# Forwardpropagation, Backpropagation and Gradient Descent with PyTorch¶

Run Jupyter Notebook

You can run the code for this section in this jupyter notebook link.

## Transiting to Backpropagation¶

- Let's go back to our simple FNN to put things in perspective
- Let us ignore non-linearities for now to keep it simpler, but it's just a tiny change subsequently
- Given a linear transformation on our input (for simplicity instead of an affine transformation that includes a bias): \(\hat y = \theta x\)
- \(\theta\) is our parameters
- \(x\) is our input
- \(\hat y\) is our prediction

- Then we have our MSE loss function \(L = \frac{1}{2} (\hat y - y)^2\)

- We need to calculate our partial derivatives of our loss w.r.t. our parameters to update our parameters: \(\nabla_{\theta} = \frac{\delta L}{\delta \theta}\)
- With chain rule we have \(\frac{\delta L}{\delta \theta} = \frac{\delta L}{\delta \hat y} \frac{\delta \hat y}{\delta \theta}\)
- \(\frac{\delta L}{\delta \hat y} = (\hat y - y)\)
- \(\frac{\delta \hat y}{\delta \theta}\) is our partial derivatives of \(y\) w.r.t. our parameters (our gradient) as we have covered previously

- With chain rule we have \(\frac{\delta L}{\delta \theta} = \frac{\delta L}{\delta \hat y} \frac{\delta \hat y}{\delta \theta}\)

## Forward Propagation, Backward Propagation and Gradient Descent¶

- All right, now let's put together what we have learnt on backpropagation and apply it on a simple feedforward neural network (FNN)
- Let us assume the following simple FNN architecture and take note that we do not have bias here to keep things simple
- FNN architecture
- Linear function: hidden size = 32
- Non-linear function: sigmoid
- Linear function: output size = 1
- Non-linear function: sigmoid

- We will be going through a binary classification problem classifying 2 types of flowers
- Output size: 1 (represented by 0 or 1 depending on the flower)
- Input size: 2 (features of the flower)
- Number of training samples: 100

- FNN architecture

Load 3-class dataset

We want to set a seed to encourage reproducibility so you can match our loss numbers.

```
import torch
import torch.nn as nn
# Set manual seed
torch.manual_seed(2)
```

Here we want to load our flower classification dataset of 150 samples. There are 2 features, hence the input size would be 150x2. There is no one-hot encoding so the output would not be a size of 150x3 but a size of 150x1.

```
from sklearn import datasets
from sklearn import preprocessing
iris = datasets.load_iris()
X = torch.tensor(preprocessing.normalize(iris.data[:, :2]), dtype=torch.float)
y = torch.tensor(iris.target.reshape(-1, 1), dtype=torch.float)
```

```
print(X.size())
print(y.size())
```

```
torch.Size([150, 2])
torch.Size([150, 1])
```

From 3 class dataset to 2 class dataset

We only want 2 classes because we want a binary classification problem. As mentioned, there is no one-hot encoding, so each class is represented by 0, 1, or 2. All we need to do is to filter out all samples with a label of 2 to have 2 classes.

```
# We only take 2 classes to make a binary classification problem
X = X[:y[y < 2].size()[0]]
y = y[:y[y < 2].size()[0]]
````
```python
print(X.size())
print(y.size())
```

```
torch.Size([100, 2])
torch.Size([100, 1])
```

Building our FNN model class from scratch

```
class FNN(nn.Module):
def __init__(self, ):
super().__init__()
# Dimensions for input, hidden and output
self.input_dim = 2
self.hidden_dim = 32
self.output_dim = 1
# Learning rate definition
self.learning_rate = 0.001
# Our parameters (weights)
# w1: 2 x 32
self.w1 = torch.randn(self.input_dim, self.hidden_dim)
# w2: 32 x 1
self.w2 = torch.randn(self.hidden_dim, self.output_dim)
def sigmoid(self, s):
return 1 / (1 + torch.exp(-s))
def sigmoid_first_order_derivative(self, s):
return s * (1 - s)
# Forward propagation
def forward(self, X):
# First linear layer
self.y1 = torch.matmul(X, self.w1) # 3 X 3 ".dot" does not broadcast in PyTorch
# First non-linearity
self.y2 = self.sigmoid(self.y1)
# Second linear layer
self.y3 = torch.matmul(self.y2, self.w2)
# Second non-linearity
y4 = self.sigmoid(self.y3)
return y4
# Backward propagation
def backward(self, X, l, y4):
# Derivative of binary cross entropy cost w.r.t. final output y4
self.dC_dy4 = y4 - l
'''
Gradients for w2: partial derivative of cost w.r.t. w2
dC/dw2
'''
self.dy4_dy3 = self.sigmoid_first_order_derivative(y4)
self.dy3_dw2 = self.y2
# Y4 delta: dC_dy4 dy4_dy3
self.y4_delta = self.dC_dy4 * self.dy4_dy3
# This is our gradients for w1: dC_dy4 dy4_dy3 dy3_dw2
self.dC_dw2 = torch.matmul(torch.t(self.dy3_dw2), self.y4_delta)
'''
Gradients for w1: partial derivative of cost w.r.t w1
dC/dw1
'''
self.dy3_dy2 = self.w2
self.dy2_dy1 = self.sigmoid_first_order_derivative(self.y2)
# Y2 delta: (dC_dy4 dy4_dy3) dy3_dy2 dy2_dy1
self.y2_delta = torch.matmul(self.y4_delta, torch.t(self.dy3_dy2)) * self.dy2_dy1
# Gradients for w1: (dC_dy4 dy4_dy3) dy3_dy2 dy2_dy1 dy1_dw1
self.dC_dw1 = torch.matmul(torch.t(X), self.y2_delta)
# Gradient descent on the weights from our 2 linear layers
self.w1 -= self.learning_rate * self.dC_dw1
self.w2 -= self.learning_rate * self.dC_dw2
def train(self, X, l):
# Forward propagation
y4 = self.forward(X)
# Backward propagation and gradient descent
self.backward(X, l, y4)
```

Training our FNN model

```
# Instantiate our model class and assign it to our model object
model = FNN()
# Loss list for plotting of loss behaviour
loss_lst = []
# Number of times we want our FNN to look at all 100 samples we have, 100 implies looking through 100x
num_epochs = 101
# Let's train our model with 100 epochs
for epoch in range(num_epochs):
# Get our predictions
y_hat = model(X)
# Cross entropy loss, remember this can never be negative by nature of the equation
# But it does not mean the loss can't be negative for other loss functions
cross_entropy_loss = -(y * torch.log(y_hat) + (1 - y) * torch.log(1 - y_hat))
# We have to take cross entropy loss over all our samples, 100 in this 2-class iris dataset
mean_cross_entropy_loss = torch.mean(cross_entropy_loss).detach().item()
# Print our mean cross entropy loss
if epoch % 20 == 0:
print('Epoch {} | Loss: {}'.format(epoch, mean_cross_entropy_loss))
loss_lst.append(mean_cross_entropy_loss)
# (1) Forward propagation: to get our predictions to pass to our cross entropy loss function
# (2) Back propagation: get our partial derivatives w.r.t. parameters (gradients)
# (3) Gradient Descent: update our weights with our gradients
model.train(X, y)
```

```
Epoch 0 | Loss: 0.9228229522705078
Epoch 20 | Loss: 0.6966760754585266
Epoch 40 | Loss: 0.6714916229248047
Epoch 60 | Loss: 0.6686137914657593
Epoch 80 | Loss: 0.666690468788147
Epoch 100 | Loss: 0.6648102402687073
```

Our loss is decreasing gradually, so it's learning. It has a possibility of reducing to almost 0 (overfitting) with sufficient model capacity (more layers or wider layers). We will explore overfitting and learning rate optimization subsequently.

## Summary¶

We've learnt...

Success

- The math behind forwardpropagation, backwardpropagation and gradient descent for FNN
- Implement a basic FNN from scratch with PyTorch

## Citation¶

If you have found these useful in your research, presentations, school work, projects or workshops, feel free to cite using this DOI.