Course_3_Week_2_Project_2.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 2: Word Embeddings

Aim: Sarcasm dataset
"""

import json
import tensorflow as tf

from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

vocab_size = 10000
embedding_dim = 18
max_length = 120
trunc_type = "post"
padding_type = "post"
oov_tok = "<OOV>"
training_size = 23000

with open("sarcasm.json", "r") as f:
    datastore = json.load(f)

sentences = []
labels = []

for item in datastore:
    sentences.append(item["headline"])
    labels.append(item["is_sarcastic"])

training_sentences = sentences[0:training_size]
testing_sentences = sentences[training_size:]

training_labels=labels[0:training_size]
testing_labels=labels[training_size:]

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

training_sequences = tokenizer.texts_to_sequences(training_sentences)
training_padded = pad_sequences(training_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)

testing_sequences = tokenizer.texts_to_sequences(testing_sentences)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)

import numpy as np

training_padded = np.array(training_padded)
testing_padded = np.array(testing_padded)
training_labels = np.array(training_labels)
testing_labels = np.array(testing_labels)

"""
model = tf.keras.Sequential([
    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.Dropout(0.2),
    tf.keras.layers.Dense(1, activation="sigmoid")
])
"""
# Code to use bidirectional LSTM with the Sarcasm dataset!

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),
    tf.keras.layers.Dense(24, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

# Code to use 1D Convolution with the Sarcasm dataset!
"""
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
    tf.keras.layers.Conv1D(128, 5, activation='relu'),
    tf.keras.layers.GlobalMaxPooling1D(),
    tf.keras.layers.Dense(24, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])
"""

model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["acc"])

model.summary()

num_epochs = 30

history = model.fit(training_padded, training_labels, epochs=num_epochs, validation_data=(testing_padded, testing_labels))

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])

print(decode_sentence(training_padded[0]))
print(training_sentences[2])
print(labels[2])

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

import io

out_v = io.open('vecs.tsv', 'w', encoding='utf-8')
out_m = io.open('meta.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()