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In [59]:
import jovian
jovian.commit()
[jovian] Saving notebook..
[jovian] Updating notebook "3f155d72b27a4d97adbfd7f9223310a4" on https://jvn.io [jovian] Uploading notebook.. [jovian] Capturing environment.. [jovian] Committed successfully! https://jvn.io/maxliubl/3f155d72b27a4d97adbfd7f9223310a4
In [113]:
import numpy as np
import pandas as pd
import torch
In [114]:
# make zhe data
inputes = np.array([[73,67, 43],
             [91, 88, 64],
             [87, 134, 58],
             [102, 43, 37],
             [69, 96, 70]],dtype="float32"
             )
targets = np.array([[56, 70],
             [81, 101],
             [119, 133],
             [22, 37],
             [103, 119]],dtype="float32"
                  )
In [115]:
inputes = torch.from_numpy(inputes)
targets = torch.from_numpy(targets)
In [116]:
# initialize the weight and biase
# w = np.random.randn(2,3)
# b = np.random.randn(5,2)
w = torch.randn(2,3, requires_grad=True)
b = torch.randn(2, requires_grad=True)
In [117]:
#define the model
def model(x):
    y_hat = x @ w.t() + b
    return y_hat
In [118]:
predict = model(inputes)
print(predict)
print(targets)
e = predict - targets
tensor([[117.2352, 32.2944], [159.4428, 38.6207], [143.2056, 103.5003], [137.6121, -2.1399], [146.9477, 50.5046]], grad_fn=<AddBackward0>) tensor([[ 56., 70.], [ 81., 101.], [119., 133.], [ 22., 37.], [103., 119.]])
In [ ]:
# 发现差距很大,因为一开始我们用的是随机数值,所以数据合不上很正常。

# 接下来我们需要通过损失函数,来评估我们的模型和实际差距多大。分为3个步骤

#     计算两个矩阵(preds和targets)之间的差异

#     平方差矩阵的所有元素以消除负值

#     计算结果矩阵中元素的平均值

# 最终结果为均方误差MSE
In [119]:
def MSE(predict,targets):
    error = predict - targets
    b = torch.sum(error * error )
    return b / torch.numel(predit)
In [120]:
MSE(predict, targets)
Out[120]:
tensor(3819.3164, grad_fn=<DivBackward0>)
In [121]:
# 定义 均方误差
def mse(t1, t2):
    diff = t1 -t2
    return torch.sum(diff*diff)/torch.numel(diff)
In [122]:
mse(predict, targets)
loss = mse(predict, targets)
In [123]:
# 计算梯度
loss.backward()
In [125]:
print(w)
print(w.grad)
tensor([[ 0.8973, -0.1001, 1.3409], [-0.2763, 1.2885, -0.7504]], requires_grad=True) tensor([[ 5707.8364, 4687.9160, 3282.2734], [-3942.7888, -4045.4365, -2713.4897]])
In [128]:
# 重置梯度
w.grad.zero_()
b.grad.zero_()
print(w.grad)
print(b.grad)
tensor([[0., 0., 0.], [0., 0., 0.]]) tensor([0., 0.])
In [130]:
# 使用梯度下降调整重量和偏差

# 我们将使用梯度下降优化算法减少损失并改进我们的模型,该算法具有以下步骤:

#     生成预测

#     计算损失

#     计算梯度w.r.t权重和偏差

#     通过减去与梯度成比例的小量来调整权重

#     将渐变重置为零

# 让我们逐步实现上述步骤

lr = 1e-5

predict = model(inputes)
loss = MSE(predict, targets)
loss.backward()

with torch.no_grad():
    w -= w.grad * lr
    b -= b.grad * lr

    w.grad.zero_()
    b.grad.zero_()
In [132]:
print(w,"\n",b)
tensor([[ 0.8402, -0.1469, 1.3080], [-0.2369, 1.3290, -0.7233]], requires_grad=True) tensor([ 0.7814, -1.5972], requires_grad=True)
In [133]:
predict = model(inputes)
loss = MSE(predict, targets)
print(loss)
tensor(2866.0225, grad_fn=<DivBackward0>)
In [147]:
epoch = 100000
for i in range(epoch):
    predict = model(inputes)
    loss = MSE(predict, targets)
    loss.backward()
    with torch.no_grad():
        w -= w.grad * lr
        b -= b.grad * lr
        w.grad.zero_()
        b.grad.zero_()
    
In [148]:
predict = model(inputes)
loss = predict - targets
print(loss)
tensor([[ 1.2902, 0.0433], [ 1.1286, -0.1632], [-0.3276, 0.0019], [-0.9343, 0.0488], [-1.0593, 0.0951]], grad_fn=<SubBackward0>)
In [149]:
predict
Out[149]:
tensor([[ 57.2902,  70.0433],
        [ 82.1286, 100.8368],
        [118.6724, 133.0019],
        [ 21.0657,  37.0488],
        [101.9407, 119.0951]], grad_fn=<AddBackward0>)
In [150]:
targets
Out[150]:
tensor([[ 56.,  70.],
        [ 81., 101.],
        [119., 133.],
        [ 22.,  37.],
        [103., 119.]])
In [ ]:
 
In [156]:
# 用PyTorch内置函数实现线性回归
# 了解了上述原理后,我们就可以用PyTorch内置的函数,简化我们的工作量。

# 接下来我们创建一个TensorDataset和一个DataLoader:
# Define tensordatase
from torch.utils.data import TensorDataset
train_ds = TensorDataset(inputes, targets)
print(train_ds[0:3])
print(train_ds[0:1])

(tensor([[ 73., 67., 43.], [ 91., 88., 64.], [ 87., 134., 58.]]), tensor([[ 56., 70.], [ 81., 101.], [119., 133.]])) (tensor([[73., 67., 43.]]), tensor([[56., 70.]]))
In [170]:
# Define DataLoader
from torch.utils.data import DataLoader
batch_size = 5
train_dl = DataLoader(train_ds, batch_size, shuffle=True)
In [171]:
for dx, dy in train_dl:
    print(dx)
    print(dy)
    break
tensor([[ 87., 134., 58.], [ 73., 67., 43.], [102., 43., 37.], [ 69., 96., 70.], [ 91., 88., 64.]]) tensor([[119., 133.], [ 56., 70.], [ 22., 37.], [103., 119.], [ 81., 101.]])
In [195]:
# 用nn.linear自动初始化

# 刚开始我们是手动随机输入的初识权重。现在我们可以使用nn.linear自动完成初始化工作。

import torch.nn as nn
model = nn.Linear(3,2)
predict = model(inputes)

import torch.nn.functional as F
loss_fc = F.mse_loss
loss = loss_fc(model(inputes),targets)
loss.backward()

# import torch.optim as opt
opt  = torch.optim.SGD(model.parameters(), lr = 1e-5)

In [198]:
# Utility function to train the model
def fit(num_epochs, model, loss_fn, opt):
    # repeat for given number of epochs
    for epoch in range(num_epochs):
        
        # Train with batches of data
        for dx, dy in train_dl:
            
            # 1. Genrate predictons
            predict = model(dx)
            
            # 2. caclculate loss 
            loss = loss_fc(predict,dy)
            
            # 3. computer gradients
            loss.backward()
            
            # 4. Update parameters using gradients
            opt.step()
            
            # 5. Reset the gradients to zero
            opt.zero_grad()
        if (epoch +1) % 10 == 0: 
            print("Epoch [{}/{}], Loss :{:.4f}".format(epoch+1, num_epochs, loss.item()))
In [201]:
fit(1000000, model, loss_fc, opt)
Epoch [10/1000000], Loss :0.5312 Epoch [20/1000000], Loss :0.5312 Epoch [30/1000000], Loss :0.5312 Epoch [40/1000000], Loss :0.5312 Epoch [50/1000000], Loss :0.5312 Epoch [60/1000000], Loss :0.5312 Epoch [70/1000000], Loss :0.5312 Epoch [80/1000000], Loss :0.5312 Epoch [90/1000000], Loss :0.5312 Epoch [100/1000000], Loss :0.5312 Epoch [110/1000000], Loss :0.5312 Epoch [120/1000000], Loss :0.5312 Epoch [130/1000000], Loss :0.5312 Epoch [140/1000000], Loss :0.5312 Epoch [150/1000000], Loss :0.5312 Epoch [160/1000000], Loss :0.5312 Epoch [170/1000000], Loss :0.5312 Epoch [180/1000000], Loss :0.5312 Epoch [190/1000000], Loss :0.5312 Epoch [200/1000000], Loss :0.5312 Epoch [210/1000000], Loss :0.5312 Epoch [220/1000000], Loss :0.5312 Epoch [230/1000000], Loss :0.5312 Epoch [240/1000000], Loss :0.5312 Epoch [250/1000000], Loss :0.5312 Epoch [260/1000000], Loss :0.5312 Epoch [270/1000000], Loss :0.5312 Epoch [280/1000000], Loss :0.5312 Epoch [290/1000000], Loss :0.5312 Epoch [300/1000000], Loss :0.5312 Epoch [310/1000000], Loss :0.5312 Epoch [320/1000000], Loss :0.5312 Epoch [330/1000000], Loss :0.5312 Epoch [340/1000000], Loss :0.5312 Epoch [350/1000000], Loss :0.5312 Epoch [360/1000000], Loss :0.5312 Epoch [370/1000000], Loss :0.5312 Epoch [380/1000000], Loss :0.5312 Epoch [390/1000000], Loss :0.5312 Epoch [400/1000000], Loss :0.5312 Epoch [410/1000000], Loss :0.5312 Epoch [420/1000000], Loss :0.5312 Epoch [430/1000000], Loss :0.5312 Epoch [440/1000000], Loss :0.5312 Epoch [450/1000000], Loss :0.5312 Epoch [460/1000000], Loss :0.5312 Epoch [470/1000000], Loss :0.5312 Epoch [480/1000000], Loss :0.5312 Epoch [490/1000000], Loss :0.5312 Epoch [500/1000000], Loss :0.5312 Epoch [510/1000000], Loss :0.5312 Epoch [520/1000000], Loss :0.5312 Epoch [530/1000000], Loss :0.5312 Epoch [540/1000000], Loss :0.5312 Epoch [550/1000000], Loss :0.5312 Epoch [560/1000000], Loss :0.5312 Epoch [570/1000000], Loss :0.5312 Epoch [580/1000000], Loss :0.5312 Epoch [590/1000000], Loss :0.5312 Epoch [600/1000000], Loss :0.5312 Epoch [610/1000000], Loss :0.5312 Epoch [620/1000000], Loss :0.5312 Epoch [630/1000000], Loss :0.5312 Epoch [640/1000000], Loss :0.5312 Epoch [650/1000000], Loss :0.5312 Epoch [660/1000000], Loss :0.5312 Epoch [670/1000000], Loss :0.5312 Epoch [680/1000000], Loss :0.5312 Epoch [690/1000000], Loss :0.5312 Epoch [700/1000000], Loss :0.5312 Epoch [710/1000000], Loss :0.5312 Epoch [720/1000000], Loss :0.5312 Epoch [730/1000000], Loss :0.5312 Epoch [740/1000000], Loss :0.5312 Epoch [750/1000000], Loss :0.5312 Epoch [760/1000000], Loss :0.5312 Epoch [770/1000000], Loss :0.5312 Epoch [780/1000000], Loss :0.5312 Epoch [790/1000000], Loss :0.5312 Epoch [800/1000000], Loss :0.5312 Epoch [810/1000000], Loss :0.5312 Epoch [820/1000000], Loss :0.5312 Epoch [830/1000000], Loss :0.5312 Epoch [840/1000000], Loss :0.5312 Epoch [850/1000000], Loss :0.5312 Epoch [860/1000000], Loss :0.5312 Epoch [870/1000000], Loss :0.5312 Epoch [880/1000000], Loss :0.5312 Epoch 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Epoch [106750/1000000], Loss :0.5304 Epoch [106760/1000000], Loss :0.5304 Epoch [106770/1000000], Loss :0.5304 Epoch [106780/1000000], Loss :0.5304 Epoch [106790/1000000], Loss :0.5304 Epoch [106800/1000000], Loss :0.5304 Epoch [106810/1000000], Loss :0.5304 Epoch [106820/1000000], Loss :0.5304 Epoch [106830/1000000], Loss :0.5304 Epoch [106840/1000000], Loss :0.5304 Epoch [106850/1000000], Loss :0.5304 Epoch [106860/1000000], Loss :0.5304 Epoch [106870/1000000], Loss :0.5304 Epoch [106880/1000000], Loss :0.5304 Epoch [106890/1000000], Loss :0.5304 Epoch [106900/1000000], Loss :0.5304 Epoch [106910/1000000], Loss :0.5304 Epoch [106920/1000000], Loss :0.5304 Epoch [106930/1000000], Loss :0.5304 Epoch [106940/1000000], Loss :0.5304 Epoch [106950/1000000], Loss :0.5304 Epoch [106960/1000000], Loss :0.5304 Epoch [106970/1000000], Loss :0.5304 Epoch [106980/1000000], Loss :0.5304 Epoch [106990/1000000], Loss :0.5304 Epoch [107000/1000000], Loss :0.5304 Epoch [107010/1000000], Loss :0.5304 Epoch [107020/1000000], Loss :0.5304 Epoch [107030/1000000], Loss :0.5304 Epoch [107040/1000000], Loss :0.5304 Epoch [107050/1000000], Loss :0.5304 Epoch [107060/1000000], Loss :0.5304 Epoch [107070/1000000], Loss :0.5304 Epoch [107080/1000000], Loss :0.5304 Epoch [107090/1000000], Loss :0.5304 Epoch [107100/1000000], Loss :0.5304 Epoch [107110/1000000], Loss :0.5304 Epoch [107120/1000000], Loss :0.5304 Epoch [107130/1000000], Loss :0.5304 Epoch [107140/1000000], Loss :0.5304 Epoch [107150/1000000], Loss :0.5304 Epoch [107160/1000000], Loss :0.5304 Epoch [107170/1000000], Loss :0.5304 Epoch [107180/1000000], Loss :0.5304 Epoch [107190/1000000], Loss :0.5304 Epoch [107200/1000000], Loss :0.5304 Epoch [107210/1000000], Loss :0.5304
Epoch [107220/1000000], Loss :0.5304 Epoch [107230/1000000], Loss :0.5304 Epoch [107240/1000000], Loss :0.5304 Epoch [107250/1000000], Loss :0.5304 Epoch [107260/1000000], Loss :0.5304 Epoch [107270/1000000], Loss :0.5304 Epoch [107280/1000000], Loss :0.5304 Epoch [107290/1000000], Loss :0.5304 Epoch [107300/1000000], Loss :0.5304 Epoch [107310/1000000], Loss :0.5304 Epoch [107320/1000000], Loss :0.5304 Epoch [107330/1000000], Loss :0.5304 Epoch [107340/1000000], Loss :0.5304 Epoch [107350/1000000], Loss :0.5304 Epoch [107360/1000000], Loss :0.5304 Epoch [107370/1000000], Loss :0.5304 Epoch [107380/1000000], Loss :0.5304 Epoch [107390/1000000], Loss :0.5304 Epoch [107400/1000000], Loss :0.5304 Epoch [107410/1000000], Loss :0.5304 Epoch [107420/1000000], Loss :0.5304 Epoch [107430/1000000], Loss :0.5304 Epoch [107440/1000000], Loss :0.5304 Epoch [107450/1000000], Loss :0.5304 Epoch [107460/1000000], Loss :0.5304 Epoch [107470/1000000], Loss :0.5304 Epoch [107480/1000000], Loss :0.5304 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Epoch [109650/1000000], Loss :0.5304 Epoch [109660/1000000], Loss :0.5304 Epoch [109670/1000000], Loss :0.5304 Epoch [109680/1000000], Loss :0.5304 Epoch [109690/1000000], Loss :0.5304 Epoch [109700/1000000], Loss :0.5304 Epoch [109710/1000000], Loss :0.5304 Epoch [109720/1000000], Loss :0.5304 Epoch [109730/1000000], Loss :0.5304 Epoch [109740/1000000], Loss :0.5304 Epoch [109750/1000000], Loss :0.5304 Epoch [109760/1000000], Loss :0.5304 Epoch [109770/1000000], Loss :0.5304 Epoch [109780/1000000], Loss :0.5304 Epoch [109790/1000000], Loss :0.5304 Epoch [109800/1000000], Loss :0.5304 Epoch [109810/1000000], Loss :0.5304 Epoch [109820/1000000], Loss :0.5304 Epoch [109830/1000000], Loss :0.5304 Epoch [109840/1000000], Loss :0.5304 Epoch [109850/1000000], Loss :0.5304 Epoch [109860/1000000], Loss :0.5304 Epoch [109870/1000000], Loss :0.5304 Epoch [109880/1000000], Loss :0.5304 Epoch [109890/1000000], Loss :0.5304 Epoch [109900/1000000], Loss :0.5304 Epoch [109910/1000000], Loss :0.5304 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Epoch [111270/1000000], Loss :0.5303 Epoch [111280/1000000], Loss :0.5304 Epoch [111290/1000000], Loss :0.5303 Epoch [111300/1000000], Loss :0.5303 Epoch [111310/1000000], Loss :0.5303 Epoch [111320/1000000], Loss :0.5304 Epoch [111330/1000000], Loss :0.5304 Epoch [111340/1000000], Loss :0.5304 Epoch [111350/1000000], Loss :0.5304 Epoch [111360/1000000], Loss :0.5304 Epoch [111370/1000000], Loss :0.5303 Epoch [111380/1000000], Loss :0.5303 Epoch [111390/1000000], Loss :0.5303 Epoch [111400/1000000], Loss :0.5304 Epoch [111410/1000000], Loss :0.5304 Epoch [111420/1000000], Loss :0.5304 Epoch [111430/1000000], Loss :0.5304 Epoch [111440/1000000], Loss :0.5304 Epoch [111450/1000000], Loss :0.5303 Epoch [111460/1000000], Loss :0.5303 Epoch [111470/1000000], Loss :0.5304 Epoch [111480/1000000], Loss :0.5303 Epoch [111490/1000000], Loss :0.5304 Epoch [111500/1000000], Loss :0.5304 Epoch [111510/1000000], Loss :0.5304 Epoch [111520/1000000], Loss :0.5303 Epoch [111530/1000000], Loss :0.5303 Epoch [111540/1000000], Loss :0.5303 Epoch [111550/1000000], Loss :0.5303 Epoch [111560/1000000], Loss :0.5303 Epoch [111570/1000000], Loss :0.5303 Epoch [111580/1000000], Loss :0.5303 Epoch [111590/1000000], Loss :0.5303 Epoch [111600/1000000], Loss :0.5303 Epoch [111610/1000000], Loss :0.5303 Epoch [111620/1000000], Loss :0.5303 Epoch [111630/1000000], Loss :0.5303 Epoch [111640/1000000], Loss :0.5303 Epoch [111650/1000000], Loss :0.5303 Epoch [111660/1000000], Loss :0.5303 Epoch [111670/1000000], Loss :0.5303 Epoch [111680/1000000], Loss :0.5303 Epoch [111690/1000000], Loss :0.5303 Epoch [111700/1000000], Loss :0.5303 Epoch [111710/1000000], Loss :0.5303 Epoch [111720/1000000], Loss :0.5303 Epoch [111730/1000000], Loss :0.5303 Epoch [111740/1000000], Loss :0.5303 Epoch [111750/1000000], Loss :0.5303 Epoch [111760/1000000], Loss :0.5303 Epoch [111770/1000000], Loss :0.5303 Epoch [111780/1000000], Loss :0.5303 Epoch [111790/1000000], Loss :0.5303 Epoch [111800/1000000], Loss :0.5303 Epoch [111810/1000000], Loss :0.5303 Epoch [111820/1000000], Loss :0.5303 Epoch [111830/1000000], Loss :0.5303 Epoch [111840/1000000], Loss :0.5303 Epoch [111850/1000000], Loss :0.5303 Epoch [111860/1000000], Loss :0.5303 Epoch [111870/1000000], Loss :0.5303 Epoch [111880/1000000], Loss :0.5303 Epoch [111890/1000000], Loss :0.5303 Epoch [111900/1000000], Loss :0.5303 Epoch [111910/1000000], Loss :0.5303 Epoch [111920/1000000], Loss :0.5303 Epoch [111930/1000000], Loss :0.5303 Epoch [111940/1000000], Loss :0.5303 Epoch [111950/1000000], Loss :0.5303 Epoch [111960/1000000], Loss :0.5303 Epoch [111970/1000000], Loss :0.5303 Epoch [111980/1000000], Loss :0.5303 Epoch [111990/1000000], Loss :0.5303 Epoch [112000/1000000], Loss :0.5303 Epoch [112010/1000000], Loss :0.5303 Epoch [112020/1000000], Loss :0.5303 Epoch [112030/1000000], Loss :0.5303 Epoch [112040/1000000], Loss :0.5303 Epoch [112050/1000000], Loss :0.5303 Epoch [112060/1000000], Loss :0.5303
Epoch [112070/1000000], Loss :0.5303 Epoch [112080/1000000], Loss :0.5303 Epoch [112090/1000000], Loss :0.5303 Epoch [112100/1000000], Loss :0.5303 Epoch [112110/1000000], Loss :0.5303 Epoch [112120/1000000], Loss :0.5303 Epoch [112130/1000000], Loss :0.5303 Epoch [112140/1000000], Loss :0.5303 Epoch [112150/1000000], Loss :0.5303 Epoch [112160/1000000], Loss :0.5303 Epoch [112170/1000000], Loss :0.5303 Epoch [112180/1000000], Loss :0.5303 Epoch [112190/1000000], Loss :0.5303 Epoch [112200/1000000], Loss :0.5303 Epoch [112210/1000000], Loss :0.5303 Epoch [112220/1000000], Loss :0.5303 Epoch [112230/1000000], Loss :0.5303 Epoch [112240/1000000], Loss :0.5303 Epoch [112250/1000000], Loss :0.5303 Epoch [112260/1000000], Loss :0.5303 Epoch [112270/1000000], Loss :0.5303 Epoch [112280/1000000], Loss :0.5303 Epoch [112290/1000000], Loss :0.5303 Epoch [112300/1000000], Loss :0.5303 Epoch [112310/1000000], Loss :0.5303 Epoch [112320/1000000], Loss :0.5303 Epoch [112330/1000000], Loss :0.5303 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