54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
import torch
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import torch.nn as nn
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import numpy as np
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import matplotlib.pyplot as plt
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plt.rcParams['font.sans-serif'] = ['SimHei'] # 使用黑体显示中文:ml-citation{ref="1,2" data="citationList"}
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plt.rcParams['axes.unicode_minus'] = False #
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# 手动实现RNN单元
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class SimpleRNNCell:
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def __init__(self, input_size, hidden_size):
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# 权重初始化
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self.W_xh = torch.randn(input_size, hidden_size) * 0.01
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self.W_hh = torch.randn(hidden_size, hidden_size) * 0.01
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self.b_h = torch.zeros(1, hidden_size)
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def forward(self, x, h_prev):
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"""
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x: 当前输入 (1, input_size)
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h_prev: 前一刻隐藏状态 (1, hidden_size)
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"""
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# RNN核心计算
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h_next = torch.tanh(torch.mm(x, self.W_xh) +
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torch.mm(h_prev, self.W_hh) +
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self.b_h)
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return h_next
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# 示例：处理序列数据
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input_size = 3
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hidden_size = 4
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seq_length = 5
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# 创建RNN单元
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rnn_cell = SimpleRNNCell(input_size, hidden_size)
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# 初始化隐藏状态
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h = torch.zeros(1, hidden_size)
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# 模拟输入序列 (5个时间步，每个时间步3维向量)
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inputs = [torch.randn(1, input_size) for _ in range(seq_length)]
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# 循环处理序列
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hidden_states = []
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for t in range(seq_length):
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h = rnn_cell.forward(inputs[t], h)
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hidden_states.append(h.detach().numpy())
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print(f"时间步 {t + 1}, 隐藏状态: {h}")
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# 可视化隐藏状态变化
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plt.figure(figsize=(10, 6))
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for i in range(hidden_size):
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plt.plot(range(1, seq_length + 1), [h[0, i] for h in hidden_states],
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label=f'隐藏单元 {i + 1}')
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plt.title('RNN隐藏状态随时间变化')
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plt.xlabel('时间步')
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plt.ylabel('隐藏状态值')
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plt.legend()
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plt.grid(True)
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plt.show() |