Now that my sentiment analysis network is built, it's time to train:
data = load_data(20000)data = pad_sequences(data)model = build_network(vocab_size=data["vocab_size"], embedding_dim=100, sequence_length=data["sequence_length"])callbacks = create_callbacks("sentiment")model.fit(x=data["X_train"], y=data["y_train"], batch_size=32, epochs=10, validation_data=(data["X_test"], data["y_test"]), callbacks=callbacks)
Keeping all of my training parameters and data in a single dictionary like this is just really a question of style and less about function. You may prefer to handle everything separately. I like using a dictionary for everything because it keeps me from passing big lists of parameters back and forth.
Since we're ...
