8.25 学习

import numpy as np
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader

class DiabetesDataset(Dataset):
    def __init__(self,filepath):
        xy = np.loadtxt(filepath,delimiter=',',dtype=np.float32)
        self.len = xy.shape[0]
        self.x_data = torch.from_numpy(xy[:,:-1])
        self.y_data = torch.from_numpy(xy[:,[-1]])

    def __getitem__(self, index):
        return self.x_data[index],self.y_data[index]

    def __len__(self):
        return self.len

dataset = DiabetesDataset('diabetes.csv.gz')
train_loader = DataLoader(dataset=dataset,batch_size=32,shuffle=True,num_workers=2)

# 模型构造
class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()#必要步骤，调用弗雷构造
        self.linear1 = torch.nn.Linear(8,6)
        self.linear2 = torch.nn.Linear(6,4)
        self.linear3 = torch.nn.Linear(4,1)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self,x):
        #这一步的输出作为下一步的输入
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        return x

model = Model() #实例化

# 损失函数与优化器
criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.parameters(),lr=0.01)


# Using DataLoader
for epoch in range(100):
    for i, data in enumerate(train_loader,0):
        # 1.Prepare data
        # 从data中提取数据和标签
        inputs, labels = data

        # 2.forward,计算预测值和损失值
        y_pred = model(inputs)
        loss = criterion(y_pred,labels)
        print(epoch,i,loss.item())

        # 3. Backward
        optimizer.zero_grad()
        loss.backward()

        #4.update
        optimizer.step()



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真的出现了，runtimeerror

import numpy as np
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader

class DiabetesDataset(Dataset):
    def __init__(self,filepath):
        xy = np.loadtxt(filepath,delimiter=',',dtype=np.float32)
        self.len = xy.shape[0]
        self.x_data = torch.from_numpy(xy[:,:-1])
        self.y_data = torch.from_numpy(xy[:,[-1]])

    def __getitem__(self, index):
        return self.x_data[index],self.y_data[index]

    def __len__(self):
        return self.len

dataset = DiabetesDataset('diabetes.csv.gz')
train_loader = DataLoader(dataset=dataset,batch_size=32,shuffle=True,num_workers=2)

# 模型构造
class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()#必要步骤，调用弗雷构造
        self.linear1 = torch.nn.Linear(8,6)
        self.linear2 = torch.nn.Linear(6,4)
        self.linear3 = torch.nn.Linear(4,1)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self,x):
        #这一步的输出作为下一步的输入
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        x = self.sigmoid(self.linear3(x))
        return x

model = Model() #实例化

# 损失函数与优化器
criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.parameters(),lr=0.01)

if __name__ == '__main__':
# Using DataLoader
    for epoch in range(100):
        for i, data in enumerate(train_loader,0):
            # 1.Prepare data
            # 从data中提取数据和标签
            inputs, labels = data

            # 2.forward,计算预测值和损失值
            y_pred = model(inputs)
            loss = criterion(y_pred,labels)
            print(epoch,i,loss.item())

            # 3. Backward
            optimizer.zero_grad()
            loss.backward()

            #4.update
            optimizer.step()



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import torch
criterion = torch.nn.CrossEntropyLoss()
Y = torch.LongTensor([2,0,1])

Y_pred1 = torch.Tensor([[0.1,0.2,0.9],
                       [1.1,0.1,0.2],
                       [0.2,2.1,0.1]])
Y_pred2 = torch.Tensor([[0.8,0.2,0.3],
                       [0.2,0.3,0.5],
                       [0.2,0.2,0.5]])
l1 = criterion(Y_pred1,Y)
l2 = criterion(Y_pred2,Y)
print("Batch Loss1 = ",l1.data,"\nBatch Loss2=",l2.data)
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和预测的结果一样，LOSS1是比较小的，因为算出来的比较吻合，损失较小
Y是预测值，Y_PRE是初始值

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import  DataLoader
import torch.nn.functional as F
import torch.optim as optim

batch_size = 64
# Convert the PIL Image to Tensor
# totensor，把输入的图像转换为张量
# normalize: mean 均值 std标准差，就是0.1307，0.3081
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,),(0.3081,))])

train_dataset = datasets.MNIST(root='../dataset/mnist/',train=True,download=True,transform=transform)
train_loader = DataLoader(train_dataset,shuffle=True,batch_size=batch_size)
test_dataset = datasets.MNIST(root='../dataset/mnist/',train=False,download=True,transform=transform)
test_loader = DataLoader(test_dataset,shuffle=False,batch_size=batch_size)

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.l1 = torch.nn.Linear(784,512)
        self.l2 = torch.nn.Linear(512,256)
        self.l3 = torch.nn.Linear(256,128)
        self.l4 = torch.nn.Linear(128, 64)
        self.l5 = torch.nn.Linear(64, 10)

    def forward(self,x):
        x = x.view(-1,784)
        x = F.relu(self.l1(x))
        x = F.relu(self.l2(x))
        x = F.relu(self.l3(x))
        x = F.relu(self.l4(x))
        return self.l5(x)

model = Net()

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),lr=0.01,momentum=0.5)

def train(epoch):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader,0):
        inputs,target = data
        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs,target)
        loss.backward()
        optimizer.step()

        # 累计的loss拿出来，取loss的时候要用item
        running_loss += loss.item()
        # 如果300次迭代就拿出来
        if batch_idx %300 == 299:
            print('[%d,%5d] loss: %.3f' % (epoch+1,batch_idx+1,running_loss/300))
            running_loss = 0.0

# test
def test():
    correct = 0
    total = 0
    # torch.no_grad这里面不会计算梯度
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            # 拿完数据做预测，拿下标
            outputs = model(images)
            # 沿着横去找最大值的下标
            _, predicted = torch.max(outputs.data,dim=1)
            # 加上总数，total就是batch_size
            total += labels.size(0)
            # 求和拿出来，我们猜对了多少个
            correct += (predicted == labels).sum().item()
    print('Accuray on test set: "%d %%' % (100*correct/total))

if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        if epoch%10 == 9:
            test()
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import torch
in_channels,out_channels = 5,10
width ,height = 100,100
kernel_size = 3 #卷积核的大小
batch_size = 1
# 在pytorch里面，所以输入的数据必须是小批量的数据

input = torch.randn(batch_size,in_channels,width,height)
# 大小，尺寸 3*3 或者5*3 都可以，一般来说是正方形
conv_layer = torch.nn.Conv2d(in_channels,out_channels,kernel_size=kernel_size)

#创建的卷积对象 conv_layer 把input送给他
output = conv_layer(input)

print(input.shape)
print(output.shape)
print(conv_layer.weight.shape)





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输入的图像5个通道，100*100
输出10个通道，98，98
10 输出的通道

import torch

# 输入的矩阵
input = [3,4,6,5,7,
         2,4,6,8,2,
         1,6,7,8,4,
         9,7,4,6,2,
         3,7,5,4,1]

# 输入=这个输入转化为1维的5，5
input = torch.Tensor(input).view(1,1,5,5)


conv_layer = torch.nn.Conv2d(1,1,kernel_size=3,padding=1,bias=False)

kernel = torch.Tensor([1,2,3,4,5,6,7,8,9]).view(1,1,3,3)
conv_layer.weight.data = kernel.data

output = conv_layer(input)
print(output)
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D:\soft\pycharm\pro\venv\Scripts\python.exe D:/soft/pycharm/pro/op/f6.py
tensor([[[[ 91., 168., 224., 215., 127.],
          [114., 211., 295., 262., 149.],
          [192., 259., 282., 214., 122.],
          [194., 251., 253., 169.,  86.],
          [ 96., 112., 110.,  68.,  31.]]]], grad_fn=<ConvolutionBackward0>)

进程已结束,退出代码0

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stride

import torch

# 输入的矩阵
input = [3,4,6,5,7,
         2,4,6,8,2,
         1,6,7,8,4,
         9,7,4,6,2,
         3,7,5,4,1]

# 输入=这个输入转化为1维的5，5
input = torch.Tensor(input).view(1,1,5,5)


conv_layer = torch.nn.Conv2d(1,1,kernel_size=3,stride=2,bias=False)

kernel = torch.Tensor([1,2,3,4,5,6,7,8,9]).view(1,1,3,3)
conv_layer.weight.data = kernel.data

output = conv_layer(input)
print(output)
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MaxPooling

import torch

input = [3,4,6,5,
         2,3,6,8,
         1,6,7,8,
         9,7,4,6]

input = torch.Tensor(input).view(1,1,4,4)

maxpooling_layer = torch.nn.MaxPool2d(kernel_size=2)

output = maxpooling_layer(input)
print(output)
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D:\soft\pycharm\pro\venv\Scripts\python.exe D:/soft/pycharm/pro/op/f8.py
tensor([[[[4., 8.],
          [9., 8.]]]])

进程已结束,退出代码0

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