### 使用 Jupyter Notebook 实现智能客服的代码示例 以下是基于 Python 和 TensorFlow 的简单智能客服实现方案。此代码可以在 Jupyter Notebook 中运行。 #### 安装依赖库 在使用之前，请确保已安装必要的库，例如 `tensorflow` 和 `numpy`。可以通过以下命令安装这些库： ```bash !pip install tensorflow numpy ``` #### 导入所需模块并定义模型结构 在第一个单元格中导入所需的模块，并构建一个简单的神经网络用于意图分类。 ```python import numpy as np import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Embedding, GlobalAveragePooling1D from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split # 设置随机种子以便结果可重复 np.random.seed(42) tf.random.set_seed(42) # 数据集 (简化版) intents = [ {"tag": "greeting", "patterns": ["hello", "hi", "hey"]}, {"tag": "farewell", "patterns": ["bye", "goodbye", "see you later"]} ] tags = [intent["tag"] for intent in intents] all_patterns = [pattern for intent in intents for pattern in intent["patterns"]] label_encoder = LabelEncoder() labels = label_encoder.fit_transform(tags * max(len(intent["patterns"]) for intent in intents)) # 将文本转换为数值向量 tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=1000, oov_token="<OOV>") tokenizer.fit_on_texts(all_patterns) sequences = tokenizer.texts_to_sequences(all_patterns) padded_sequences = tf.keras.preprocessing.sequence.pad_sequences(sequences, padding="post") X_train, X_val, y_train, y_val = train_test_split(padded_sequences, labels, test_size=0.2, random_state=42) ``` [^1] #### 构建和训练模型 创建一个简单的嵌入层模型来处理文本数据，并将其映射到标签空间。 ```python vocab_size = len(tokenizer.word_index) + 1 embedding_dim = 16 max_length = padded_sequences.shape[1] model = Sequential([ Embedding(vocab_size, embedding_dim, input_length=max_length), GlobalAveragePooling1D(), Dense(16, activation="relu"), Dense(len(set(labels)), activation="softmax") # 输出维度等于类别数 ]) model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) history = model.fit(X_train, y_train, epochs=50, validation_data=(X_val, y_val), verbose=2) ``` [^3] #### 测试模型 通过输入新的句子测试模型的表现。 ```python def predict_intent(text): sequence = tokenizer.texts_to_sequences([text]) padded_sequence = tf.keras.preprocessing.sequence.pad_sequences(sequence, maxlen=max_length, padding="post") prediction = model.predict(padded_sequence)[0] predicted_tag_idx = np.argmax(prediction) confidence = prediction[predicted_tag_idx] if confidence > 0.7: # 可调整阈值 return label_encoder.inverse_transform([predicted_tag_idx])[0], confidence else: return "unknown", confidence test_sentences = ["hi there", "cya", "howdy"] for sentence in test_sentences: tag, confidence = predict_intent(sentence) print(f"Sentence: {sentence} -> Predicted Tag: {tag}, Confidence: {confidence:.2f}") ``` [^2] --- ###