def build_model(max_features=1, maxlen=65): """Build LSTM model""" model = Sequential() model.add(Embedding(max_features, 128, input_length=maxlen)) model.add(LSTM(128)) model.add(Dropout(0.5)) model.add(Dense(1)) model.add(Activation('sigmoid')) # model.add(tf.keras.layers.BatchNormalization()) model.compile(loss='binary_crossentropy', optimizer='rmsprop') return model def data(Domain): valid_chars = pickle.load(open('dictionary.plk','rb')) Domain = [[valid_chars[y] for y in x] for x in Domain] Domain = sequence.pad_sequences(Domain, maxlen=64) return Domain def run(Domain): Domains=data(Domain) model = build_model() model = model.load_weights('Mymodel.h5') predictions = model.predict(Domains) return predictions if __name__ == "__main__": print(run('baidu.com')) # Run with 1 to make it fast

在 `run` 函数中，使用 `model.load_weights` 方法加载模型权重时，不需要重新定义 `model`，直接使用之前定义的 `model` 即可。修改后的代码如下：

def run(Domain):
    Domains=data(Domain)
    model = build_model()
    model.load_weights('Mymodel.h5')
    predictions = model.predict(Domains)
    return predictions

此外，`run` 函数的参数需要传入一个列表或数组，因为 `data` 函数返回的是一个二维数组。修改后的代码如下：

if __name__ == "__main__":
    print(run(['baidu.com'])) # 注意需要传入一个列表或数组

另外，`build_model` 函数中的 `maxlen` 参数为 65，而在 `data` 函数中使用的是 64。需要保持一致。

相关问题

def build_model(max_features=1, maxlen=65): """Build LSTM model""" model = Sequential() model.add(Embedding(max_features, 128, input_length=maxlen)) model.add(LSTM(128)) model.add(Dropout(0.5)) model.add(Dense(1)) model.add(Activation('sigmoid')) # model.add(tf.keras.layers.BatchNormalization()) model.compile(loss='binary_crossentropy', optimizer='rmsprop') return model def data(Domain): valid_chars = pickle.load(open('dictionary.plk','rb')) Domain = [[valid_chars[y] for y in x] for x in Domain] Domain = sequence.pad_sequences(Domain, maxlen=65) return Domain def run(Domain): Domains = data(Domain) modelaaa = build_model(max_features=1, maxlen=65) modelaaa.load_weights('Mymodel.h5') predictions = model.predict(Domains) return predictions if __name__ == "__main__": print(run(['baidu.com'])) # Run with 1 to make it fast报错Unable to load weights saved in HDF5 format into a subclassed Model which has not created its variables yet. Call the Model first, then load the weights.怎么改

在 `run` 函数中，我们应该先创建模型，再加载权重。所以我们需要将创建模型和加载权重的顺序调换一下，同时将模型命名为 `modelaaa`，但在 `predict` 中使用的却是 `model`，需要将其改为 `modelaaa`。修改后的代码如下：

def run(Domain):
    Domains = data(Domain)
    modelaaa = build_model(max_features=1, maxlen=65)
    modelaaa.load_weights('Mymodel.h5')
    predictions = modelaaa.predict(Domains)
    return predictions

这样就可以避免 `Unable to load weights saved in HDF5 format into a subclassed Model which has not created its variables yet. Call the Model first, then load the weights.` 的错误了。

import numpy as np from keras.preprocessing import sequence from keras_preprocessing import sequence from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from tensorflow.python.keras.layers.embeddings import Embedding from keras.layers import LSTM import sklearn import pickle # from sklearn.cross_validation import train_test_split import sklearn.model_selection from sklearn.model_selection import train_test_split from keras.models import load_model def build_model(max_features=1, maxlen=65): """Build LSTM model""" model = Sequential() model.add(Embedding(max_features, 128, input_length=maxlen)) model.add(LSTM(128)) model.add(Dropout(0.5)) model.add(Dense(1)) model.add(Activation('sigmoid')) # model.add(tf.keras.layers.BatchNormalization()) model.compile(loss='binary_crossentropy', optimizer='rmsprop') return model def data(Domain): valid_chars = pickle.load(open('dictionary.plk','rb')) Domain = [[valid_chars[y] for y in x] for x in Domain] Domain = sequence.pad_sequences(Domain, maxlen=65) return Domain def run(Domain): Domains=data(Domain) model = build_model() model.load_weights('Mymodel.h5') predictions = model.predict(Domains) return predictions if name == "main": print(run(['baidu.com'])) # Run with 1 to make it fast

这段代码是一个使用 LSTM 模型对域名进行分类的程序，其中包括了模型的构建和加载，以及对数据进行预处理和预测。具体来说，该程序使用了 Keras 和 TensorFlow 库进行模型的建立和训练，同时使用了 pickle 库进行数据的读取和存储。在运行该程序时，输入一个域名进行分类，输出该域名属于恶意或者良性的概率。