用Pytorch实现XOR

3xiyfsfu 于 12个月前发布在其他

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我想实现XOR逻辑运算符。我没有得到最佳答案。我需要三个输出列用于下一个操作。我有一个data.csv文件作为数据文件（XOR）：

in1,in2,in3,in4,out1,out2,out3
0,0,0,0,0,0,0
0,0,0,1,0,0,1
0,0,1,0,0,0,1
0,0,1,1,0,0,0
0,1,0,0,0,0,1
0,1,0,1,0,0,0
0,1,1,0,0,0,0
1,0,0,0,0,0,1
1,0,0,1,0,0,0
1,0,1,0,0,0,0
1,0,1,1,0,0,1
1,1,0,0,0,0,0
1,1,0,1,0,0,1
1,1,1,0,0,0,1

字符串
Python代码是：

import torch 
import torch.nn as nn
import pandas as pd
import numpy as np

# Defining input size, hidden layer size, output size and batch size respectively
n_in, n_h, n_out = 4, 5, 3

# Create dummy input and target tensors (data)
df = pd.read_csv('data.csv')

input_cols = ['in1', 'in2', 'in3', 'in4'] 
output_cols = ['out1', 'out2', 'out3']

input_np_array = df[input_cols].to_numpy()
target_np_array = df[output_cols].to_numpy()

inputs = torch.tensor(input_np_array, dtype=torch.float32)
targets = torch.tensor(target_np_array, dtype=torch.float32)

model = nn.Sequential(
    nn.Linear(n_in, n_h),
    nn.Sigmoid(),
    nn.Linear(n_h, n_out),
    nn.Sigmoid())

# Construct the loss function
criterion = torch.nn.MSELoss()
# Construct the optimizer (Stochastic Gradient Descent in this case)
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)

# Gradient Descent
for epoch in range(500000):
   # Forward pass: Compute predicted y by passing x to the model
   y_pred = model(inputs)

   # Compute and print loss
   loss = criterion(y_pred, targets)
   if epoch % 100==0:
     print('epoch: ', epoch,' loss: ', loss.item())

   # Zero gradients, perform a backward pass, and update the weights.
   optimizer.zero_grad()

   # perform a backward pass (backpropagation)
   loss.backward()

   # Update the parameters
   optimizer.step()

test_data = torch.tensor([1,1,1,1], dtype=torch.float32) # 0,0,0
output = model(test_data)
probabilities = torch.nn.functional.sigmoid(output)
# probabilities = torch.nn.functional.softmax(output, dim=0)
print(probabilities)

test_data = torch.tensor([0,1,1,1], dtype=torch.float32) # 0,0,1
output = model(test_data)
probabilities = torch.nn.functional.sigmoid(output)
print(probabilities)

型
输出为：

tensor([0.5058, 0.5060, 0.6247], grad_fn=<SigmoidBackward0>)
tensor([0.5052, 0.5058, 0.6214], grad_fn=<SigmoidBackward0>)

型
为什么我得不到最佳答案？

pytorch

来源：https://stackoverflow.com/questions/77474965/implement-xor-with-pytorch

1条答案

按热度按时间

p8h8hvxi1#

你可以这样修改你的代码：

import torch 
import torch.nn as nn
import pandas as pd

# Defining input size, hidden layer size, output size, and batch size respectively
n_in, n_h, n_out = 4, 5, 3

# Create dummy input and target tensors (data)
df = pd.read_csv('data.csv')

input_cols = ['in1', 'in2', 'in3', 'in4'] 
output_cols = ['out1', 'out2', 'out3']

input_np_array = df[input_cols].to_numpy()
target_np_array = df[output_cols].to_numpy()

inputs = torch.tensor(input_np_array, dtype=torch.float32)
targets = torch.tensor(target_np_array, dtype=torch.float32)

model = nn.Sequential(
    nn.Linear(n_in, n_h),
    nn.Sigmoid(),
    nn.Linear(n_h, n_out),
    nn.Sigmoid()  # Using Sigmoid activation for the output layer
)

# Construct the loss function
criterion = torch.nn.BCELoss()  # Binary Cross Entropy Loss for binary classification
# Construct the optimizer (Stochastic Gradient Descent in this case)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# Gradient Descent
for epoch in range(500000):
    # Forward pass: Compute predicted y by passing x to the model
    y_pred = model(inputs)

    # Compute and print loss
    loss = criterion(y_pred, targets)
    if epoch % 100 == 0:
        print('epoch: ', epoch, ' loss: ', loss.item())

    # Zero gradients, perform a backward pass, and update the weights.
    optimizer.zero_grad()

    # perform a backward pass (backpropagation)
    loss.backward()

    # Update the parameters
    optimizer.step()

# Testing
with torch.no_grad():
    test_data = torch.tensor([1, 1, 1, 1], dtype=torch.float32)
    output = model(test_data)
    probabilities = (output >= 0.5).float()
    print(probabilities)

    test_data = torch.tensor([0, 1, 1, 1], dtype=torch.float32)
    output = model(test_data)
    probabilities = (output >= 0.5).float()
    print(probabilities)

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用Pytorch实现XOR

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