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3345 lines
128 KiB
Plaintext
3345 lines
128 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "NXTSugt6ieXh"
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},
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"source": [
|
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"## Training CBoW Model\n",
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"\n",
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"This notebooks is a part of [AI for Beginners Curriculum](http://aka.ms/ai-beginners)\n",
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"\n",
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"In this example, we will look at training CBoW language model to get our own Word2Vec embedding space. We will use AG News dataset as the source of text."
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 30,
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||
"metadata": {
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||
"id": "hvf7izZpieXk"
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},
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"outputs": [],
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"source": [
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"from tensorflow import keras\n",
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"import tensorflow as tf\n",
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"import tensorflow_datasets as tfds\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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"source": [
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"We will start by loading the dateset:"
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]
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||
},
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{
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"cell_type": "code",
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||
"execution_count": 1,
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||
"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 299,
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"referenced_widgets": [
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"id": "pWPCrm2jieXl",
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"outputId": "7ffa325f-d5d2-4044-d318-0a521f4f5c98"
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},
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"outputs": [],
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"source": [
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"ds_train, ds_test = tfds.load('ag_news_subset').values()"
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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"source": [
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"## CBoW Model\n",
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"\n",
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"CBoW learns to predict a word based on the $2N$ neighboring words. For example, when $N=1$, we will get the following pairs from the sentence *I like to train networks*: (like,I), (I, like), (to, like), (like,to), (train,to), (to, train), (networks, train), (train,networks). Here, first word is the neighboring word used as an input, and second word is the one we are predicting.\n",
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"\n",
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||
"To build a network to predict next word, we will need to supply neighboring word as input, and get word number as output. The architecture of CBoW network is the following:\n",
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"\n",
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"* Input word is passed through the embedding layer. This very embedding layer would be our Word2Vec embedding, thus we will define it separately as `embedder` variable. We will use embedding size = 30 in this example, even though you might want to experiment with higher dimensions (real word2vec has 300)\n",
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"* Embedding vector would then be passed to a dense layer that will predict output word. Thus it has the `vocab_size` neurons.\n",
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"\n",
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"Embedding layer in Keras automatically knows how to convert numeric input into one-hot encoding, so that we do not have to one-hot-encode input word separately. We specify `input_length=1` to indicate that we want just one word in the input sequence - normally embedding layer is designed to work with longer sequences.\n",
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"\n",
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"For the output, if we use `sparse_categorical_crossentropy` as loss function, we would also have to provide just word numbers as expected results, without one-hot encoding.\n",
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"\n",
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"We will set `vocab_size` to 5000 to limit computations a bit. We will also define a vectorizer which we will use later. "
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 68,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
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||
},
|
||
"id": "6PHiH8oRieXl",
|
||
"outputId": "0259a0d5-b5f1-4bc9-d632-73c31893fa3f"
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||
},
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||
"outputs": [
|
||
{
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||
"name": "stdout",
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||
"output_type": "stream",
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"text": [
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||
"Model: \"sequential_1\"\n",
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"_________________________________________________________________\n",
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" Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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" embedding_1 (Embedding) (None, 1, 30) 150000 \n",
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" \n",
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" dense_1 (Dense) (None, 1, 5000) 155000 \n",
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" \n",
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"=================================================================\n",
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"Total params: 305,000\n",
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"Trainable params: 305,000\n",
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||
"Non-trainable params: 0\n",
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||
"_________________________________________________________________\n"
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]
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||
}
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||
],
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||
"source": [
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||
"vocab_size = 5000\n",
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"\n",
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"vectorizer = keras.layers.experimental.preprocessing.TextVectorization(max_tokens=vocab_size,input_shape=(1,))\n",
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"embedder = keras.layers.Embedding(vocab_size,30,input_length=1)\n",
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"\n",
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"model = keras.Sequential([\n",
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" embedder,\n",
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" keras.layers.Dense(vocab_size,activation='softmax')\n",
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"])\n",
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"\n",
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||
"model.summary()"
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||
]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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||
"Let's initialize the vectorizer and get out the vocabulary:"
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 69,
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||
"metadata": {
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||
"id": "rWnylDAIieXn"
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||
},
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||
"outputs": [],
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||
"source": [
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||
"def extract_text(x):\n",
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||
" return x['title']+' '+x['description']\n",
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"\n",
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"vectorizer.adapt(ds_train.take(500).map(extract_text))\n",
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"vocab = vectorizer.get_vocabulary()"
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]
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},
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||
{
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"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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||
"## Preparing Training Data\n",
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"\n",
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"Now let's program the main function that will compute CBoW word pairs from text. This function will allow us to specify window size, and will return a set of pairs - input and output word. Note that this function can be used on words, as well as on vectors/tensors - which will allow us to encode the text, before passing it to `to_cbow` function."
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]
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||
},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": 70,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "x-dsXygOieXn",
|
||
"outputId": "11828ef5-5961-4909-f777-ff7b9b93adbd"
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||
},
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||
"outputs": [
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||
{
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||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
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"[['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",
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"[[<tf.Tensor: shape=(), dtype=int64, numpy=376>, <tf.Tensor: shape=(), dtype=int64, numpy=771>], [<tf.Tensor: shape=(), dtype=int64, numpy=3>, <tf.Tensor: shape=(), dtype=int64, numpy=771>], [<tf.Tensor: shape=(), dtype=int64, numpy=771>, <tf.Tensor: shape=(), dtype=int64, numpy=376>], [<tf.Tensor: shape=(), dtype=int64, numpy=3>, <tf.Tensor: shape=(), dtype=int64, numpy=376>], [<tf.Tensor: shape=(), dtype=int64, numpy=1>, <tf.Tensor: shape=(), dtype=int64, numpy=376>], [<tf.Tensor: shape=(), dtype=int64, numpy=771>, <tf.Tensor: shape=(), dtype=int64, numpy=3>], [<tf.Tensor: shape=(), dtype=int64, numpy=376>, <tf.Tensor: shape=(), dtype=int64, numpy=3>], [<tf.Tensor: shape=(), dtype=int64, numpy=1>, <tf.Tensor: shape=(), dtype=int64, numpy=3>], [<tf.Tensor: shape=(), dtype=int64, numpy=1045>, <tf.Tensor: shape=(), dtype=int64, numpy=3>], [<tf.Tensor: shape=(), dtype=int64, numpy=376>, <tf.Tensor: shape=(), dtype=int64, numpy=1>], [<tf.Tensor: shape=(), dtype=int64, numpy=3>, <tf.Tensor: shape=(), dtype=int64, numpy=1>], [<tf.Tensor: shape=(), dtype=int64, numpy=1045>, <tf.Tensor: shape=(), dtype=int64, numpy=1>], [<tf.Tensor: shape=(), dtype=int64, numpy=3>, <tf.Tensor: shape=(), dtype=int64, numpy=1045>], [<tf.Tensor: shape=(), dtype=int64, numpy=1>, <tf.Tensor: shape=(), dtype=int64, numpy=1045>]]\n"
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]
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||
}
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],
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"source": [
|
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"def to_cbow(sent,window_size=2):\n",
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" res = []\n",
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" for i,x in enumerate(sent):\n",
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" for j in range(max(0,i-window_size),min(i+window_size+1,len(sent))):\n",
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" if i!=j:\n",
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" res.append([sent[j],x])\n",
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" return res\n",
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"\n",
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"print(to_cbow(['I','like','to','train','networks']))\n",
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"print(to_cbow(vectorizer('I like to train networks')))"
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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"source": [
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"Let's prepare the training dataset. We will go through all news, call `to_cbow` to get the list of word pairs, and add those pairs to `X` and `Y`. For the sake of time, we will only consider first 10k news items - you can easily remove the limitation in case you have more time to wait, and want to get better embeddings :)"
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]
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},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 100,
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||
"metadata": {
|
||
"id": "54b-Gd9TieXo"
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||
},
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||
"outputs": [],
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||
"source": [
|
||
"X = []\n",
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"Y = []\n",
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"for i,x in zip(range(10000),ds_train.map(extract_text).as_numpy_iterator()):\n",
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" for w1, w2 in to_cbow(vectorizer(x),window_size=1):\n",
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||
" X.append(tf.expand_dims(w1,0))\n",
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" Y.append(tf.expand_dims(w2,0))"
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]
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},
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||
{
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"cell_type": "markdown",
|
||
"metadata": {},
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||
"source": [
|
||
"We will also convert that data to one dataset, and batch it for training:"
|
||
]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 101,
|
||
"metadata": {
|
||
"id": "AbLUcojlieXo"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"ds = tf.data.Dataset.from_tensor_slices((X,Y)).batch(256)"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Now let's do the actual training. We will use `SGD` optimizer with pretty high learning rate. You can also try playing around with other optimizers, such as `Adam`. We will train for 200 epochs to begin with - and you can re-run this cell if you want even lower loss."
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||
]
|
||
},
|
||
{
|
||
"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",
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||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.3513\n",
|
||
"Epoch 17/200\n",
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||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.3459\n",
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"Epoch 18/200\n",
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||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.3408\n",
|
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"Epoch 19/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.3359\n",
|
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"Epoch 20/200\n",
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||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.3312\n",
|
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"Epoch 21/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.3266\n",
|
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"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 [==============================] - 7s 3ms/step - loss: 5.1627\n",
|
||
"Epoch 85/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1609\n",
|
||
"Epoch 86/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1592\n",
|
||
"Epoch 87/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1574\n",
|
||
"Epoch 88/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1557\n",
|
||
"Epoch 89/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1539\n",
|
||
"Epoch 90/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1522\n",
|
||
"Epoch 91/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1505\n",
|
||
"Epoch 92/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1488\n",
|
||
"Epoch 93/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1471\n",
|
||
"Epoch 94/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1454\n",
|
||
"Epoch 95/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1438\n",
|
||
"Epoch 96/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1421\n",
|
||
"Epoch 97/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1405\n",
|
||
"Epoch 98/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1389\n",
|
||
"Epoch 99/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1372\n",
|
||
"Epoch 100/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1356\n",
|
||
"Epoch 101/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1341\n",
|
||
"Epoch 102/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1325\n",
|
||
"Epoch 103/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1309\n",
|
||
"Epoch 104/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1293\n",
|
||
"Epoch 105/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1278\n",
|
||
"Epoch 106/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1263\n",
|
||
"Epoch 107/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1247\n",
|
||
"Epoch 108/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1232\n",
|
||
"Epoch 109/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1217\n",
|
||
"Epoch 110/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1202\n",
|
||
"Epoch 111/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1187\n",
|
||
"Epoch 112/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1173\n",
|
||
"Epoch 113/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1158\n",
|
||
"Epoch 114/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1144\n",
|
||
"Epoch 115/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1129\n",
|
||
"Epoch 116/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1115\n",
|
||
"Epoch 117/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1101\n",
|
||
"Epoch 118/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1086\n",
|
||
"Epoch 119/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1072\n",
|
||
"Epoch 120/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1058\n",
|
||
"Epoch 121/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1045\n",
|
||
"Epoch 122/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1031\n",
|
||
"Epoch 123/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1017\n",
|
||
"Epoch 124/200\n",
|
||
"2156/2156 [==============================] - 7s 3ms/step - loss: 5.1004\n",
|
||
"Epoch 125/200\n",
|
||
"2156/2156 [==============================] - 7s 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": [
|
||
"<keras.callbacks.History at 0x7ff7e52572d0>"
|
||
]
|
||
},
|
||
"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": [
|
||
"## Trying out Word2Vec\n",
|
||
"\n",
|
||
"To use Word2Vec, let's extract vectors corresponding to all words in our vocabulary:"
|
||
]
|
||
},
|
||
{
|
||
"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": [
|
||
"Let's see, for example, how the word **Paris** is encoded into a vector:"
|
||
]
|
||
},
|
||
{
|
||
"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": [
|
||
"It is interesting to use Word2Vec to look for synonyms. The following function will return `n` closest words to a given input. To find them, we compute the norm of $|w_i - v|$, where $v$ is the vector corresponding to our input word, and $w_i$ is the encoding of $i$-th word in the vocabulary. We then sort the array and return corresponding indices using `argsort`, and take first `n` elements of the list, which encode positions of closest words in the vocabulary. "
|
||
]
|
||
},
|
||
{
|
||
"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')"
|
||
]
|
||
},
|
||
{
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"cell_type": "markdown",
|
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"metadata": {
|
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"id": "My0VeTDd3Ji8"
|
||
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|
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"source": [
|
||
"## Takeaway\n",
|
||
"\n",
|
||
"Using clever techniques such as CBoW, we can train Word2Vec model. You may also try to train skip-gram model that is trained to predict the neighboring word given the central one, and see how well it performs. "
|
||
]
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||
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