C3W2: Diving deeper into the BBC News archive

BBC News Classification Dataset

• 2225 examples of new articles with their respective categories (labels).

import tensorflow as tf
from tensorflow.keras import layers, losses
import matplotlib.pyplot as plt
import numpy as np
import csv
import re 
import string

VOCAB_SIZE = 1000
EMBEDDING_DIM = 16
SEQUENCE_LENGTH = 120
OOV_TOKEN = '[UNK]'
TRAINING_SPLIT = .8

stopwords = ["a", "about", "above", "after", "again", "against", "all", "am", "an", "and", "any", "are", "as", "at", "be", "because", "been", "before", "being", "below", "between", "both", "but", "by", "could", "did", "do", "does", "doing", "down", "during", "each", "few", "for", "from", "further", "had", "has", "have", "having", "he", "he'd", "he'll", "he's", "her", "here", "here's", "hers", "herself", "him", "himself", "his", "how", "how's", "i", "i'd", "i'll", "i'm", "i've", "if", "in", "into", "is", "it", "it's", "its", "itself", "let's", "me", "more", "most", "my", "myself", "nor", "of", "on", "once", "only", "or", "other", "ought", "our", "ours", "ourselves", "out", "over", "own", "same", "she", "she'd", "she'll", "she's", "should", "so", "some", "such", "than", "that", "that's", "the", "their", "theirs", "them", "themselves", "then", "there", "there's", "these", "they", "they'd", "they'll", "they're", "they've", "this", "those", "through", "to", "too", "under", "until", "up", "very", "was", "we", "we'd", "we'll", "we're", "we've", "were", "what", "what's", "when", "when's", "where", "where's", "which", "while", "who", "who's", "whom", "why", "why's", "with", "would", "you", "you'd", "you'll", "you're", "you've", "your", "yours", "yourself", "yourselves" ]

def parse_data_from_file(filename):

    sentences = []
    labels = []

    with open(filename, 'r') as csvfile:
        reader = csv.reader(csvfile)
        next(reader)

        for row in reader:
            sentences.append(row[1])
            labels.append(row[0])

    return sentences, labels

dataset_path = '../../../data/bbc-text.csv'
sentences, labels = parse_data_from_file(dataset_path)

def train_val_split(sentences, labels, training_split):

    train_size = int(len(sentences) * training_split)

    train_sentences = sentences[:train_size]
    y_train = labels[:train_size]

    validation_sentences = sentences[train_size:]
    validation_labels = labels[train_size:]
    
    return train_sentences, validation_sentences, y_train, validation_labels

train_sentences, val_sentences, y_train, val_labels = train_val_split(sentences, labels, TRAINING_SPLIT)

print(f"{len(train_sentences)} sentences for training.")
print(f"{len(val_sentences)} sentences for validation.")

1780 sentences for training.
445 sentences for validation.

def custom_standardization(input_data):
    
    text = tf.strings.lower(input_data)
    text = tf.strings.regex_replace(text, '[%s]' % re.escape(string.punctuation), ' ')

    for word in stopwords:
        text = tf.strings.regex_replace(text, rf'\b{word}\b', '')

    return text

classes = {label: i for i, label in enumerate(set((y_train)))}

y_train = [[classes[label]] for label in y_train]
val_labels = [[classes[label]] for label in val_labels]

raw_train_ds = tf.data.Dataset.from_tensor_slices((train_sentences, y_train))
raw_val_ds = tf.data.Dataset.from_tensor_slices((val_sentences, val_labels))

vectorize_layer = layers.TextVectorization(
    standardize=custom_standardization,
    max_tokens=VOCAB_SIZE,
    output_sequence_length=SEQUENCE_LENGTH)

vectorize_layer.adapt(train_sentences)

def vectorize_text(text, label):
    text = tf.expand_dims(text, -1)
    return vectorize_layer(text), label

train_ds = raw_train_ds.map(vectorize_text)
val_ds = raw_val_ds.map(vectorize_text)

train_ds = train_ds.cache().prefetch(tf.data.AUTOTUNE)
val_ds = val_ds.cache().prefetch(tf.data.AUTOTUNE)

def create_model(vocab_size, embedding_dim, sequence_length):

    model = tf.keras.Sequential([
        layers.Embedding(vocab_size, embedding_dim),
        layers.Dropout(0.2),
        layers.GlobalAveragePooling1D(),
        layers.Dropout(0.2),
        layers.Dense(5)])

    model.compile(optimizer='adam',
                  loss=losses.SparseCategoricalCrossentropy(from_logits=True),
                  metrics=['accuracy'])

    return model

class myCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if logs['accuracy'] > 0.95 and logs['val_accuracy'] > 0.9:
            print('\nReached 95% accuracy and 90% validation accuracy so cancelling training!')
            self.model.stop_training = True

callback = myCallback()

model = create_model(VOCAB_SIZE, EMBEDDING_DIM, SEQUENCE_LENGTH)
history = model.fit(train_ds, epochs=30, validation_data=val_ds, callbacks=[callback])

Epoch 1/30
1780/1780 [==============================] - 13s 6ms/step - loss: 1.4364 - accuracy: 0.5146 - val_loss: 1.1552 - val_accuracy: 0.8180
Epoch 2/30
1780/1780 [==============================] - 12s 7ms/step - loss: 0.8404 - accuracy: 0.8826 - val_loss: 0.6182 - val_accuracy: 0.9371
Epoch 3/30
1780/1780 [==============================] - 11s 6ms/step - loss: 0.4509 - accuracy: 0.9494 - val_loss: 0.3773 - val_accuracy: 0.9393
Epoch 4/30
1773/1780 [============================>.] - ETA: 0s - loss: 0.2778 - accuracy: 0.9645
Reached 95% accuracy and 90% validation accuracy so cancelling training!
1780/1780 [==============================] - 11s 6ms/step - loss: 0.2779 - accuracy: 0.9640 - val_loss: 0.2723 - val_accuracy: 0.9461

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training y Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(loss, label='Training loss')
plt.plot(val_loss, label='Validation loss')
plt.legend(loc='upper right')
plt.title('Trainind and Validation Loss')
plt.show()