imdb.py

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, Flatten, Dense, Dropout, Conv1D, MaxPooling1D
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np
import matplotlib.pyplot as plt

# Model configuration
max_sequence_length = 100
num_distinct_words = 10000
embedding_output_dims = 15
loss_function = 'binary_crossentropy'
optimizer = 'adam'
additional_metrics = ['accuracy']
number_of_epochs = 100
verbosity_mode = True
validation_split = 0.20

# Load dataset
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=num_distinct_words)
print(x_train.shape)
print(x_test.shape)

# Here, you'd normally test first that the model generalizes and concatenate all data
# (that is, normally, you'd perform e.g. K-fold Cross Validation first)
# Then, you can use all data for a full training run. Now, we'll use x_train for training only.

# Pad all sequences
padded_inputs = pad_sequences(x_train, maxlen=max_sequence_length, value = 0.0) # 0.0 because it corresponds with <PAD>
padded_inputs_test = pad_sequences(x_test, maxlen=max_sequence_length, value = 0.0) # 0.0 because it corresponds with <PAD>

# Obtain 3 texts
for i in np.random.randint(0, len(padded_inputs), 3):
  INDEX_FROM=3   # word index offset
  word_to_id = imdb.get_word_index()
  word_to_id = {k:(v+INDEX_FROM) for k,v in word_to_id.items()}
  word_to_id["<PAD>"] = 0
  word_to_id["<START>"] = 1
  word_to_id["<UNK>"] = 2
  word_to_id["<UNUSED>"] = 3

  id_to_word = {value:key for key,value in word_to_id.items()}
  print('=================================================')
  print(f'Sample = {i} | Length = {len(padded_inputs[i])}')
  print('=================================================')
  print(' '.join(id_to_word[id] for id in padded_inputs[i] ))

# Define the Keras model
model = Sequential()
model.add(Embedding(num_distinct_words, embedding_output_dims, input_length=max_sequence_length))
model.add(Dropout(0.50))
model.add(Conv1D(filters=32, kernel_size=2, padding='same', activation='relu'))
model.add(Dropout(0.50))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dropout(0.50))
model.add(Dense(1, activation='sigmoid'))

# Compile the model
model.compile(optimizer=optimizer, loss=loss_function, metrics=additional_metrics)

# Give a summary
model.summary()

# Train the model
history = model.fit(padded_inputs, y_train, epochs=number_of_epochs, verbose=verbosity_mode, validation_split=validation_split)

# Test the model after training
test_results = model.evaluate(padded_inputs_test, y_test, verbose=False)
print(f'Test results - Loss: {test_results[0]} - Accuracy: {100*test_results[1]}%')

# Visualize history
# Plot history: Validation loss
plt.plot(history.history['val_loss'])
plt.title('Validation loss history')
plt.ylabel('Loss value')
plt.xlabel('No. epoch')
plt.show()

# Plot history: Accuracy
plt.plot(history.history['val_accuracy'])
plt.title('Validation accuracy history')
plt.ylabel('Accuracy value (%)')
plt.xlabel('No. epoch')
plt.show()