使用Python建立RNN实现二进制加法的示例代码

2023年5月16日上午1:48 • 循环神经网络

下面是关于“使用Python建立RNN实现二进制加法的示例代码”的完整攻略。

解决方案

以下是使用Python建立RNN实现二进制加法的详细步骤：

步骤一：RNN介绍

循环神经网络（Recurrent Neural Network，RNN）是一种用于处理序列数据的神经网络。它可以通过对序列中的每个元素进行逐个处理，从而捕捉序列中的时间依赖关系。RNN在自然语言处理、语音识别、图像描述等领域中得到了广泛的应用。

步骤二：二进制加法的实现

以下是使用Python建立RNN实现二进制加法的示例代码：

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim

# 定义二进制加法的数据集
binary_dim = 8
largest_number = pow(2, binary_dim)
binary = np.unpackbits(np.array([range(largest_number)], dtype=np.uint8).T, axis=1)

# 定义输入数据和输出数据
input_data = torch.zeros((largest_number, binary_dim, 2))
output_data = torch.zeros((largest_number, binary_dim, 1))

# 将二进制数据转换为PyTorch张量
for i in range(largest_number):
    n1 = binary[i][::-1]
    n2 = binary[i][::-1]
    for j in range(binary_dim):
        input_data[i][j] = torch.tensor([n1[j], n2[j]])
        output_data[i][j] = torch.tensor([n1[j] ^ n2[j]])

# 定义RNN模型
class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
        self.i2o = nn.Linear(input_size + hidden_size, output_size)
        self.sigmoid = nn.Sigmoid()

    def forward(self, input, hidden):
        combined = torch.cat((input, hidden), 1)
        hidden = self.i2h(combined)
        output = self.i2o(combined)
        output = self.sigmoid(output)
        return output, hidden

    def init_hidden(self):
        return torch.zeros(1, self.hidden_size)

# 定义超参数
input_size = 2
hidden_size = 16
output_size = 1
learning_rate = 0.1

# 定义RNN模型和优化器
rnn = RNN(input_size, hidden_size, output_size)
optimizer = optim.SGD(rnn.parameters(), lr=learning_rate)
criterion = nn.BCELoss()

# 训练模型
for epoch in range(10000):
    for i in range(largest_number):
        hidden = rnn.init_hidden()
        optimizer.zero_grad()
        loss = 0

        for j in range(binary_dim):
            input = input_data[i][j].view(1, -1)
            output, hidden = rnn(input, hidden)
            loss += criterion(output, output_data[i][j])

        loss.backward()
        optimizer.step()

    if epoch % 1000 == 0:
        print('Epoch %d, Loss: %.4f' % (epoch, loss.item()))

# 测试模型
for i in range(largest_number):
    hidden = rnn.init_hidden()
    binary_a = binary[i][::-1]
    binary_b = binary[i][::-1]
    a = 0
    b = 0
    for j in range(binary_dim):
        a += binary_a[j] * pow(2, j)
        b += binary_b[j] * pow(2, j)
    c = a + b
    output = []
    for j in range(binary_dim):
        input = torch.tensor([binary_a[j], binary_b[j]]).view(1, -1)
        y_pred, hidden = rnn(input, hidden)
        output.append(int(np.round(y_pred.item())))
    output.reverse()
    output_num = 0
    for j in range(binary_dim):
        output_num += output[j] * pow(2, j)
    print('%d + %d = %d' % (a, b, output_num))