Course_3_Week_2_Project_4.py

"""
This is is a part of the DeepLearning.AI TensorFlow Developer Professional Certificate offered on Coursera.

All copyrights belong to them. I am sharing this work here to showcase the projects I have worked on
Course: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Week 3: Word Embeddings

Aim: BBC News Dataset
"""

import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import csv

tf.keras.backend.clear_session()

vocab_size = 1000
embedding_dim = 16
max_length = 120
trunc_type = "post"
padding_type = "post"
oov_tok = "<OOV>"
training_portion = 0.8

sentences = []
labels = []
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"]

print(len(stopwords))

with open("bbc-text.csv", "r") as csvfile:
    csv_reader = csv.reader(csvfile, delimiter=",")
    first_line = True
    temp_images = []
    temp_labels = []
    for row in csv_reader:
        if first_line:
            first_line = False
        else:
            labels.append(row[0])
            sentence = row[1]
            for word in stopwords:
                stp_wrd = " " + word + " "
                sentence = sentence.replace(stp_wrd, " ")
                sentence = sentence.replace("  ", " ")
            sentences.append(sentence)

print(len(labels))
print(len(sentences))
print(sentences[0])

train_size = int(len(sentences)*training_portion)

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

validation_sentences = sentences[train_size:]
validation_labels = labels[train_size:]

print(train_size)
print(len(train_sentences))
print(len(train_labels))
print(len(validation_sentences))
print(len(validation_labels))

tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)
tokenizer.fit_on_texts(train_sentences)
word_index = tokenizer.word_index

train_sequences = tokenizer.texts_to_sequences(train_sentences)
train_padded = pad_sequences(train_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)

print(len(train_sequences[0]))
print(len(train_padded[0]))

print(len(train_sequences[1]))
print(len(train_padded[1]))

print(len(train_sequences[10]))
print(len(train_padded[10]))

validation_sequences = tokenizer.texts_to_sequences(validation_sentences)
validation_padded = pad_sequences(validation_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)

print(len(validation_sequences))
print(validation_padded.shape)

label_tokenizer = Tokenizer()
label_tokenizer.fit_on_texts(labels)

training_label_seq = np.array(label_tokenizer.texts_to_sequences(train_labels))
validation_label_seq = np.array(label_tokenizer.texts_to_sequences(validation_labels))

print(training_label_seq[0])
print(training_label_seq[1])
print(training_label_seq[2])
print(training_label_seq.shape)

print(validation_label_seq[0])
print(validation_label_seq[1])
print(validation_label_seq[2])
print(validation_label_seq.shape)

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(24, activation="relu"),
    tf.keras.layers.Dense(6, activation="softmax")
])

model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['acc'])

model.summary()

num_epochs = 30
history = model.fit(train_padded, training_label_seq, validation_data=(validation_padded, validation_label_seq), epochs=num_epochs)

import matplotlib.pyplot as plt


def plot_graphs(history, string):
    plt.plot(history.history[string])
    plt.plot(history.history['val_' + string])
    plt.xlabel("Epochs")
    plt.ylabel(string)
    plt.legend([string, 'val_' + string])
    plt.show()


plot_graphs(history, "acc")
plot_graphs(history, "loss")

reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])

def decode_sentence(text):
    return ' '.join([reverse_word_index.get(i, '?') for i in text])

e = model.layers[0]
weights = e.get_weights()[0]
print(weights.shape) # shape: (vocab_size, embedding_dim)

# Expected output
# (1000, 16)

import io

out_v = io.open('vecs4.tsv', 'w', encoding='utf-8')
out_m = io.open('meta4.tsv', 'w', encoding='utf-8')
for word_num in range(1, vocab_size):
  word = reverse_word_index[word_num]
  embeddings = weights[word_num]
  out_m.write(word + "\n")
  out_v.write('\t'.join([str(x) for x in embeddings]) + "\n")
out_v.close()
out_m.close()