pretrained_word_embeddings.ipynb

Kernel: Python 3

Using pre-trained word embeddings

Author: fchollet
Date created: 2020/05/05
Last modified: 2020/05/05
Description: Text classification on the Newsgroup20 dataset using pre-trained GloVe word embeddings.

Setup

import os

# Only the TensorFlow backend supports string inputs.
os.environ["KERAS_BACKEND"] = "tensorflow"

import pathlib
import numpy as np
import tensorflow.data as tf_data
import keras
from keras import layers

Introduction

In this example, we show how to train a text classification model that uses pre-trained word embeddings.

We'll work with the Newsgroup20 dataset, a set of 20,000 message board messages belonging to 20 different topic categories.

For the pre-trained word embeddings, we'll use GloVe embeddings.

Download the Newsgroup20 data

data_path = keras.utils.get_file(
    "news20.tar.gz",
    "http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20.tar.gz",
    untar=True,
)

Let's take a look at the data

data_dir = pathlib.Path(data_path).parent / "20_newsgroup"
dirnames = os.listdir(data_dir)
print("Number of directories:", len(dirnames))
print("Directory names:", dirnames)

fnames = os.listdir(data_dir / "comp.graphics")
print("Number of files in comp.graphics:", len(fnames))
print("Some example filenames:", fnames[:5])

Here's a example of what one file contains:

print(open(data_dir / "comp.graphics" / "38987").read())

As you can see, there are header lines that are leaking the file's category, either explicitly (the first line is literally the category name), or implicitly, e.g. via the Organization filed. Let's get rid of the headers:

samples = []
labels = []
class_names = []
class_index = 0
for dirname in sorted(os.listdir(data_dir)):
    class_names.append(dirname)
    dirpath = data_dir / dirname
    fnames = os.listdir(dirpath)
    print("Processing %s, %d files found" % (dirname, len(fnames)))
    for fname in fnames:
        fpath = dirpath / fname
        f = open(fpath, encoding="latin-1")
        content = f.read()
        lines = content.split("\n")
        lines = lines[10:]
        content = "\n".join(lines)
        samples.append(content)
        labels.append(class_index)
    class_index += 1

print("Classes:", class_names)
print("Number of samples:", len(samples))

There's actually one category that doesn't have the expected number of files, but the difference is small enough that the problem remains a balanced classification problem.

Shuffle and split the data into training & validation sets

# Shuffle the data
seed = 1337
rng = np.random.RandomState(seed)
rng.shuffle(samples)
rng = np.random.RandomState(seed)
rng.shuffle(labels)

# Extract a training & validation split
validation_split = 0.2
num_validation_samples = int(validation_split * len(samples))
train_samples = samples[:-num_validation_samples]
val_samples = samples[-num_validation_samples:]
train_labels = labels[:-num_validation_samples]
val_labels = labels[-num_validation_samples:]

Create a vocabulary index

Let's use the TextVectorization to index the vocabulary found in the dataset. Later, we'll use the same layer instance to vectorize the samples.

Our layer will only consider the top 20,000 words, and will truncate or pad sequences to be actually 200 tokens long.

vectorizer = layers.TextVectorization(max_tokens=20000, output_sequence_length=200)
text_ds = tf_data.Dataset.from_tensor_slices(train_samples).batch(128)
vectorizer.adapt(text_ds)

You can retrieve the computed vocabulary used via vectorizer.get_vocabulary(). Let's print the top 5 words:

vectorizer.get_vocabulary()[:5]

Let's vectorize a test sentence:

output = vectorizer([["the cat sat on the mat"]])
output.numpy()[0, :6]

As you can see, "the" gets represented as "2". Why not 0, given that "the" was the first word in the vocabulary? That's because index 0 is reserved for padding and index 1 is reserved for "out of vocabulary" tokens.

Here's a dict mapping words to their indices:

voc = vectorizer.get_vocabulary()
word_index = dict(zip(voc, range(len(voc))))

As you can see, we obtain the same encoding as above for our test sentence:

test = ["the", "cat", "sat", "on", "the", "mat"]
[word_index[w] for w in test]

Load pre-trained word embeddings

Let's download pre-trained GloVe embeddings (a 822M zip file).

You'll need to run the following commands:

!wget https://downloads.cs.stanford.edu/nlp/data/glove.6B.zip
!unzip -q glove.6B.zip

The archive contains text-encoded vectors of various sizes: 50-dimensional, 100-dimensional, 200-dimensional, 300-dimensional. We'll use the 100D ones.

Let's make a dict mapping words (strings) to their NumPy vector representation:

path_to_glove_file = "glove.6B.100d.txt"

embeddings_index = {}
with open(path_to_glove_file) as f:
    for line in f:
        word, coefs = line.split(maxsplit=1)
        coefs = np.fromstring(coefs, "f", sep=" ")
        embeddings_index[word] = coefs

print("Found %s word vectors." % len(embeddings_index))

Now, let's prepare a corresponding embedding matrix that we can use in a Keras Embedding layer. It's a simple NumPy matrix where entry at index i is the pre-trained vector for the word of index i in our vectorizer's vocabulary.

num_tokens = len(voc) + 2
embedding_dim = 100
hits = 0
misses = 0

# Prepare embedding matrix
embedding_matrix = np.zeros((num_tokens, embedding_dim))
for word, i in word_index.items():
    embedding_vector = embeddings_index.get(word)
    if embedding_vector is not None:
        # Words not found in embedding index will be all-zeros.
        # This includes the representation for "padding" and "OOV"
        embedding_matrix[i] = embedding_vector
        hits += 1
    else:
        misses += 1
print("Converted %d words (%d misses)" % (hits, misses))

Next, we load the pre-trained word embeddings matrix into an Embedding layer.

Note that we set trainable=False so as to keep the embeddings fixed (we don't want to update them during training).

from keras.layers import Embedding

embedding_layer = Embedding(
    num_tokens,
    embedding_dim,
    trainable=False,
)
embedding_layer.build((1,))
embedding_layer.set_weights([embedding_matrix])

Build the model

A simple 1D convnet with global max pooling and a classifier at the end.

int_sequences_input = keras.Input(shape=(None,), dtype="int32")
embedded_sequences = embedding_layer(int_sequences_input)
x = layers.Conv1D(128, 5, activation="relu")(embedded_sequences)
x = layers.MaxPooling1D(5)(x)
x = layers.Conv1D(128, 5, activation="relu")(x)
x = layers.MaxPooling1D(5)(x)
x = layers.Conv1D(128, 5, activation="relu")(x)
x = layers.GlobalMaxPooling1D()(x)
x = layers.Dense(128, activation="relu")(x)
x = layers.Dropout(0.5)(x)
preds = layers.Dense(len(class_names), activation="softmax")(x)
model = keras.Model(int_sequences_input, preds)
model.summary()

Train the model

First, convert our list-of-strings data to NumPy arrays of integer indices. The arrays are right-padded.

x_train = vectorizer(np.array([[s] for s in train_samples])).numpy()
x_val = vectorizer(np.array([[s] for s in val_samples])).numpy()

y_train = np.array(train_labels)
y_val = np.array(val_labels)

We use categorical crossentropy as our loss since we're doing softmax classification. Moreover, we use sparse_categorical_crossentropy since our labels are integers.

model.compile(
    loss="sparse_categorical_crossentropy", optimizer="rmsprop", metrics=["acc"]
)
model.fit(x_train, y_train, batch_size=128, epochs=20, validation_data=(x_val, y_val))

Export an end-to-end model

Now, we may want to export a Model object that takes as input a string of arbitrary length, rather than a sequence of indices. It would make the model much more portable, since you wouldn't have to worry about the input preprocessing pipeline.

Our vectorizer is actually a Keras layer, so it's simple:

string_input = keras.Input(shape=(1,), dtype="string")
x = vectorizer(string_input)
preds = model(x)
end_to_end_model = keras.Model(string_input, preds)

probabilities = end_to_end_model(
    keras.ops.convert_to_tensor(
        [["this message is about computer graphics and 3D modeling"]]
    )
)

print(class_names[np.argmax(probabilities[0])])