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 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。

import matplotlib.pyplot as plt import tensorflow as tf from tensorflow import keras import numpy as np #加载IMDB数据 imdb = keras.datasets.imdb (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=100) print("训练记录数量：{}，标签数量：{}".format(len(train_data),len(train_labels))) print(train_data[0]) #数据标准化 train_data = keras.preprocessing.sequence.pad_sequences(train_data,value=0,padding='post',maxlen=256) #text_data = keras.preprocessing.sequence.pad_sequences(train_data,value=0,padding='post',maxlen=256) text_data = keras.preprocessing.sequence.pad_sequences(test_data,value=0,padding='post',maxlen=256) print(train_data[0]) #构建模型 vocab_size = 10000 model = tf.keras.Sequential([tf.keras.layers.Embedding(vocab_size, 64), tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.layers.Dense(64,activation='relu'), tf.keras.layers.Dense(1) ]) model.summary() #配置并训练模型 model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy']) x_val = train_data[:10000] partial_x_train = train_data[10000:] y_val = train_labels[:10000] partial_y_train = train_labels[10000:] history = model.fit(partial_x_train,partial_y_train,epochs=1,batch_size=512,validation_data=(x_val,y_val),verbose=1) #测试性能 results = model.evaluate(test_data, test_labels, verbose=2) print(results) #训练过程可视化 history_dict = history.history print(history_dict.keys()) def plot_graphs(history, string): plt.plot(history.history[string]) plt.plot(history.history['val_'+string]) plt.xlabel("Epochs") plt.ylabel(string) plt.legend([string,'val_'+string]) plt.show() plot_graphs(history,"accuracy") plot_graphs(history,"loss")

这段代码使用了IMDB电影评论数据集，通过对评论进行情感分析，将评论分为正面或负面两类。其中，数据标准化使用了keras.preprocessing.sequence.pad_sequences函数，将每条评论的长度都设置为256，模型使用了嵌入层、双向LSTM层和2个全连接层，其中嵌入层的大小为10000，LSTM层的大小为64，全连接层的大小为64和1。模型的优化器使用了Adam，损失函数使用了二元交叉熵，评价指标使用了准确率。并且，使用了matplotlib库，对模型的训练过程进行可视化。