Create Real-Time Data

Real-Time Data

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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()