59 lines
1.9 KiB
Python
59 lines
1.9 KiB
Python
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import numpy as np
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from keras.layers import Embedding, SimpleRNN, Dense
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from keras.models import Sequential
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# 训练数据（包含逗号）
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text = "用户：今天想吃火锅吗？ 客服：我们海鲜火锅很受欢迎。用户：但朋友对海鲜过敏，推荐其他吧。客服：好的，我们有菌汤火锅。"
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base_chars = ['，', '。', '？', '：'] # 确保基础标点存在
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chars = sorted(list(set(text + ''.join(base_chars))))
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char_to_idx = {c:i for i,c in enumerate(chars)}
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idx_to_char = {i:c for c,i in char_to_idx.items()}
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# 创建训练序列
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max_length = 20
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X, y = [], []
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for i in range(len(text)-max_length):
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seq = text[i:i+max_length]
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target = text[i+max_length]
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X.append([char_to_idx[c] for c in seq])
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y.append(char_to_idx[target])
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# 模型构建
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model = Sequential([
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Embedding(input_dim=len(chars), output_dim=32, input_length=max_length),
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SimpleRNN(128),
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Dense(len(chars), activation='softmax')
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])
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model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')
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# 训练
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X = np.array(X)
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y = np.array(y)
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model.fit(X, y, epochs=50, batch_size=32)
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# 增强后的生成函数
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def generate_response(prompt):
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generated = prompt
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for _ in range(30):
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# 过滤并处理未知字符
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valid_chars = []
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for c in generated[-max_length:]:
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if c in char_to_idx:
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valid_chars.append(c)
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else:
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valid_chars.append(' ') # 未知字符替换为空格
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# 填充序列
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seq = valid_chars[-max_length:]
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seq = seq + [' ']*(max_length - len(seq))
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# 转换为索引
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seq_indices = [char_to_idx[c] for c in seq]
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# 生成下一个字符
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pred = model.predict(np.array([seq_indices]), verbose=0)
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next_char = idx_to_char[np.argmax(pred)]
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generated += next_char
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return generated
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print(generate_response("用户：朋友海鲜过敏，能不能推荐一些其他的？"))
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