To run this:

- `pip3 install -r requirements.txt`

- For training, since we're using transfer learning, you first need to download, extract [GloVe](http://nlp.stanford.edu/data/glove.6B.zip) and put to `data` folder, this is a pre trained embedding vectors that map each word to its vector, two words that have similar meanings tend to have very close vectors, and so on.

```

from keras.preprocessing.text import Tokenizer

from keras.preprocessing.sequence import pad_sequences

from keras.utils import to_categorical

from 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_embedding_vectors, get_model, SEQUENCE_LENGTH, EMBEDDING_SIZE, TEST_SIZE

from utils import BATCH_SIZE, EPOCHS, int2label, label2int

def load_data():

"""

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

X, y = load_data()

tokenizer = Tokenizer()

tokenizer.fit_on_texts(X)

pickle.dump(tokenizer, open("results/tokenizer.pickle", "wb"))

X = tokenizer.texts_to_sequences(X)

print(X[0])

X = np.array(X)

y = np.array(y)

X = pad_sequences(X, maxlen=SEQUENCE_LENGTH)

print(X[0])

y = [ label2int[label] for label in y ]

y = to_categorical(y)

print(y[0])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=7)

model = get_model(tokenizer=tokenizer, lstm_units=128)

model_checkpoint = ModelCheckpoint("results/spam_classifier_{val_loss:.2f}", save_best_only=True,

verbose=1)

tensorboard = TensorBoard(f"logs/spam_classifier_{time.time()}")

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)

model.fit(X_train, y_train, validation_data=(X_test, y_test),

batch_size=BATCH_SIZE, epochs=EPOCHS,

callbacks=[tensorboard, model_checkpoint],

verbose=1)

result = model.evaluate(X_test, y_test)

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}%")