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import torch
import torch.nn as nn
import torch.optim as optim
class RealTimeModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(RealTimeModel, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
return x
model = RealTimeModel(input_size=10, hidden_size=20, output_size=1)
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
import numpy as np
import time
def get_new_data():
return torch.tensor(np.random.rand(10), dtype=torch.float32)
def real_time_update():
while True:
new_data = get_new_data().unsqueeze(0)
target = torch.tensor([0.5], dtype=torch.float32)
output = model(new_data)
loss = criterion(output, target)
optimizer.zero_grad()
loss.backward()
optimzier.step()
print(f"Real-Time Update - Loss: {loss.item():.4f}")
time.sleep(1)
import matplotlib.pyplot as plt
def visualize_loss(loss_values):
plt.plot(loss_values)
plt.xlabel("Time")
plt.ylabel("Loss")
plt.show()
import numpy as np
import matplotlib.pyplot as plt
import torch
import time
def get_new_data():
return torch.sin(torch.linspace(0,2 * np.p, 100) + time.time()).numpy()
plt.ion()
fig, ax = plt.subplots()
x_data = np.linspace(0, 2 * np.pi, 100)
y_data = get_new_data()
line, = ax.plot(x_data, y_data)
def real_time_plot():
while True:
new_y_data = get_new_data()
line.set_ydata(new_y_data)
fig.canvas.draw()
fig.canvas.flush_events()
time.sleep(0.1)
try:
real_time_plot()
except KeyboardInterrupt:
print("Real-time plotting stopped.")
finally:
plt.ioff()
plt.show() |