letor-part-1-model-training-loop.py

loss_hist = []

for i in range(20):
    
    if i > 0:
        
        content_words, context_words, labels = make_skipgrams()
        
        hist = model.fit([content_words, context_words], labels, epochs=1, verbose=0)
        print(f"loss: {hist.history['loss'][-1]:.4f}")
        loss_hist.extend(hist.history['loss'])
    
    embedding_vectors = np.array(embeddings.weights[0].numpy())

    fig, ax = plt.subplots(figsize=(10,10))

    ax.scatter(embedding_vectors[1:, 0], embedding_vectors[1:, 1],  c='white')

    for idx, word in sorted(tokeniser.index_word.items()):
        x_coord = embedding_vectors[idx, 0]
        y_coord = embedding_vectors[idx, 1]

        ax.annotate(
            word, 
            (x_coord, y_coord), 
            horizontalalignment='center',
            verticalalignment='center',
            size=20,
            alpha=0.7
        )
        
        ax.set_title(f"iteration-{i+1}")

    plt.savefig(f"iteration-{i+1:03d}.png")