Mini-Batch GD

author: Juma Shafara date: "2024-08-08" title: Training Two Parameter Mini-Batch Gradient Decent keywords: Training Two Parameter, Mini-Batch Gradient Decent, Training Two Parameter Mini-Batch Gradient Decent] description: In this Lab, you will practice training a model by using Mini-Batch Gradient Descent.

Photo by DATAIDEA

Linear Regression 1D: Training Two Parameter Mini-Batch Gradient Decent

Objective

How to use Mini-Batch Gradient Descent to train model.

Preparation

We'll need the following libraries:

# Import the libraries we need for this lab

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
from dataidea_science.plots import plot_error_surfaces

The class plot_error_surfaces is just to help you visualize the data space and the parameter space during training and has nothing to do with PyTorch.

Make Some Data

Import PyTorch and set random seed:

# Import PyTorch library

import torch
torch.manual_seed(1)

<torch._C.Generator at 0x7dc5dfe66290>

Generate values from -3 to 3 that create a line with a slope of 1 and a bias of -1. This is the line that you need to estimate. Add some noise to the data:

# Generate the data with noise and the line

X = torch.arange(-3, 3, 0.1).view(-1, 1)
f = 1 * X - 1
Y = f + 0.1 * torch.randn(X.size())

Plot the results:

# Plot the line and the data

plt.plot(X.numpy(), Y.numpy(), 'o', label = 'y', c='g')
plt.plot(X.numpy(), f.numpy(), label = 'f', c='b')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()

No description has been provided for this image

Create the Model and Cost Function (Total Loss)

Define the forward function:

# Define the prediction function

def forward(x):
    return w * x + b

Define the cost or criterion function:

# Define the cost function

def criterion(yhat, y):
    return torch.mean((yhat - y) ** 2)

Create a plot_error_surfaces object to visualize the data space and the parameter space during training:

# Create a plot_error_surfaces object.

get_surface = plot_error_surfaces(15, 13, X, Y, 30)

<Figure size 640x480 with 0 Axes>

Train the Model: Batch Gradient Descent (BGD)

Define train_model_BGD function.

# Define the function for training model

w = torch.tensor(-15.0, requires_grad = True)
b = torch.tensor(-10.0, requires_grad = True)
lr = 0.1
LOSS_BGD = []

def train_model_BGD(epochs):
    for epoch in range(epochs):
        Yhat = forward(X)
        loss = criterion(Yhat, Y)
        LOSS_BGD.append(loss)
        get_surface.set_para_loss(w.data.tolist(), b.data.tolist(), loss.tolist())
        get_surface.plot_ps()
        loss.backward()
        w.data = w.data - lr * w.grad.data
        b.data = b.data - lr * b.grad.data
        w.grad.data.zero_()
        b.grad.data.zero_()

Run 10 epochs of batch gradient descent: bug data space is 1 iteration ahead of parameter space.

# Run train_model_BGD with 10 iterations

train_model_BGD(10)

Stochastic Gradient Descent (SGD) with Dataset DataLoader

Create a plot_error_surfaces object to visualize the data space and the parameter space during training:

# Create a plot_error_surfaces object.

get_surface = plot_error_surfaces(15, 13, X, Y, 30, go = False)

Import Dataset and DataLoader libraries

# Import libraries

from torch.utils.data import Dataset, DataLoader

Create Data class

# Create class Data

class Data(Dataset):

    # Constructor
    def __init__(self):
        self.x = torch.arange(-3, 3, 0.1).view(-1, 1)
        self.y = 1 * X - 1
        self.len = self.x.shape[0]

    # Getter
    def __getitem__(self, index):    
        return self.x[index], self.y[index]

    # Get length
    def __len__(self):
        return self.len

Create a dataset object and a dataloader object:

# Create Data object and DataLoader object

dataset = Data()
trainloader = DataLoader(dataset = dataset, batch_size = 1)

Define train_model_SGD function for training the model.

# Define train_model_SGD function

w = torch.tensor(-15.0, requires_grad = True)
b = torch.tensor(-10.0, requires_grad = True)
LOSS_SGD = []
lr = 0.1
def train_model_SGD(epochs):
    for epoch in range(epochs):
        Yhat = forward(X)
        get_surface.set_para_loss(w.data.tolist(), b.data.tolist(), criterion(Yhat, Y).tolist())
        get_surface.plot_ps()
        LOSS_SGD.append(criterion(forward(X), Y).tolist())
        for x, y in trainloader:
            yhat = forward(x)
            loss = criterion(yhat, y)
            get_surface.set_para_loss(w.data.tolist(), b.data.tolist(), loss.tolist())
            loss.backward()
            w.data = w.data - lr * w.grad.data
            b.data = b.data - lr * b.grad.data
            w.grad.data.zero_()
            b.grad.data.zero_()
        get_surface.plot_ps()

Run 10 epochs of stochastic gradient descent: bug data space is 1 iteration ahead of parameter space.

# Run train_model_SGD(iter) with 10 iterations

train_model_SGD(10)

Mini Batch Gradient Descent: Batch Size Equals 5

Create a plot_error_surfaces object to visualize the data space and the parameter space during training:

# Create a plot_error_surfaces object.

get_surface = plot_error_surfaces(15, 13, X, Y, 30, go = False)
get_surface

<__main__.plot_error_surfaces at 0x77fc19f16660>

Create Data object and create a Dataloader object where the batch size equals 5:

# Create DataLoader object and Data object

dataset = Data()
trainloader = DataLoader(dataset = dataset, batch_size = 5)

Define train_model_Mini5 function to train the model.

# Define train_model_Mini5 function

w = torch.tensor(-15.0, requires_grad = True)
b = torch.tensor(-10.0, requires_grad = True)
LOSS_MINI5 = []
lr = 0.1

def train_model_Mini5(epochs):
    for epoch in range(epochs):
        Yhat = forward(X)
        get_surface.set_para_loss(w.data.tolist(), b.data.tolist(), criterion(Yhat, Y).tolist())
        get_surface.plot_ps()
        LOSS_MINI5.append(criterion(forward(X), Y).tolist())
        for x, y in trainloader:
            yhat = forward(x)
            loss = criterion(yhat, y)
            get_surface.set_para_loss(w.data.tolist(), b.data.tolist(), loss.tolist())
            loss.backward()
            w.data = w.data - lr * w.grad.data
            b.data = b.data - lr * b.grad.data
            w.grad.data.zero_()
            b.grad.data.zero_()

Run 10 epochs of mini-batch gradient descent: bug data space is 1 iteration ahead of parameter space.

# Run train_model_Mini5 with 10 iterations.

train_model_Mini5(10)

Mini Batch Gradient Descent: Batch Size Equals 10

Create a plot_error_surfaces object to visualize the data space and the parameter space during training:

# Create a plot_error_surfaces object.

get_surface = plot_error_surfaces(15, 13, X, Y, 30, go = False)

Create Data object and create a Dataloader object batch size equals 10

# Create DataLoader object

dataset = Data()
trainloader = DataLoader(dataset = dataset, batch_size = 10)

Define train_model_Mini10 function for training the model.

# Define train_model_Mini5 function

w = torch.tensor(-15.0, requires_grad = True)
b = torch.tensor(-10.0, requires_grad = True)
LOSS_MINI10 = []
lr = 0.1

def train_model_Mini10(epochs):
    for epoch in range(epochs):
        Yhat = forward(X)
        get_surface.set_para_loss(w.data.tolist(), b.data.tolist(), criterion(Yhat, Y).tolist())
        get_surface.plot_ps()
        LOSS_MINI10.append(criterion(forward(X),Y).tolist())
        for x, y in trainloader:
            yhat = forward(x)
            loss = criterion(yhat, y)
            get_surface.set_para_loss(w.data.tolist(), b.data.tolist(), loss.tolist())
            loss.backward()
            w.data = w.data - lr * w.grad.data
            b.data = b.data - lr * b.grad.data
            w.grad.data.zero_()
            b.grad.data.zero_()

Run 10 epochs of mini-batch gradient descent: bug data space is 1 iteration ahead of parameter space.

# Run train_model_Mini5 with 10 iterations.

train_model_Mini10(10)

Plot the loss for each epoch:

LOSS_BGD_ = [loss.item() for loss in LOSS_BGD]

# Plot out the LOSS for each method

plt.plot(LOSS_BGD_,label = "Batch Gradient Descent")
plt.plot(LOSS_SGD,label = "Stochastic Gradient Descent")
plt.plot(LOSS_MINI5,label = "Mini-Batch Gradient Descent, Batch size: 5")
plt.plot(LOSS_MINI10,label = "Mini-Batch Gradient Descent, Batch size: 10")
plt.legend()

<matplotlib.legend.Legend at 0x77fc3583c5f0>

Practice

Perform mini batch gradient descent with a batch size of 20. Store the total loss for each epoch in the list LOSS20.

# Practice: Perform mini batch gradient descent with a batch size of 20.

dataset = Data()
trainloader = DataLoader(dataset = dataset, batch_size = 20)
w = torch.tensor(-15.0, requires_grad = True)
b = torch.tensor(-10.0, requires_grad = True)

LOSS_MINI20 = []
lr = 0.1

def my_train_model(epochs):
    for epoc in range(epochs):
        Yhat = forward(X)
        get_surface.set_para_loss(w.data.tolist(), b.data.tolist(), criterion(Yhat, Y).tolist())
        get_surface.plot_ps()
        LOSS_MINI20.append(criterion(forward(X), Y).tolist())
        for x, y in trainloader:
            yhat = forward(x)
            loss = criterion(yhat, y)
            get_surface.set_para_loss(w.data.tolist(), b.data.tolist(), loss.tolist())
            loss.backward()
            w.data = w.data - lr * w.grad.data
            b.data = b.data - lr * b.grad.data
            w.grad.data.zero_()
            b.grad.data.zero_()

Double-click here for the solution.

Plot a graph that shows the LOSS results for all the methods.

# Practice: Plot a graph to show all the LOSS functions

# Type your code here

Double-click here for the solution.

About the Author:

Hi, My name is Juma Shafara. Am a Data Scientist and Instructor at DATAIDEA. I have taught hundreds of peope Programming, Data Analysis and Machine Learning.

I also enjoy developing innovative algorithms and models that can drive insights and value.

I regularly share some content that I find useful throughout my learning/teaching journey to simplify concepts in Machine Learning, Mathematics, Programming, and related topics on my website jumashafara.dataidea.org.

Besides these technical stuff, I enjoy watching soccer, movies and reading mystery books.

Mini-Batch GD

Linear Regression 1D: Training Two Parameter Mini-Batch Gradient Decent

Objective

Table of Contents

Preparation

Make Some Data

Create the Model and Cost Function (Total Loss)

Train the Model: Batch Gradient Descent (BGD)

Stochastic Gradient Descent (SGD) with Dataset DataLoader

Mini Batch Gradient Descent: Batch Size Equals 5

Mini Batch Gradient Descent: Batch Size Equals 10

Practice

About the Author:

What's on your mind? Put it in the comments!

Mini-Batch GD

Linear Regression 1D: Training Two Parameter Mini-Batch Gradient Decent

Objective

Table of Contents

Don't Miss Any Updates!

Preparation

Make Some Data

Create the Model and Cost Function (Total Loss)

Train the Model: Batch Gradient Descent (BGD)

Stochastic Gradient Descent (SGD) with Dataset DataLoader

Mini Batch Gradient Descent: Batch Size Equals 5

Mini Batch Gradient Descent: Batch Size Equals 10

Practice

About the Author:

What's on your mind? Put it in the comments!