We have covered a lot in this chapter. Let's consolidate all our code here:
from keras.datasets import imdb
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Embedding
from keras.layers import Dense, Embedding
from keras.layers import LSTM
from matplotlib import pyplot as plt
from sklearn.metrics import confusion_matrix
import seaborn as sns
# Import IMDB dataset
training_set, testing_set = imdb.load_data(num_words = 10000)
X_train, y_train = training_set
X_test, y_test = testing_set
print("Number of training samples = {}".format(X_train.shape[0]))
print("Number of testing samples = {}".format(X_test.shape[0]))
# Zero-Padding
X_train_padded = sequence.pad_sequences(X_train, maxlen= 100)
X_test_padded = sequence.pad_sequences(X_test, maxlen= 100)
print("X_train vector shape = {}".format...