Exploring TensorFlow, PyTorch, scikit-learn, and Keras with Python: Real-Life Examples for Machine Learning and Deep Learning Tasks | by Mahesh | Oct, 2023

TensorFlow is an open-source machine learning framework developed by Google. It is known for its flexibility and scalability, making it suitable for various machine learning tasks.

Real-Life Example:

Let’s create a simple TensorFlow model to classify handwritten digits using the MNIST dataset.

import tensorflow as tf
from tensorflow import keras
# Load the MNIST dataset
mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# Preprocess the data
train_images, test_images = train_images / 255.0, test_images / 255.0
# Build a simple neural network
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dropout(0.2),
keras.layers.Dense(10)
])
# Compile the model
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# Train the model
model.fit(train_images, train_labels, epochs=10)
# Evaluate the model
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f"Test accuracy: {test_acc}")

PyTorch is an open-source deep learning framework developed by Facebook’s AI Research lab. It’s known for its dynamic computation graph and flexibility.

Real-Life Example:

Let’s create a simple PyTorch model to classify handwritten digits using the MNIST dataset.

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
# Load the MNIST dataset
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True)
# Define a simple neural network
class Net(nn.Module):
def __init__(self):
super(Net, self).__init()
self.fc1 = nn.Linear(28 * 28, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = x.view(-1, 28 * 28)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
net = Net()
# Define the loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# Train the model
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print("Finished Training")

scikit-learn is a popular machine learning library for traditional machine learning algorithms and data preprocessing.

Real-Life Example:

Let’s use scikit-learn for a simple classification task using the Iris dataset.

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
# Load the Iris dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create and train a Random Forest classifier
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
# Make predictions on the test set
y_pred = clf.predict(X_test)
# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

Keras is an open-source neural network library that runs on top of other deep learning frameworks, including TensorFlow.

Real-Life Example:

Let’s create a simple Keras model for image classification using the CIFAR-10 dataset.

from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from keras.utils import to_categorical
# Load the CIFAR-10 dataset
(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()
# Preprocess the data
train_images = train_images / 255.0
test_images = test_images / 255.0
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
# Build a convolutional neural network
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
# Compile the model
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
# Train the model
model.fit(train_images, train_labels, epochs=10, batch_size=32)
# Evaluate the model
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f"Test accuracy: {test_acc}")