Supplemental material for https://austingwalters.com/fasttext-for-sentence-classification/

fasttext.py

import keras
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Embedding,GlobalAveragePooling1D

model = Sequential()

# Created Embedding (Input) Layer (max_words) --> Pooling Layer
model.add(Embedding(max_words, embedding_dims, input_length=maxlen))

# Create the average Pooling Layer
model.add(GlobalAveragePooling1D())

# Create the output layer (num_classes)
model.add(Dense(num_classes, activation='softmax'))

# Add optimization method, loss function and optimization value
model.compile(loss='categorical_crossentropy',
              optimizer='adam', metrics=['accuracy'])

# "Fit the model" (train model), using training data (80% of datset)
model.fit(x_train, y_train, batch_size=batch_size,
          epochs=epochs, validation_data=(x_test, y_test))

# Evaluate the trained model, using the test data (20% of the dataset)
score = model.evaluate(x_test, y_test, batch_size=batch_size)