Neural Network from Scratch Using PyTorch

#PyTorch #python #machinelearning #neuralnetworks

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.shapeCONSOLE: 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 = 3weights_1 = initalize_parameters(                   (x.shape[1],                      first_layer_output_size))weights_1, weights_1.shapeCONSOLE: (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.shapeCONSOLE: (tensor([-2.5051], requires_grad=True),           torch.Size([1]))weights_2 = initalize_parameters((first_layer_output_size,1))weights_2, weights_2.shapeCONSOLE: (tensor([[-0.9567],                  [-1.6121],                  [ 0.6514]], requires_grad=True),          torch.Size([3, 1]))bias_2 = initalize_parameters([1])bias_2, bias_2.shapeCONSOLE: (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()