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。

相关问题

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有错误吗

这段代码中似乎没有明显的语法错误，但是在导入模块的时候存在重复导入同一个模块的情况。具体来说，`from keras.preprocessing import sequence` 和 `from keras_preprocessing import sequence` 两行代码都导入了 `sequence` 模块，实际上只需要保留其中一行即可。另外，`name` 变量在程序中没有被定义，需要将其改为 `__name__`。

import tensorflow as tf from tensorflow import keras import numpy as np # 加载 IMDB 数据集 imdb = keras.datasets.imdb (train_data, train_labels), (test, test_labels) = imdb.load_data(num_words=00) # 将整数序列填充为相同的长度 train_data = keras.preprocessing.sequence.pad_sequences(train_data, value=0, padding='post', maxlen=256) test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=0, padding='post', maxlen=256) # 构建模型 model = keras.Sequential() model.add(keras.layers.Embed(10000, 16)) model.add(keras.layers.GlobalAveragePooling1D()) model.add(keras.layers(16, activation=tf.nn.relu)) model.add(keras.layers.Dense(1, activation=tf.sigmoid)) # 编译模型 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # 训练模型 history = model.fit(train_data, train_labels, epochs=40, batch_size=512, validation_data=(test_data, test_labels), verbose=1) # 在测试数据上评估模型 results = model.evaluate(test_data, test_labels) print('Test loss:', results[0]) print('Test accuracy:', results[1])test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=0, padding='post', maxlen=256) NameError: name 'test_data' is not defined

这个错误的原因是在使用 `test_data` 变量之前没有定义它。你需要先加载测试数据集 `test_data`，然后再使用 `pad_sequences` 函数对其进行填充。

你可以尝试在以下代码中添加加载测试数据集的语句：

import tensorflow as tf 
from tensorflow import keras 
import numpy as np 

# 加载 IMDB 数据集
imdb = keras.datasets.imdb 
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000) 

# 将整数序列填充为相同的长度
maxlen = 256
train_data = keras.preprocessing.sequence.pad_sequences(train_data, value=0, padding='post', maxlen=maxlen)
test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=0, padding='post', maxlen=maxlen)

# 构建模型
model = keras.Sequential() 
model.add(keras.layers.Embedding(10000, 16)) 
model.add(keras.layers.GlobalAveragePooling1D()) 
model.add(keras.layers.Dense(16, activation=tf.nn.relu)) 
model.add(keras.layers.Dense(1, activation=tf.sigmoid)) 

# 编译模型
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) 

# 训练模型
history = model.fit(train_data, train_labels, epochs=40, batch_size=512, validation_data=(test_data, test_labels), verbose=1) 

# 在测试数据上评估模型
results = model.evaluate(test_data, test_labels) 
print('Test loss:', results[0]) 
print('Test accuracy:', results[1])

这样你就不会再遇到 `NameError: name 'test_data' is not defined` 错误了。