Make predictions for out seq2seq with attention model

predict_att_seq2seq.py

def predict_seq2seq_att(input_text, input_max_len, tokenizer_inputs, word2idx_outputs, idx2word_outputs):
    if input_text is None:
        input_text = input_data[np.random.choice(len(input_data))]
    print(input_text)
    # Tokenize the input text
    input_seq = tokenizer_inputs.texts_to_sequences([input_text])
    # Pad the sentence
    input_seq = pad_sequences(input_seq, maxlen=input_max_len, padding='post')
    # Get the encoder initial states
    en_initial_states = encoder.init_states(1)
    # Get the encoder outputs or hidden states
    en_outputs = encoder(tf.constant(input_seq), en_initial_states)
    # Set the decoder input to the sos token
    de_input = tf.constant([[word2idx_outputs['<sos>']]])
    # Set the initial hidden states of the decoder to the hidden states of the encoder
    de_state_h, de_state_c = en_outputs[1:]
    
    out_words = []
    alignments = []

    while True:
        # Get the decoder with attention output
        de_output, de_state_h, de_state_c, alignment = decoder(
            de_input, (de_state_h, de_state_c), en_outputs[0])
        de_input = tf.expand_dims(tf.argmax(de_output, -1), 0)
        # Detokenize the output
        out_words.append(idx2word_outputs[de_input.numpy()[0][0]])
        # Save the aligment matrix
        alignments.append(alignment.numpy())

        if out_words[-1] == '<eos>' or len(out_words) >= 20:
            break
    # Join the output words
    print(' '.join(out_words))
    return np.array(alignments), input_text.split(' '), out_words