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])优化代码

以下是对代码的优化： 1. 使用更加简洁的导入方式： ``` from tensorflow import keras import tensorflow as tf import numpy as np ``` 2. 将模型的架构定义为一个函数，这样可以使代码更加清晰： ``` def build_model(): model = keras.Sequential([ keras.layers.Embedding(10000, 16), keras.layers.GlobalAveragePooling1D(), keras.layers.Dense(16, activation=tf.nn.relu), keras.layers.Dense(1, activation=tf.sigmoid) ]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) return model ``` 3. 使用 EarlyStopping 和 ModelCheckpoint 回调函数来提高模型的性能： ``` early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5) model_checkpoint = keras.callbacks.ModelCheckpoint('best_model.h5', save_best_only=True) history = model.fit(train_data, train_labels, epochs=40, batch_size=512, validation_data=(test_data, test_labels), verbose=1, callbacks=[early_stop, model_checkpoint]) ``` 4. 使用更加简洁的方式来输出测试结果： ``` loss, accuracy = model.evaluate(test_data, test_labels) print(f'Test loss: {loss}, Test accuracy: {accuracy}') ``` 综上所述，我们可以对代码进行如下的优化： ``` from tensorflow import keras import tensorflow as tf import numpy as np def build_model(): model = keras.Sequential([ keras.layers.Embedding(10000, 16), keras.layers.GlobalAveragePooling1D(), keras.layers.Dense(16, activation=tf.nn.relu), keras.layers.Dense(1, activation=tf.sigmoid) ]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) return model # 加载 IMDB 数据集 (train_data, train_labels), (test_data, test_labels) = keras.datasets.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 = build_model() # 使用 EarlyStopping 和 ModelCheckpoint 回调函数来提高模型的性能 early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5) model_checkpoint = keras.callbacks.ModelCheckpoint('best_model.h5', save_best_only=True) # 训练模型 history = model.fit(train_data, train_labels, epochs=40, batch_size=512, validation_data=(test_data, test_labels), verbose=1, callbacks=[early_stop, model_checkpoint]) # 在测试数据上评估模型 loss, accuracy = model.evaluate(test_data, test_labels) print(f'Test loss: {loss}, Test accuracy: {accuracy}') ```