# Implementation of linear regression model in Python task 1

Linear regression belongs to regression model, i.e. it assumes that the output and input of the model are time-linear.

First, import dependencies

```import torch
from torch import nn
import numpy as np
torch.manual_seed(1)

print(torch.__version__)
torch.set_default_tensor_type('torch.FloatTensor')```

Generate part of data set randomly

```num_inputs = 2
num_examples = 1000

true_w = [2, -3.4]
true_b = 4.2

features = torch.tensor(np.random.normal(0, 1, (num_examples, num_inputs)), dtype=torch.float)
labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b
labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=torch.float)```

```import torch.utils.data as Data

batch_size = 10

# combine featues and labels of dataset
dataset = Data.TensorDataset(features, labels)

dataset=dataset,            # torch TensorDataset format
batch_size=batch_size,      # mini batch size
shuffle=True,               # whether shuffle the data or not
)```

Check the basic information of the data, where X is the characteristic column of the data and y is the label of the data.

```for X, y in data_iter:
print(X, '\n', y)
break```

Definition model

```class LinearNet(nn.Module):
def __init__(self, n_feature):
super(LinearNet, self).__init__()      # call father function to init
self.linear = nn.Linear(n_feature, 1)  # function prototype: `torch.nn.Linear(in_features, out_features, bias=True)`

def forward(self, x):
y = self.linear(x)
return y

net = LinearNet(num_inputs)
#print(net)

# ways to init a multilayer network
# method one
net = nn.Sequential(
nn.Linear(num_inputs, 1)
# other layers can be added here
)

# method two
net = nn.Sequential()

# method three
from collections import OrderedDict
net = nn.Sequential(OrderedDict([
('linear', nn.Linear(num_inputs, 1))
# ......
]))

#print(net)
#print(net[0])```

Initialize parameters of the model

```from torch.nn import init

init.normal_(net[0].weight, mean=0.0, std=0.01)
init.constant_(net[0].bias, val=0.0)  # or you can use `net[0].bias.data.fill_(0)` to modify it directly

for param in net.parameters():
print(param)```

The parameters are as follows

Define loss function

```loss = nn.MSELoss()    # nn built-in squared loss function
# function prototype: `torch.nn.MSELoss(size_average=None, reduce=None, reduction='mean')````

Define optimization function

```import torch.optim as optim

optimizer = optim.SGD(net.parameters(), lr=0.03)   # built-in random gradient descent function
print(optimizer)  # function prototype: `torch.optim.SGD(params, lr=, momentum=0, dampening=0, weight_decay=0, nesterov=False)````

Related parameters of optimization function

Training. Only three generations have been trained here

```num_epochs = 3
for epoch in range(1, num_epochs + 1):
for X, y in data_iter:
output = net(X)
l = loss(output, y.view(-1, 1))
l.backward()
optimizer.step()
print('epoch %d, loss: %f' % (epoch, l.item()))```

Finally, the comparison of results

```# result comparision
dense = net[0]
print(true_w, dense.weight.data)
print(true_b, dense.bias.data)```

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Tags: network

Posted on Fri, 14 Feb 2020 10:32:25 -0500 by skyriders