小编Urv*_*esh的帖子

我正在使用 pytorch 来训练我的 CNN 网络。我想绘制训练和验证损失曲线以可视化模型性能。如何绘制两条曲线？

我有下面的代码

# create a function (this my favorite choice)
def RMSELoss(predicted,target):
    return torch.sqrt(torch.mean((predicted-target)**2))

criterion = RMSELoss

# loss = torch.sqrt(criterion(x, y))
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
epochs = 300

n_total_steps = len(train_dataset)

trainingEpoch_loss = []
validationEpoch_loss = []

for epoch in range(epochs):
    step_loss = []
    model.train()
    for i, data in enumerate(train_dataset):
        feature,target = data['data'].type(torch.FloatTensor),torch.tensor(data['target']).type(torch.FloatTensor)
         
        # Clear the gradients
        optimizer.zero_grad()
        # Forward Pass
        outputs = model(feature)
        # Find the Loss
        training_loss = criterion(outputs, target)
        # Calculate gradients …

我有两个清单。第一个列表包含 X 坐标值，第二个列表包含 Y 坐标值。使用这两个列表，我想创建一个按第一个元素排序的元组。

X coordinate = [2, 3, 4, 4, 3, 2, 1, 0, 0, 1, 1, 1, 1, 2, 2, 2]
Y coordinate = [3, 3, 3, 4, 4, 4, 4, 4, 3, 3, 2, 1, 0, 0, 1, 2]

我希望我的输出是这样的：

[(0, 3), (0, 4), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (3, 3), (3, 4), (4, 3), (4, 4)]

为了实现这个结果，我编写了下面的代码并得到了下面提到的输出。

merged_list = list(tuple(zip(X3_coordinate, …