我有以下工作代码。 我想在新行上打印所有第 5000 个项目的每一项损失 (% 5000) 但进度条应该继续 它的方式一劳永逸(应该只打印一次)并显示总进度。我该如何修改代码?
import torch
from math import tanh,cos
from tqdm import tqdm
from time import sleep
batch, dim_in, dim_h, dim_out = 1, 100, 10, 1
input_X = torch.randn(batch, dim_in)
output_Y = torch.randn(batch, dim_out)
SGD_model = torch.nn.Sequential(
torch.nn.Linear(dim_in, dim_h),
torch.nn.Tanh(),
torch.nn.Linear(dim_h, dim_out),
)
loss_fn = torch.nn.MSELoss(reduction='sum')
rate_learning = 0.01
optim = torch.optim.SGD(SGD_model.parameters(), lr=rate_learning, momentum=0.01)
for values in tqdm(range(1000)):
pred_y = SGD_model(input_X)
loss = loss_fn(pred_y, output_Y)
if values % 100 == 0:
print(values, loss.item())
optim.zero_grad()
loss.backward()
optim.step()
三个进度条而不是一个:
chatGPT 4 为我提供了这段代码,但仍然有 4 个进度条:
import torch
from math import tanh
from time import sleep
batch, dim_in, dim_h, dim_out = 32, 10, 5, 1
input_X = torch.randn(batch, dim_in)
output_Y = torch.randn(batch, dim_out)
SGD_model = torch.nn.Sequential(
torch.nn.Linear(dim_in, dim_h),
torch.nn.Tanh(),
torch.nn.Linear(dim_h, dim_out),
)
loss_fn = torch.nn.MSELoss(reduction='sum')
rate_learning = 0.0001
optim = torch.optim.SGD(SGD_model.parameters(), lr=rate_learning, momentum=0.4)
# Define the training loop with a range
epochs = 1000
for epoch in range(epochs):
progress = (epoch + 1) / epochs
bar_length = 50
block = int(round(bar_length * progress))
text = f"Epoch {epoch+1}/{epochs} [{'#' * block + '-' * (bar_length - block)}] {100 * progress:.2f}%"
pred_y = SGD_model(input_X)
loss = loss_fn(pred_y, output_Y)
if epoch % 100 == 0:
text += f', Loss: {loss.item()}'
print(text, end='\r')
optim.zero_grad()
loss.backward()
optim.step()
print("\nWeights:", SGD_model[0].weight)
print("Bias:", SGD_model[0].bias)
print("Bias:", SGD_model[2].bias)