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Colin 2024-10-31 15:15:13 +08:00
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36
unsuper/Readme.md
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@ -1,9 +1,35 @@
1. 3x3的时候会有重复
1. 重复的权重，虽然权重看起来都一样，但是有稍微的不同，不是完全一样
2. 3x3太小了导致了样本的信噪比太低，大部分的样本切出来都是0
2. 5x5的时候会有网格状重复
3. 7x7的时候边框区域问题
1. 输入的信噪比 1. 输入的信噪比
2. loss函数的设计 2. loss函数的设计
3. grad信息的应用 3. grad信息的应用
abs.cpu().detach().numpy()
array([[8.1206687e-02, 2.2388995e-05, 3.7080176e-02, 5.7033218e-02,
1.7404296e-03, 7.6270252e-02, 5.9453689e-02, 4.0801242e-05]],
dtype=float32)
ratio_nor.cpu().detach().numpy()
array([[4.3121886e+00, 3.2778070e-07, 8.9907879e-01, 2.1270120e+00,
1.9807382e-03, 3.8038602e+00, 2.3113825e+00, 1.0885816e-06]],
dtype=float32)
都比较差的时候区分不开
## 发现的问题
1. 3x3的时候会有重复
1. 重复的权重，虽然权重看起来都一样，但是有稍微的不同，不是完全一样
2. 3x3太小了导致了样本的信噪比太低，大部分的样本切出来都是0
2. 5x5的时候会有网格状重复
3. 7x7的时候边框区域问题
## 发现的原因
1. 几个卷积核都是重复的，网格状的
1. grad太大，learning rate 太大
2. grad太小，训练的epoch不合适
## 可能的策略
1. 每个卷积核的改变权重(grad)能量守恒
2. 卷积核的每个像素的权重都独立统计？
1. 权重的reduce体现的是相互之间的可比性，关系
3. 需要考虑梯度的绝对比值？

26
unsuper/minist.py
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@ -20,8 +20,8 @@ np.random.seed(seed)
random.seed(seed) random.seed(seed)
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = torch.device("cpu") # device = torch.device("cpu")
# device = torch.device("mps") # device = torch.device("mps")
num_epochs = 1 num_epochs = 1
@ -110,11 +110,27 @@ model = ConvNet().to(device)
model.train() model.train()
# Train the model unsuper # Train the model unsuper
epochs = 2 epochs = 3
n_total_steps = len(train_loader) n_total_steps = len(train_loader)
for epoch in range(epochs): for epoch in range(epochs):
for i, (images, labels) in enumerate(train_loader): for i, (images, labels) in enumerate(train_loader):
images = images.to(device) images = images.to(device)
# images = torch.ones((1, 1, 5, 5), device=device)
# type = random.randint(0, 10)
# if type == 0:
# rand = random.randint(0, 4)
# images[:, :, rand, :] = images[:, :, rand, :] * 0.5
# if type == 1:
# rand = random.randint(0, 4)
# images[:, :, :, rand] = images[:, :, :, rand] * 0.5
# if type == 2:
# images[:, :, 0, 0] = images[:, :, 0, 0] * 0.5
# images[:, :, 1, 1] = images[:, :, 1, 1] * 0.5
# images[:, :, 2, 2] = images[:, :, 2, 2] * 0.5
# images[:, :, 3, 3] = images[:, :, 3, 3] * 0.5
# images[:, :, 4, 4] = images[:, :, 4, 4] * 0.5
outputs = model.forward_unsuper(images) outputs = model.forward_unsuper(images)
outputs = outputs.permute(0, 2, 3, 1) # 64 8 24 24 -> 64 24 24 8 outputs = outputs.permute(0, 2, 3, 1) # 64 8 24 24 -> 64 24 24 8
@ -137,7 +153,9 @@ for epoch in range(epochs):
model.conv1.weight.grad = None model.conv1.weight.grad = None
loss.backward() loss.backward()
model.conv1.weight.data = model.conv1.weight.data - model.conv1.weight.grad * 1000 # if epoch >= (epochs - 1):
# continue
model.conv1.weight.data = model.conv1.weight.data - model.conv1.weight.grad * 0.001
model.conv1.weight.data = model.normal_conv1_weight() model.conv1.weight.data = model.normal_conv1_weight()
if (i + 1) % 100 == 0: if (i + 1) % 100 == 0: