In this article I show how to build a neural network from scratch. The example is simple and short to make it easier to understand but I haven’t took any shortcuts to hide details.

Looking for Tensorflow version of this same tutorial? Go here.

```
import torch import matplotlib.pyplot as plt
```

First we create some random data. x is just 1-D tensor and the model will predict one value y.

```
x = torch.tensor([[1.,2.]])
x.shape
CONSOLE: torch.Size([1, 2])
y = 5.
```

The parameters are initialized using normal distribution where mean is 0 and variance 1.

```
def initalize_parameters(size, variance=1.0):
return (torch.randn(size) * variance).requires_grad_()
first_layer_output_size = 3
weights_1 = initalize_parameters(
(x.shape[1],
first_layer_output_size))
weights_1, weights_1.shape
CONSOLE: (tensor([[ 0.3575, -1.6650, 1.1152],
[-0.2687, -0.6715, -1.2855]],
requires_grad=True),
torch.Size([2, 3]))
bias_1 = initalize_parameters(1)
bias_1, bias_1.shape
CONSOLE: (tensor([-2.5051], requires_grad=True),
torch.Size([1]))
weights_2 = initalize_parameters((first_layer_output_size,1))
weights_2, weights_2.shape
CONSOLE: (tensor([[-0.9567],
[-1.6121],
[ 0.6514]], requires_grad=True),
torch.Size([3, 1]))
bias_2 = initalize_parameters([1])
bias_2, bias_2.shape
CONSOLE: (tensor([0.2285], requires_grad=True),
torch.Size([1]))
```

The neural network contains two linear functions and one non-linear function between them.

```
def simple_neural_network(xb):
# linear (1,2 @ 2,3 = 1,3)
l1 = xb @ weights_1 + bias_1
# non-linear
l2 = l1.max(torch.tensor(0.0))
# linear (1,3 @ 3,1 = 1,1)
l3 = l2 @ weights_2 + bias_2
return l3
```

Loss function measures how close the predictions are to the real values.

```
def loss_func(preds, yb):
# Mean Squared Error (MSE)
return ((preds-yb)**2).mean()
```

Learning rate reduces gradient making sure parameters are not changed too much in each step.

```
lr = 10E-4
```

Helper function that updates the parameters and then clears the gradient.

```
def update_params(a):
a.data -= a.grad * lr
a.grad = None
```

Training contains three simple steps:

- Make prediction
- Calculate how good the prediction was compared to the real value (When calculating loss it automatically calculates gradient so we don't need to think about it)
- Update parameters by subtracting gradient times learning rate

The code continues taking steps until the loss is less than or equal to 0.1. Finally it plots the loss change.

```
losses = []
while(len(losses) == 0 or losses[-1] > 0.1):
# 1. predict
preds = simple_neural_network(x)
# 2. loss
loss = loss_func(preds, y)
loss.backward()
# 3. update parameters
update_params(weights_1)
update_params(bias_1)
update_params(weights_2)
update_params(bias_2)
losses.append(loss)
plt.plot(list(range(len(losses))), losses)
plt.ylabel('loss (MSE)')
plt.xlabel('steps')
plt.show()
```

It changes a lot how many steps it takes to get to loss under 0.1.

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