Code for How to Build a Spam Classifier using Keras and TensorFlow in Python Tutorial

utils.py

import tqdm
import numpy as np
from tensorflow.keras.layers import Embedding, LSTM, Dropout, Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.metrics import Recall, Precision

SEQUENCE_LENGTH = 100 # the length of all sequences (number of words per sample)
EMBEDDING_SIZE = 100  # Using 100-Dimensional GloVe embedding vectors
TEST_SIZE = 0.25 # ratio of testing set

BATCH_SIZE = 64
EPOCHS = 20 # number of epochs

label2int = {"ham": 0, "spam": 1}
int2label = {0: "ham", 1: "spam"}

def get_embedding_vectors(tokenizer, dim=100):
    embedding_index = {}
    with open(f"data/glove.6B.{dim}d.txt", encoding='utf8') as f:
        for line in tqdm.tqdm(f, "Reading GloVe"):
            values = line.split()
            word = values[0]
            vectors = np.asarray(values[1:], dtype='float32')
            embedding_index[word] = vectors
    word_index = tokenizer.word_index
    # we do +1 because Tokenizer() starts from 1
    embedding_matrix = np.zeros((len(word_index)+1, dim))
    for word, i in word_index.items():
        embedding_vector = embedding_index.get(word)
        if embedding_vector is not None:
            # words not found will be 0s
            embedding_matrix[i] = embedding_vector
    return embedding_matrix


def get_model(tokenizer, lstm_units):
    """
    Constructs the model,
    Embedding vectors => LSTM => 2 output Fully-Connected neurons with softmax activation
    """
    # get the GloVe embedding vectors
    embedding_matrix = get_embedding_vectors(tokenizer)
    model = Sequential()
    model.add(Embedding(len(tokenizer.word_index)+1,
              EMBEDDING_SIZE,
              weights=[embedding_matrix],
              trainable=False,
              input_length=SEQUENCE_LENGTH))

    model.add(LSTM(lstm_units, recurrent_dropout=0.2))
    model.add(Dropout(0.3))
    model.add(Dense(2, activation="softmax"))
    # compile as rmsprop optimizer
    # aswell as with recall metric
    model.compile(optimizer="rmsprop", loss="categorical_crossentropy",
                  metrics=["accuracy", Precision(), Recall()])
    model.summary()
    return model

spam_classifier.py

import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    # only use GPU memory that we need, not allocate all the GPU memory
    tf.config.experimental.set_memory_growth(gpus[0], enable=True)

from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
from sklearn.model_selection import train_test_split
import time
import numpy as np
import pickle

from utils import get_model, SEQUENCE_LENGTH, TEST_SIZE
from utils import BATCH_SIZE, EPOCHS, label2int


def load_data():
    """
    Loads SMS Spam Collection dataset
    """
    texts, labels = [], []
    with open("data/SMSSpamCollection") as f:
        for line in f:
            split = line.split()
            labels.append(split[0].strip())
            texts.append(' '.join(split[1:]).strip())
    return texts, labels

    
# load the data
X, y = load_data()

# Text tokenization
# vectorizing text, turning each text into sequence of integers
tokenizer = Tokenizer()
tokenizer.fit_on_texts(X)
# lets dump it to a file, so we can use it in testing
pickle.dump(tokenizer, open("results/tokenizer.pickle", "wb"))

# convert to sequence of integers
X = tokenizer.texts_to_sequences(X)
print(X[0])
# convert to numpy arrays
X = np.array(X)
y = np.array(y)
# pad sequences at the beginning of each sequence with 0's
# for example if SEQUENCE_LENGTH=4:
# [[5, 3, 2], [5, 1, 2, 3], [3, 4]]
# will be transformed to:
# [[0, 5, 3, 2], [5, 1, 2, 3], [0, 0, 3, 4]]
X = pad_sequences(X, maxlen=SEQUENCE_LENGTH)
print(X[0])
# One Hot encoding labels
# [spam, ham, spam, ham, ham] will be converted to:
# [1, 0, 1, 0, 1] and then to:
# [[0, 1], [1, 0], [0, 1], [1, 0], [0, 1]]

y = [ label2int[label] for label in y ]
y = to_categorical(y)
print(y[0])

# split and shuffle
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=7)
# print our data shapes
print("X_train.shape:", X_train.shape)
print("X_test.shape:", X_test.shape)
print("y_train.shape:", y_train.shape)
print("y_test.shape:", y_test.shape)
# constructs the model with 128 LSTM units
model = get_model(tokenizer=tokenizer, lstm_units=128)

# initialize our ModelCheckpoint and TensorBoard callbacks
# model checkpoint for saving best weights
model_checkpoint = ModelCheckpoint("results/spam_classifier_{val_loss:.2f}.h5", save_best_only=True,
                                    verbose=1)
# for better visualization
tensorboard = TensorBoard(f"logs/spam_classifier_{time.time()}")

# train the model
model.fit(X_train, y_train, validation_data=(X_test, y_test),
          batch_size=BATCH_SIZE, epochs=EPOCHS,
          callbacks=[tensorboard, model_checkpoint],
          verbose=1)

# get the loss and metrics
result = model.evaluate(X_test, y_test)
# extract those
loss = result[0]
accuracy = result[1]
precision = result[2]
recall = result[3]

print(f"[+] Accuracy: {accuracy*100:.2f}%")
print(f"[+] Precision:   {precision*100:.2f}%")
print(f"[+] Recall:   {recall*100:.2f}%")

test.py

import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    # only use GPU memory that we need, not allocate all the GPU memory
    tf.config.experimental.set_memory_growth(gpus[0], enable=True)
from utils import get_model, int2label
from tensorflow.keras.preprocessing.sequence import pad_sequences

import pickle
import numpy as np

SEQUENCE_LENGTH = 100

# get the tokenizer
tokenizer = pickle.load(open("results/tokenizer.pickle", "rb"))

model = get_model(tokenizer, 128)
# change to the model name in results folder
model.load_weights("results/spam_classifier_0.06.h5")

def get_predictions(text):
    sequence = tokenizer.texts_to_sequences([text])
    # pad the sequence
    sequence = pad_sequences(sequence, maxlen=SEQUENCE_LENGTH)
    # get the prediction
    prediction = model.predict(sequence)[0]
    # one-hot encoded vector, revert using np.argmax
    return int2label[np.argmax(prediction)]


while True:
    text = input("Enter the mail:")
    # convert to sequences
    print(get_predictions(text))

If you want the tutorial notebook, refer to the GitHub repo here.