", "Epoch 129/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0937\n", "Epoch 130/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0924\n", "Epoch 131/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0911\n", "Epoch 132/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0898\n", "Epoch 133/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0885\n", "Epoch 134/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0873\n", "Epoch 135/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0860\n", "Epoch 136/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0848\n", "Epoch 137/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0835\n", "Epoch 138/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0823\n", "Epoch 139/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0810\n", "Epoch 140/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0798\n", "Epoch 141/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0786\n", "Epoch 142/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0774\n", "Epoch 143/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0762\n", "Epoch 144/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0750\n", "Epoch 145/200\n", "2156/2156 [==============================] - 8s 4ms/step - loss: 5.0739\n", "Epoch 146/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0727\n", "Epoch 147/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0715\n", "Epoch 148/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0704\n", "Epoch 149/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0692\n", "Epoch 150/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0681\n", "Epoch 151/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0670\n", "Epoch 152/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0658\n", "Epoch 153/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0647\n", "Epoch 154/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0636\n", "Epoch 155/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0625\n", "Epoch 156/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0614\n", "Epoch 157/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0603\n", "Epoch 158/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0593\n", "Epoch 159/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0582\n", "Epoch 160/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0571\n", "Epoch 161/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0561\n", "Epoch 162/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0550\n", "Epoch 163/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0539\n", "Epoch 164/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0529\n", "Epoch 165/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0519\n", "Epoch 166/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0508\n", "Epoch 167/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0498\n", "Epoch 168/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0488\n", "Epoch 169/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0478\n", "Epoch 170/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0468\n", "Epoch 171/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0458\n", "Epoch 172/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0448\n", "Epoch 173/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0438\n", "Epoch 174/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0428\n", "Epoch 175/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0418\n", "Epoch 176/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0409\n", "Epoch 177/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0399\n", "Epoch 178/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0389\n", "Epoch 179/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0380\n", "Epoch 180/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0370\n", "Epoch 181/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0361\n", "Epoch 182/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0351\n", "Epoch 183/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0342\n", "Epoch 184/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0333\n", "Epoch 185/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0323\n", "Epoch 186/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0314\n", "Epoch 187/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0305\n", "Epoch 188/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0296\n", "Epoch 189/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0287\n", "Epoch 190/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0278\n", "Epoch 191/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0269\n", "Epoch 192/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0260\n", "Epoch 193/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0251\n", "Epoch 194/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0242\n", "Epoch 195/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0233\n", "Epoch 196/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0225\n", "Epoch 197/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0216\n", "Epoch 198/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0207\n", "Epoch 199/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0199\n", "Epoch 200/200\n", "2156/2156 [==============================] - 7s 3ms/step - loss: 5.0190\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.compile(optimizer=keras.optimizers.SGD(lr=0.1),loss='sparse_categorical_crossentropy')\n", "model.fit(ds,epochs=200)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 尝试使用 Word2Vec\n", "\n", "为了使用 Word2Vec，让我们提取与词汇表中所有单词对应的向量：\n" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "id": "r8TatcXjkU_t" }, "outputs": [], "source": [ "vectors = embedder(vectorizer(vocab))\n", "vectors = tf.reshape(vectors,(-1,30)) # we need reshape to get rid of extra dimension" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "让我们来看一个例子，单词**Paris**是如何被编码成一个向量的：\n" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bz6tAeLzieXp", "outputId": "c0422bc7-ca08-4f99-bced-e46d8b9b93e3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tf.Tensor(\n", "[-0.13308628 0.50972325 0.00344684 0.185389 -0.03176536 0.22262476\n", " -0.3856765 -0.6854793 0.5185803 -0.7215402 -0.16101503 0.15622072\n", " 0.00653811 -0.14954254 0.03379822 -0.01243829 0.27907634 -0.32538188\n", " 0.21718933 0.31112966 -0.24142407 0.15589055 0.2915561 0.19029242\n", " 0.08425518 -0.0941902 -0.54313695 -0.24854654 0.26196313 0.18027727], shape=(30,), dtype=float32)\n" ] } ], "source": [ "paris_vec = embedder(vectorizer('paris'))[0]\n", "print(paris_vec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "使用Word2Vec查找同义词是很有趣的。以下函数将返回与给定输入最接近的`n`个单词。为了找到它们，我们计算$|w_i - v|$的范数，其中$v$是对应于输入单词的向量，$w_i$是词汇表中第$i$个单词的编码。然后我们对数组进行排序，并使用`argsort`返回相应的索引，取列表的前`n`个元素，这些元素编码了词汇表中最接近单词的位置。\n" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NlZyi-_olFar", "outputId": "4e4543db-4472-4b46-affd-71f39df4d342" }, "outputs": [ { "data": { "text/plain": [ "['paris', 'philippines', 'seoul', 'jakarta', 'zoo']" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def close_words(x,n=5):\n", " vec = embedder(vectorizer(x))[0]\n", " top5 = np.linalg.norm(vectors-vec,axis=1).argsort()[:n]\n", " return [ vocab[x] for x in top5 ]\n", "\n", "close_words('paris')" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-dQq7xeAln0U", "outputId": "3fdf5f9b-554c-4546-d84e-b88a96dc0e01" }, "outputs": [ { "data": { "text/plain": [ "['china', 'russia', 'pakistan', 'israel', 'turkey']" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "close_words('china')" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fJXqK26b29sa", "outputId": "7a51e71f-1a1d-409e-c050-cffebb145095" }, "outputs": [ { "data": { "text/plain": [ "['official', 'military', 'office', 'police', 'sources']" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "close_words('official')" ] }, { "cell_type": "markdown", "metadata": { "id": "My0VeTDd3Ji8" }, "source": [ "## 要点\n", "\n", "通过使用像CBoW这样的巧妙技术，我们可以训练Word2Vec模型。你也可以尝试训练skip-gram模型，该模型通过给定中心词预测邻近词，看看它的表现如何。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n---\n\n**免责声明**： \n本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原文档的原始语言版本作为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而引起的任何误解或误读不承担责任。\n" ] } ], "metadata": { 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