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1. 3x3的时候会有重复
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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
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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
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2. 5x5的时候会有网格状重复
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3. 7x7的时候边框区域问题
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1. 输入的信噪比
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2. loss函数的设计
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3. grad信息的应用
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abs.cpu().detach().numpy()
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array([[8.1206687e-02, 2.2388995e-05, 3.7080176e-02, 5.7033218e-02,
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1.7404296e-03, 7.6270252e-02, 5.9453689e-02, 4.0801242e-05]],
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dtype=float32)
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ratio_nor.cpu().detach().numpy()
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array([[4.3121886e+00, 3.2778070e-07, 8.9907879e-01, 2.1270120e+00,
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1.9807382e-03, 3.8038602e+00, 2.3113825e+00, 1.0885816e-06]],
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dtype=float32)
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都比较差的时候区分不开
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## 发现的问题
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1. 3x3的时候会有重复
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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
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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
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2. 5x5的时候会有网格状重复
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3. 7x7的时候边框区域问题
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## 发现的原因
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1. 几个卷积核都是重复的,网格状的
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1. grad太大,learning rate 太大
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2. grad太小,训练的epoch不合适
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## 可能的策略
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1. 每个卷积核的改变权重(grad)能量守恒
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2. 卷积核的每个像素的权重都独立统计?
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1. 权重的reduce体现的是相互之间的可比性,关系
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3. 需要考虑梯度的绝对比值?
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@ -20,8 +20,8 @@ np.random.seed(seed)
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random.seed(seed)
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# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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device = torch.device("cpu")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# device = torch.device("cpu")
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# device = torch.device("mps")
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num_epochs = 1
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@ -110,11 +110,27 @@ model = ConvNet().to(device)
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model.train()
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# Train the model unsuper
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epochs = 2
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epochs = 3
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n_total_steps = len(train_loader)
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for epoch in range(epochs):
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for i, (images, labels) in enumerate(train_loader):
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images = images.to(device)
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# images = torch.ones((1, 1, 5, 5), device=device)
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# type = random.randint(0, 10)
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# if type == 0:
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# rand = random.randint(0, 4)
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# images[:, :, rand, :] = images[:, :, rand, :] * 0.5
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# if type == 1:
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# rand = random.randint(0, 4)
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# images[:, :, :, rand] = images[:, :, :, rand] * 0.5
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# if type == 2:
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# images[:, :, 0, 0] = images[:, :, 0, 0] * 0.5
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# images[:, :, 1, 1] = images[:, :, 1, 1] * 0.5
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# images[:, :, 2, 2] = images[:, :, 2, 2] * 0.5
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# images[:, :, 3, 3] = images[:, :, 3, 3] * 0.5
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# images[:, :, 4, 4] = images[:, :, 4, 4] * 0.5
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outputs = model.forward_unsuper(images)
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outputs = outputs.permute(0, 2, 3, 1) # 64 8 24 24 -> 64 24 24 8
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@ -137,7 +153,9 @@ for epoch in range(epochs):
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model.conv1.weight.grad = None
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loss.backward()
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model.conv1.weight.data = model.conv1.weight.data - model.conv1.weight.grad * 1000
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# if epoch >= (epochs - 1):
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# continue
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model.conv1.weight.data = model.conv1.weight.data - model.conv1.weight.grad * 0.001
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model.conv1.weight.data = model.normal_conv1_weight()
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if (i + 1) % 100 == 0:
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