Coursera Tensorflow Developer Professional Certificate - nlp in tensorflow week02 (Word Embeddings)

February 8, 2021 9 minute read

Tags: coursera-tensorflow-developer-professional-certificate, nlp, tensorflow, word-embeddings

Embedding Projector

Embedding Projector

IMBD dataset

TensorFlow Data Services TFDS

Imgur

import tensorflow as tf
print(tf.__version__)

如果用 tf 1.x
- 請先打 tf.enable_eager_execution()

import tensorflow_datasets as tfds
imdb, info = tfds.load("imbd_reviews", with_info = True, as_supervised=True)

用到這邊剛好我的 tf 是 1.x 版我乾脆直接升版本到 2.x
- pip install --upgrade tensorflow

import numpy as np

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

training_sentences = []
training_labels = []

testing_sentences = []
testing_labels = []

for s, l in train_data:
    training_sentences.append(str(s.numpy()))
    training_labels.append(l.numpy())

for s, l in test_data:
    testing_sentences.append(str(s.numpy()))
    testing_labels.append(l.numpy())

"""
train_data 
<PrefetchDataset shapes: ((), ()), types: (tf.string, tf.int64)>
"""

tf.Tensor

Labels

training_labels_final = np.array(training_labels)
testing_labels_final = np.array(testing_labels)

Tokenizer

vocab_size = 10000
embedding_dim = 16
max_length = 120
trunc_type = 'post'
oov_tok = '<OOV>'

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

# tokenize init 設定要抓少個字 和 未出現字元(out of vocabulary)的 default 數值
tokenizer = Tokenizer(num_words= vocab_size, oov_token= oov_tok)
# 吃資料的句子
tokenizer.fit_on_texts(training_sentences)
# 把每個字標上 index
word_index = tokenizer.word_index
# 將句子轉成 index 的樣子
sequences = tokenizer.texts_to_sequences(training_sentences)
# 把所有 sequences 長度用成一樣 
padded = pad_sequences(sequences, maxlen=max_length, truncating=trunc_type)

testing_sequences = tokenizer.texts_to_sequences(testing_sentences)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length)

Model

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length = max_length),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(6, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

Vectors
- tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length = max_length),
- 舉例: 電影評論 好/壞 兩種可能把句子打成向量的形式然後給予 label(好/壞) 這樣玩就簡單許多

GlobalAveragePolling1D

比較快一點

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

Compile

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

Training

num_epochs = 3
model.fit(padded,
          training_labels_final,
          epochs=num_epochs,
          validation_data=(testing_padded, testing_labels_final))

Words Embedding Details

e = model.layers[0]
weights = e.get_weights()[0]
print(weights.shape) # shape: (vocab_size, embedding_dim)
# (10000, 16)

Imgur

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

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

print(decode_review(padded[0]))
print(training_sentences[0])

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()

到 projector tensorflow
- 把剛剛寫出來的 vecs.tsv, meta.tsv 丟上去句子打向量後的樣子感受一下

NoteBook (IMBD datasets)

幹! 真的超級好玩的兒!!!

Sarcasem Dataset

import json
import tensorflow as tf

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


voca_size = 10000
embedding_dim = 16
max_length = 32
trunc_type = 'post'
padding_type = 'post'
oov_tok = "<OOV>"
training_size = 2000

download link

!wget --no-check-certificate \
 http://storage.googleapis.com/laurencemoroney-blog.appspot.com/sarcasm.json \
 -O /tmp/sarcasm.json

with open("/tmp/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 = sentecnes[training_size:]
training_labels = labels[0:training_size]
testing_labels = labels[training_size:]

tokenizer = Tokenzer(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.text_to_sequences(teting_sentences)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length,
                               padding=padding_type, truncating=trunc_type)

mdoel = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
    tf.keras.layers.GlobalAveragePolling1D(),
    tf.keras.layers,Dense(24, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

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

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num_epochs = 30

history = model.fit(training_padded, training_labels, epochs=num_epochs, 
                    validation_data=(testing_padded, testing_labels), verbose=2)

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')

Imgur

調超參數 loss function

參數	loss 圖表

NoteBook (Sarcasem Dataset)

tensorflow datasets

imbd_reviews

imbd_reviews/subwords32k

imdb_reviews/subwords8k

Config description: Uses tfds.deprecated.text.SubwordTextEncoder with 8k vocab size
Features:

FeaturesDict({
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
    'text': Text(shape=(None,), dtype=tf.int64, encoder=<SubwordTextEncoder vocab_size=8185>),
})

Examples (tfds.as_dataframe):

import tensorflow_datasets as tfds
imdb, info = tfds.load("imbd_reviews/subwords8k", with_into=True, as_supervised=True)

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

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

tensorflow.org/datasets/api_docs/python/tfds/features/text/SubwordTextEncoder
tensorflow.org/datasets/api_docs/python/tfds/deprecated/text/SubwordTextEncoder

Imgur

sample_string = 'TensorFlow, from basics to mastery'

tokenized_string = tokenizer.encode(sample_string)
print('Tokenized string is {}'.format(tokenized_string))

original_string = tokenizer.decode(tokenized_string)
print('The original string: {}'.format(original_string))

Imgur

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

Imgur

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()

Imgur

num_epochs = 10

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

history = model.fit(train_data,
                    epochs=num_epochs,
                    validation_data=test_data)

import matploylib.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")

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ＮoteBook (mdb_reviews/subwords8k)

Quiz 2 (錯一題)

What is the name of the TensorFlow library containing common data that you can use to train and test neural networks?
- TensorFlow Datasets
How many reviews are there in the IMDB dataset and how are they split?
- 50,000 records, 50/50 train/test split
How are the labels for the IMDB dataset encoded?
- Reviews encoded as a number 0-1
What is the purpose of the embedding dimension?
- It is the number of dimensions for the vector representing the word encoding
When tokenizing a corpus, what does the num_words=n parameter do?
- It specifies the maximum number of words to be tokenized, and picks the most common ‘n’ words
To use word embeddings in TensorFlow, in a sequential layer, what is the name of the class?
- tf.keras.layers.Embedding
IMDB Reviews are either positive or negative. What type of loss function should be used in this scenario?
- Binary crossentropy
When using IMDB Sub Words dataset, our results in classification were poor. Why?
- Sequence becomes much more important when dealing with subwords, but we’re ignoring word positions

Week2 Exercise - BBC news archive

Curl
- windows
  - !curl https://storage.googleapis.com/laurencemoroney-blog.appspot.com/bbc-text.csv --output .\bbc-text.csv

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

!wget --no-check-certificate \
    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/bbc-text.csv \
    -O /tmp/bbc-text.csv

vocab_size = 1000 # YOUR CODE HERE
embedding_dim = 16 # YOUR CODE HERE
max_length = 120 # YOUR CODE HERE
trunc_type = 'post' # YOUR CODE HERE
padding_type = 'post' # YOUR CODE HERE
oov_tok = "<OOV>" # YOUR CODE HERE
training_portion = .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))
# Expected Output
# 153

with open("/tmp/bbc-text.csv", 'r') as csvfile:
    # YOUR CODE HERE
    csv_data = csv.reader(csvfile, delimiter=",")
    for idx, row in enumerate(csv_data):
      if idx!= 0:
        labels.append(row[0])
        sentence = row[1]
        for word in stopwords:
          token = " " + word + " "
          sentence = sentence.replace(token, " ")
          sentence = sentence.replace("  ", " ")
        sentences.append(sentence)
        

    
print(len(labels))
print(len(sentences))
print(sentences[0])
# Expected Output
# 2225
# 2225
# tv future hands viewers home theatre systems  plasma high-definition tvs  digital video recorders moving living room  way people watch tv will radically different five years  time.  according expert panel gathered annual consumer electronics show las vegas discuss new technologies will impact one favourite pastimes. us leading trend  programmes content will delivered viewers via home networks  cable  satellite  telecoms companies  broadband service providers front rooms portable devices.  one talked-about technologies ces digital personal video recorders (dvr pvr). set-top boxes  like us s tivo uk s sky+ system  allow people record  store  play  pause forward wind tv programmes want.  essentially  technology allows much personalised tv. also built-in high-definition tv sets  big business japan us  slower take off europe lack high-definition programming. not can people forward wind adverts  can also forget abiding network channel schedules  putting together a-la-carte entertainment. us networks cable satellite companies worried means terms advertising revenues well  brand identity  viewer loyalty channels. although us leads technology moment  also concern raised europe  particularly growing uptake services like sky+.  happens today  will see nine months years  time uk   adam hume  bbc broadcast s futurologist told bbc news website. likes bbc  no issues lost advertising revenue yet. pressing issue moment commercial uk broadcasters  brand loyalty important everyone.  will talking content brands rather network brands   said tim hanlon  brand communications firm starcom mediavest.  reality broadband connections  anybody can producer content.  added:  challenge now hard promote programme much choice.   means  said stacey jolna  senior vice president tv guide tv group  way people find content want watch simplified tv viewers. means networks  us terms  channels take leaf google s book search engine future  instead scheduler help people find want watch. kind channel model might work younger ipod generation used taking control gadgets play them. might not suit everyone  panel recognised. older generations comfortable familiar schedules channel brands know getting. perhaps not want much choice put hands  mr hanlon suggested.  end  kids just diapers pushing buttons already - everything possible available   said mr hanlon.  ultimately  consumer will tell market want.   50 000 new gadgets technologies showcased ces  many enhancing tv-watching experience. high-definition tv sets everywhere many new models lcd (liquid crystal display) tvs launched dvr capability built  instead external boxes. one example launched show humax s 26-inch lcd tv 80-hour tivo dvr dvd recorder. one us s biggest satellite tv companies  directtv  even launched branded dvr show 100-hours recording capability  instant replay  search function. set can pause rewind tv 90 hours. microsoft chief bill gates announced pre-show keynote speech partnership tivo  called tivotogo  means people can play recorded programmes windows pcs mobile devices. reflect increasing trend freeing multimedia people can watch want  want.

train_size = int(len(sentences) * training_portion) # 1780 # YOUR CODE HERE

train_sentences = sentences[0: train_size] # YOUR CODE HERE
train_labels = labels[0: train_size] # YOUR CODE HERE

validation_sentences = sentences[train_size:] # YOUR CODE HERE
validation_labels = labels[train_size:] # YOUR CODE HERE

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

# Expected output (if training_portion=.8)
# 1780
# 1780
# 1780
# 445
# 445

tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)# YOUR CODE HERE
tokenizer.fit_on_texts(train_sentences)# YOUR CODE HERE
word_index = tokenizer.word_index # YOUR CODE HERE

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

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]))

# Expected Ouput
# 449
# 120
# 200
# 120
# 192
# 120

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

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

# Expected output
# 445
# (445, 120)

label_tokenizer = Tokenizer() # YOUR CODE HERE
label_tokenizer.fit_on_texts(labels)

training_label_seq = np.array(label_tokenizer.texts_to_sequences(train_labels))  # YOUR CODE HERE
validation_label_seq = np.array(label_tokenizer.texts_to_sequences(validation_labels)) # YOUR CODE HERE

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)

# Expected output
# [4]
# [2]
# [1]
# (1780, 1)
# [5]
# [4]
# [3]
# (445, 1)

model = tf.keras.Sequential([
# YOUR CODE HERE
   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=['accuracy'])
model.summary()

# Expected Output
# Layer (type)                 Output Shape              Param #   
# =================================================================
# embedding (Embedding)        (None, 120, 16)           16000     
# _________________________________________________________________
# global_average_pooling1d (Gl (None, 16)                0         
# _________________________________________________________________
# dense (Dense)                (None, 24)                408       
# _________________________________________________________________
# dense_1 (Dense)              (None, 6)                 150       
# =================================================================
# Total params: 16,558
# Trainable params: 16,558
# Non-trainable params: 0

num_epochs = 30
history = model.fit(
    train_padded, 
    training_label_seq, 
    epochs=num_epochs, 
    validation_data=(validation_padded, validation_label_seq),
    verbose=2)

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")

Imgur

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('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()

try:
  from google.colab import files
except ImportError:
  pass
else:
  files.download('vecs.tsv')
  files.download('meta.tsv')

Imgur

超帥~~~~~~~~~

Anser

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阿葛廷

Coursera Tensorflow Developer Professional Certificate - nlp in tensorflow week02 (Word Embeddings)

Embedding Projector

IMBD dataset

Labels

Tokenizer

Model

Compile

Training

Words Embedding Details

NoteBook (IMBD datasets)

Sarcasem Dataset

調超參數 loss function

NoteBook (Sarcasem Dataset)

tensorflow datasets

imbd_reviews

imdb_reviews/subwords8k

ＮoteBook (mdb_reviews/subwords8k)

Quiz 2 (錯一題)

Week2 Exercise - BBC news archive

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