详细的记录 evaluate函数的实现。
解决报错

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ValueError                                Traceback (most recent call last)
<ipython-input-44-2ec1176683f0> in <module>
----> 1 translate(u'Estoy trabajando.')

<ipython-input-43-4364cc5c7981> in translate(input_sentence)
     49 
     50 def translate(input_sentence):
---> 51     results, input_sentence, attention_matrix = evaluate(input_sentence)
     52 
     53     print("Input: %s" % (input_sentence))

<ipython-input-43-4364cc5c7981> in evaluate(input_sentence)
     20     decoding_input = tf.expand_dims([out_tokenizer.word_index['<start>']], 0)
     21     for t in range(max_length_output):
---> 22         predictions. decoding_hidden, attention_weights = decoder(decoding_input, decoding_hidden, encoding_outputs)
     23         attention_weights = tf.reshape(attention_weights, (-1,))
     24         attention_matrix[t] = attention_weights.numpy()

ValueError: too many values to unpack (expected 2)

注意看predictions 后面的标点符号

接收的是一个文本的输入，首先就要转换成适合模型的数据类型。

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def evaluate(input_sentence):
    attention_matrix = np.zeros((max_length_output, max_length_input)) 
    input_sentence = preprocess_sentence(input_sentence) # 输入的句子进行预处理。就是分割标点符号/

    inputs = [input_tokenizer.word_index[token] for token in input_sentence.split(' ')] # text--->id 把句子转换成id
    inputs = keras.preprocessing.sequence.pad_sequences([inputs], maxlen = max_length_input, padding= 'post') # 把转换成id的向量，进行padding
    inputs = tf.convert_to_tensor(inputs) #把向量转换为tensor

    results = '' # 定义str, 保存translate的结果

#     encoding_hidden = encoder.initialize_hidden_state()

    encoding_hidden = tf.zeros((1, units)) #初始化encoding_hidden层

    encoding_outputs, encoding_hidden = encoder(inputs, encoding_hidden) # 这一步得到的encoding_hidden就是decoding_hidden 的第一个值
    decoding_hidden = encoding_hidden


    decoding_input = tf.expand_dims([out_tokenizer.word_index['<start>']], 0) # 找到开始的第一个输入的id
    for t in range(max_length_output):
        predictions, decoding_hidden, attention_weights = decoder(decoding_input, decoding_hidden, encoding_outputs)
        attention_weights = tf.reshape(attention_weights, (-1,))
        attention_matrix[t] = attention_weights.numpy()

        predicted_id = tf.argmax(predictions[0]).numpy()

        results += out_tokenizer.index_word[predicted_id] + ' '

        if out_tokenizer.index_word[predicted_id] == '<end>':
            return results, input_sentence, attention_matrix

        decoding_input = tf.expand_dims([predicted_id], 0)
    return results, input_sentence, attention_matrix

def plot_attention(attention_matrix, input_sentence, predicted_sentence):
    fig = plt.figure(figsize=(10,10))
    ax = fig.add_subplot(1, 1, 1)

    ax.matshow(attention_matrix, cmap='viridis')

    font_dict = {'fontsize': 14}

    ax.set_xticklabels([''] + input_sentence,
                              fontdict = font_dict, rotation = 90)
    ax.sey_yticklables([''] + predicted_sentence,
                              fontdict = font_dict,)
    plt.show()

def translate(input_sentence):
    results, input_sentence, attention_matrix = evaluate(input_sentence)

    print("Input: %s" % (input_sentence))
    print("Predicted translation: %s" % (results))

    attention_matrix = attention_matrix[:len(results.split(' ')),
                                                       :len(input_sentence.split(' '))]
    plot_attention(attention_matrix, input_sentence.split(' '), results.split(' '))