human
Write me a the Python program you requested given the following:
I am working on the file "Equation_Notes2.ipynb".
The first task is:
Move to GPU
The main idea behind PyTorch’s GPU handling is that if an object (tensor, Variable, Parameter) is available on a device with id 0 , then after performing some operation (transformation) on it, the resultant object will also be stored on the same device 0. Further, operations cannot be performed on two objects residing on different devices, i.e., no a+b is allowed if a is on CPU and b is on GPU.
So, in order to run our training on GPU, it is enough to copy our model parameters and our input data on the GPU. This is done using .cuda() attribute available for all tensors and modules. For tensor, it is straightforward that this will copy the tensor to GPU, but for module (model), calling .cuda() will recursively copy all child modules and parameters to GPU.
We can verify if torch.cuda.is_available() returns True to check if a GPU is available on a machine.
cuda(device=None)
Access individual layer weights and gradients
Can you write Python program for it?
assistant
#Here is the Python program you requested.

import torch

import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt

x = torch.randn(1, 2).cuda()
y = torch.randn(1, 1).cuda()

class net(torch.nn.Module):
  def __init__(self):
    super(net,self).__init__()
    self.linear1=torch.nn.Linear(2,1)
    self.linear2=torch.nn.Linear(1,2)
    
  def forward(self,x):
    h_relu = torch.nn.functional.relu(self.linear1(x))
    y_pred = self.linear2(h_relu)
    return y_pred

mymodel = net()
  
if torch.cuda.is_available():
  mymodel.cuda()

optimizer = torch.optim.SGD(mymodel.parameters(), lr=0.0009, momentum=0.9)

# Before training
print("predict (before training)", mymodel(x).data[0])

loss_col = []
epochs = 5
for epoch in range(epochs):
  optimizer.zero_grad()
  model_output = mymodel(x)
  loss = torch.nn.functional.mse_loss(model_output, y) # direct use of mse_loss from nn.functional
  loss_col.append(loss)
  #print(epoch, "{:.20f}".format(loss.item()))
  loss.backward()
  optimizer.step()
  print("progress:", epoch, loss.data.item())

plt.plot(loss_col)
plt.show()  
  
#print(list(mymodel.parameters()))

# After training
hour_var = torch.Tensor([1.0,4.0]).cuda()
y_pred = mymodel(hour_var)
print("predict (after training)", y_pred.data)