Tensorflow 如何保存和恢复Keras LSTM模型？

我训练了一个LSTM网络来预测股票价格，但我不知道如何保存和恢复它

下面是我的代码：

CONST_TRAINING_SEQUENCE_LENGTH = 12
CONST_TESTING_CASES = 5


def dataNormalization(data):
    return [(datum - data[0]) / data[0] for datum in data]


def dataDeNormalization(data, base):
    return [(datum + 1) * base for datum in data]


def getDeepLearningData(ticker):
    # Step 1. Load data
    data = pandas.read_csv('./data/Intraday/' + ticker + '.csv')[
        'close'].tolist()
    # Step 2. Building Training data
    dataTraining = []
    for i in range(len(data) - CONST_TESTING_CASES * CONST_TRAINING_SEQUENCE_LENGTH):
        dataSegment = data[i:i + CONST_TRAINING_SEQUENCE_LENGTH + 1]
        dataTraining.append(dataNormalization(dataSegment))

    dataTraining = numpy.array(dataTraining)
    numpy.random.shuffle(dataTraining)
    X_Training = dataTraining[:, :-1]
    Y_Training = dataTraining[:, -1]

    # Step 3. Building Testing data
    X_Testing = []
    Y_Testing_Base = []
    for i in range(CONST_TESTING_CASES, 0, -1):
        dataSegment = data[-(i + 1) * CONST_TRAINING_SEQUENCE_LENGTH:-i * CONST_TRAINING_SEQUENCE_LENGTH]
        Y_Testing_Base.append(dataSegment[0])
        X_Testing.append(dataNormalization(dataSegment))

    Y_Testing = data[-CONST_TESTING_CASES * CONST_TRAINING_SEQUENCE_LENGTH:]

    X_Testing = numpy.array(X_Testing)
    Y_Testing = numpy.array(Y_Testing)

    # Step 4. Reshape for deep learning
    X_Training = numpy.reshape(X_Training, (X_Training.shape[0], X_Training.shape[1], 1))
    X_Testing = numpy.reshape(X_Testing, (X_Testing.shape[0], X_Testing.shape[1], 1))

    return X_Training, Y_Training, X_Testing, Y_Testing, Y_Testing_Base


def predict(model, X):
    predictionsNormalized = []

    for i in range(len(X)):
        data = X[i]
        result = []

        for j in range(CONST_TRAINING_SEQUENCE_LENGTH):
            predicted = model.predict(data[numpy.newaxis, :, :])[0, 0]
            result.append(predicted)
            data = data[1:]
            data = numpy.insert(data, [CONST_TRAINING_SEQUENCE_LENGTH - 1], predicted, axis=0)

        predictionsNormalized.append(result)

    return predictionsNormalized


def plotResults(Y_Hat, Y):
    plt.plot(Y)

    for i in range(len(Y_Hat)):
        padding = [None for _ in range(i * CONST_TRAINING_SEQUENCE_LENGTH)]
        plt.plot(padding + Y_Hat[i])

    plt.show()


def predictLSTM(ticker):
    # Step 1. Load data
    X_Training, Y_Training, X_Testing, Y_Testing, Y_Testing_Base = getDeepLearningData(ticker)

    # Step 2. Build model
    model = Sequential()

    model.add(LSTM(
        input_shape=(None, 1),
        units=50,
        return_sequences=True))
    model.add(Dropout(0.2))

    model.add(LSTM(
        200,
        return_sequences=False))
    model.add(Dropout(0.2))

    model.add(Dense(units=1))
    model.add(Activation('linear'))

    model.compile(loss='mse', optimizer='rmsprop')

    # Step 3. Train model
    model.fit(X_Training, Y_Training,
              batch_size=512,
              epochs=27,
              validation_split=0.05)

    # Step 4. Predict
    predictionsNormalized = predict(model, X_Testing)

    # Step 5. De-nomalize
    predictions = []
    for i, row in enumerate(predictionsNormalized):
        predictions.append(dataDeNormalization(row, Y_Testing_Base[i]))

    # Step 6. Plot
    plotResults(predictions, Y_Testing)


predictLSTM(ticker='IBM')

现在所有的预测结果都是历史数据。但是我想用这个模型来预测未来的价格。任何朋友都可以用特定的代码来帮助

---

任何朋友都可以帮我，真的很感激

这很简单。首先必须保存模型的json，然后保存模型的权重。保存重量、结构和完整keras模型后，删除先前创建的模型

from pathlib import Path
# Save neural network structure
model_structure = model.to_json()
f = Path("C:\\----yourfolderpath.json")
f.write_text(model_structure)
print('done')

# Save neural network's trained weights
your_model.save_weights("C:\\---------yourfolderpath_weights.h5")
print('done')

# or you can save the full model via:
your_model.save('C:\\---------yourfolderpath_fullkeras_model.h5')

#delete your model in memory
del your_model

#Know to load your model use:
my_new_model = tf.keras.models.load_model("path to model")


#compile my_new_model:
my_new_model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

---

中描述了保存模型及其权重的步骤。以下是一份总结：

• 要保存模型和权重，请使用模型的

  save（）

  函数

• 对于仅存储模型定义，您可以获取其JSON或YAML描述：

要再次加载它，只需应用

    # model reconstruction from JSON:
    from keras.models import model_from_json
    model = model_from_json(json_string)

    # model reconstruction from YAML:
    from keras.models import model_from_yaml
    model = model_from_yaml(yaml_string)

• 如果您只想存储权重，请使用

再次加载模型后，您可以通过将其应用于以前加载的数据来使用它，如

predicted_output = model.predict(input, batch_size=BS)

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非常感谢你的朋友！你能告诉我“通过编译初始化你的模型”的更具体步骤或代码吗？我在深度学习方面真的很新。例如，我如何通过“通过编译初始化你的模型”预测ibm股票真的很精确！欢迎光临。我也是新来的。您需要调用“model.predict（）”`这是文档：嗨，亲爱的朋友，你能帮我解决这个相关问题吗？非常感谢！嗨，朋友们，当尝试预测时，输出=模型。预测（新数据，批量大小=512）嗨，亲爱的朋友，你能帮我解决这个相关问题吗？非常感谢@Martin我们需要在LSTM中存储所有参数吗？当我有1K大小的LSTM单元时，还有其他选择吗？你必须存储所有的数据。否则，您将丢失从培训中获得的信息。

    # model reconstruction from JSON:
    from keras.models import model_from_json
    model = model_from_json(json_string)

    # model reconstruction from YAML:
    from keras.models import model_from_yaml
    model = model_from_yaml(yaml_string)

    model.save_weights('my_model_weights.h5')  # to store
    model.load_weights('my_model_weights.h5')  # to load

predicted_output = model.predict(input, batch_size=BS)