在 keras 中加载模型后的不同预测

我在 Keras 中构建了一个序列模型，经过训练后它给了我很好的预测，但是当我保存然后加载模型时，我没有在同一数据集上获得相同的预测。为什么？ 请注意，我检查了模型的权重，它们以及模型的架构都是相同的，并使用 model.summary() 和 model.getWeights() 检查。在我看来这很奇怪，我不知道如何处理这个问题。 我没有任何错误，但预测不同

1. 我尝试使用 model.save() 和 load_model()

2. 我尝试使用 model.save_weights() ，然后重新构建模型，然后加载模型

我对这两个选项都有同样的问题。

def Classifier(input_shape, word_to_vec_map, word_to_index, emb_dim, num_activation):

    sentence_indices = Input(shape=input_shape, dtype=np.int32)
    emb_dim = 300  # embedding di 300 parole in italiano
    embedding_layer = pretrained_embedding_layer(word_to_vec_map, word_to_index, emb_dim)

    embeddings = embedding_layer(sentence_indices)   

    X = LSTM(256, return_sequences=True)(embeddings)
    X = Dropout(0.15)(X)
    X = LSTM(128)(X)
    X = Dropout(0.15)(X)
    X = Dense(num_activation, activation='softmax')(X)

    model = Model(sentence_indices, X)

    sequentialModel = Sequential(model.layers)    
    return sequentialModel

    model = Classifier((maxLen,), word_to_vec_map, word_to_index, maxLen, num_activation)
    ...
    model.fit(Y_train_indices, Z_train_oh, epochs=30, batch_size=32, shuffle=True)

    # attempt 1
    model.save('classificationTest.h5', True, True)
    modelRNN = load_model(r'C:\Users\Alessio\classificationTest.h5')

    # attempt 2
    model.save_weights("myWeight.h5")

    model = Classifier((maxLen,), word_to_vec_map, word_to_index, maxLen, num_activation)
    model.load_weights(r'C:\Users\Alessio\myWeight.h5') 

    # PREDICTION TEST
    code_train, category_train, category_code_train, text_train = read_csv_for_email(r'C:\Users\Alessio\Desktop\6Febbraio\2test.csv')

    categories, code_categories = get_categories(r'C:\Users\Alessio\Desktop\6Febbraio\2test.csv')

    X_my_sentences = text_train
    Y_my_labels = category_code_train
    X_test_indices = sentences_to_indices(X_my_sentences, word_to_index, maxLen)
    pred = model.predict(X_test_indices)

    def codeToCategory(categories, code_categories, current_code):

        i = 0;
        for code in code_categories:
            if code == current_code:
                return categories[i]
            i = i + 1 
        return "no_one_find"   

    # result
    for i in range(len(Y_my_labels)):
        num = np.argmax(pred[i])

    # Pretrained embedding layer
    def pretrained_embedding_layer(word_to_vec_map, word_to_index, emb_dim):
    """
    Creates a Keras Embedding() layer and loads in pre-trained GloVe 50-dimensional vectors.

    Arguments:
    word_to_vec_map -- dictionary mapping words to their GloVe vector representation.
    word_to_index -- dictionary mapping from words to their indices in the vocabulary (400,001 words)

    Returns:
    embedding_layer -- pretrained layer Keras instance
    """

    vocab_len = len(word_to_index) + 1                  # adding 1 to fit Keras embedding (requirement)

    ### START CODE HERE ###
    # Initialize the embedding matrix as a numpy array of zeros of shape (vocab_len, dimensions of word vectors = emb_dim)
    emb_matrix = np.zeros((vocab_len, emb_dim))

    # Set each row "index" of the embedding matrix to be the word vector representation of the "index"th word of the vocabulary
    for word, index in word_to_index.items():
        emb_matrix[index, :] = word_to_vec_map[word]

    # Define Keras embedding layer with the correct output/input sizes, make it trainable. Use Embedding(...). Make sure to set trainable=False. 
    embedding_layer = Embedding(vocab_len, emb_dim)
    ### END CODE HERE ###

    # Build the embedding layer, it is required before setting the weights of the embedding layer. Do not modify the "None".
    embedding_layer.build((None,))

    # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.
    embedding_layer.set_weights([emb_matrix])

    return embedding_layer

您有什么建议吗？

提前致谢。

Edit1：如果使用在同一“页面”中保存和加载的代码（我使用笔记本jupyter），它工作正常。如果我更改“页面”，它就不起作用。难道是和tensorflow session有关系？

Edit2：我的最终目标是使用 Java 中的 Deeplearning4J 加载在 Keras 中训练的模型。因此，如果您知道将 keras 模型“转换”为 DL4J 中其他可读内容的解决方案，无论如何它都会有所帮助。

Edit3：添加函数 pretrained_embedding_layer()

Edit4：使用 gensim 读取 word2Vec 模型中的字典

from gensim.models import Word2Vec
model = Word2Vec.load('C:/Users/Alessio/Desktop/emoji_ita/embedding/glove_WIKI')

def getMyModels (model):
word_to_index = dict({})
index_to_word = dict({})
word_to_vec_map = dict({})
for idx, key in enumerate(model.wv.vocab):
    word_to_index[key] = idx
    index_to_word[idx] = key
    word_to_vec_map[key] = model.wv[key]
return word_to_index, index_to_word, word_to_vec_map

加载模型时是否以相同的方式预处理数据？

如果是，您是否设置了预处理函数的种子？ 如果你用 keras 构建字典，句子的顺序是否相同？