Course_3_Week_2_Project_3.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: IMDB Dataset
"""


import tensorflow as tf
import tensorflow_datasets as tfds
imdb, info = tfds.load("imdb_reviews/subwords8k", with_info=True, as_supervised=True)

train_data, test_data = imdb['train'], imdb['test']

tokenizer = info.features["text"].encoder

print(tokenizer.subwords)

sample_string = "Her: I am so horny, I want my man to pin me down and dissolve me into the bliss of extasy"

tokenized_string = tokenizer.encode(sample_string)
print("Tokenized string: {}".format(tokenized_string))

original_string = tokenizer.decode(tokenized_string)
print("Original string: {}".format(original_string))

for ts in tokenized_string:
    print("{} ---> {}".format(ts, tokenizer.decode([ts])))

BUFFER_SIZE = 10000
BATCH_SIZE = 64

train_dataset = train_data.shuffle(BUFFER_SIZE)
train_dataset = train_dataset.padded_batch(BATCH_SIZE, tf.compat.v1.data.get_output_shapes(train_dataset))

test_dataset = test_data.shuffle(BUFFER_SIZE)
test_dataset = test_dataset.padded_batch(BATCH_SIZE, tf.compat.v1.data.get_output_shapes(test_dataset))

embedding_dim = 64
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(tokenizer.vocab_size, embedding_dim),
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(6, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.summary()

num_epochs = 10

model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])

history = model.fit(train_dataset, epochs=num_epochs, validation_data=test_dataset)

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, "accuracy")
plot_graphs(history, "loss")

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, tokenizer.vocab_size):
  word = tokenizer.decode([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()