Keras Python (How It Works For Developers)

Keras is a powerful, easy-to-use Python library for developing and evaluating deep-learning models. Initially developed by François Chollet, Keras models have gained popularity for their simplicity and user-friendly interface, making it an excellent choice for both beginners and experts in the field of machine learning.

Additionally, we will look into the IronPDF PDF generation library and how we can combine these two libraries to generate results and export them to PDF.

Key Features of Keras

1. User-Friendly and Modular

With the catchphrase 'Deep learning for humans', Keras is designed to be easy to use, modular, and extensible. Keras models provide clear and actionable feedback upon errors, which helps developers debug and optimize their models efficiently.

2. Support for Multiple Backends

Keras can run on top of various deep learning frameworks such as TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). This flexibility allows developers to choose the backend that best suits their needs.

3. Extensive Support for Neural Networks

Keras supports a wide range of neural network layers, including convolutional layers, recurrent layers, and fully connected layers. It also provides support for complex architectures like multi-input and multi-output models, layer sharing, and model sharing.

4. Preprocessing Utilities

Keras includes utilities for data preprocessing, such as image and text processing, which simplify the preparation of datasets for training models.

5. Model Visualization and Debugging Tools

Keras offers tools for visualizing the structure of neural networks and monitoring the training process. This is crucial for understanding the behavior of models and making necessary adjustments. The simplest model type is the Sequential Keras model, which is simply a linear stack of layers.

Installation

Installing Keras is straightforward. You can install it using pip:

pip install keras
pip install tensorflow

pip install keras
pip install tensorflow

Building a Simple Neural Network with Keras

Below is an example of how to build a simple feedforward neural network using Keras:

import keras
import numpy as np
import matplotlib.pyplot as plt

# Function to read UCR formatted data
def readucr(filename):
    """
    Reads a UCR format file and returns the features and labels.

    Args:
    filename (str): Path to the data file

    Returns:
    x, y: Features and labels as numpy arrays
    """
    data = np.loadtxt(filename, delimiter="\t")
    y = data[:, 0]
    x = data[:, 1:]
    return x, y.astype(int)

# Define the root URL for the dataset
root_url = "https://raw.githubusercontent.com/hfawaz/cd-diagram/master/FordA/"
x_train, y_train = readucr(root_url + "FordA_TRAIN.tsv")
x_test, y_test = readucr(root_url + "FordA_TEST.tsv")

# Get unique classes from the dataset
classes = np.unique(np.concatenate((y_train, y_test), axis=0))

# Plot an example from each class
plt.figure()
for c in classes:
    c_x_train = x_train[y_train == c]
    plt.plot(c_x_train[0], label="class " + str(c))
plt.legend(loc="best")
plt.show()
plt.close()

import keras
import numpy as np
import matplotlib.pyplot as plt

# Function to read UCR formatted data
def readucr(filename):
    """
    Reads a UCR format file and returns the features and labels.

    Args:
    filename (str): Path to the data file

    Returns:
    x, y: Features and labels as numpy arrays
    """
    data = np.loadtxt(filename, delimiter="\t")
    y = data[:, 0]
    x = data[:, 1:]
    return x, y.astype(int)

# Define the root URL for the dataset
root_url = "https://raw.githubusercontent.com/hfawaz/cd-diagram/master/FordA/"
x_train, y_train = readucr(root_url + "FordA_TRAIN.tsv")
x_test, y_test = readucr(root_url + "FordA_TEST.tsv")

# Get unique classes from the dataset
classes = np.unique(np.concatenate((y_train, y_test), axis=0))

# Plot an example from each class
plt.figure()
for c in classes:
    c_x_train = x_train[y_train == c]
    plt.plot(c_x_train[0], label="class " + str(c))
plt.legend(loc="best")
plt.show()
plt.close()

Output

Real-World Applications

1. Image Classification

Keras is widely used in image classification tasks. For example, convolutional neural networks (CNNs) built with Keras can achieve high accuracy in recognizing objects in images.

2. Natural Language Processing

Keras provides tools for building models that can process and understand human language. Recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks in Keras are commonly used for tasks like sentiment analysis and machine translation.

3. Generative Models

Keras can be used to develop generative models such as Generative Adversarial Networks (GANs), which are used to generate new data samples that resemble the training data.

Introducing IronPDF

IronPDF is a powerful Python library developed and maintained by Iron Software. It allows developers to create, edit, and extract PDF content in Python projects. Here are some key features of IronPDF:

1. PDF Generation:

   • You can generate PDFs from various sources, including HTML, URLs, JavaScript, CSS, and image formats.

   • Headers, footers, signatures, attachments, and security features can be added to the generated PDFs.

2. Performance Optimization:

   • IronPDF supports full multithreading and asynchronous operations.

3. Cross-Platform Compatibility:

   • It works with Python 3.7+ on Windows, macOS, Linux, Docker, Azure, and AWS.

To get started, install IronPDF using pip:

pip install ironpdf

pip install ironpdf

Once installed, you can create PDFs using either HTML content or URLs. Here are examples:

1. HTML to PDF:

from ironpdf import ChromePdfRenderer

# Create a PDF renderer
renderer = ChromePdfRenderer()

# Render HTML content as a PDF
pdf = renderer.RenderHtmlAsPdf("<h1>Hello World</h1>")
pdf.SaveAs("ironAwesome.pdf")

from ironpdf import ChromePdfRenderer

# Create a PDF renderer
renderer = ChromePdfRenderer()

# Render HTML content as a PDF
pdf = renderer.RenderHtmlAsPdf("<h1>Hello World</h1>")
pdf.SaveAs("ironAwesome.pdf")

2. URL to PDF:

from ironpdf import ChromePdfRenderer

# Create a PDF renderer
renderer = ChromePdfRenderer()

# Render a URL as a PDF
pdf = renderer.RenderUrlAsPdf("https://ironpdf.com/python/")
pdf.SaveAs("ironAwesome.pdf")

from ironpdf import ChromePdfRenderer

# Create a PDF renderer
renderer = ChromePdfRenderer()

# Render a URL as a PDF
pdf = renderer.RenderUrlAsPdf("https://ironpdf.com/python/")
pdf.SaveAs("ironAwesome.pdf")

IronPDF and Keras Python: Generating PDF of Model

Installation

pip install ironpdf
pip install keras
pip install tensorflow

pip install ironpdf
pip install keras
pip install tensorflow

Now generate the model plot and export it to a PDF using the below code:

import keras
import numpy as np
import matplotlib.pyplot as plt
from ironpdf import License, ImageToPdfConverter

# Apply your license key
License.LicenseKey = "your key goes here"

def readucr(filename):
    """Read and parse UCR formatted data."""
    data = np.loadtxt(filename, delimiter="\t")
    y = data[:, 0]
    x = data[:, 1:]
    return x, y.astype(int)

# Define the root URL for the dataset
root_url = "https://raw.githubusercontent.com/hfawaz/cd-diagram/master/FordA/"
x_train, y_train = readucr(root_url + "FordA_TRAIN.tsv")
x_test, y_test = readucr(root_url + "FordA_TEST.tsv")

# Extract unique classes from the dataset
classes = np.unique(np.concatenate((y_train, y_test), axis=0))

# Plot example data for each class
plt.figure()
for c in classes:
    c_x_train = x_train[y_train == c]
    plt.plot(c_x_train[0], label="class " + str(c))
plt.legend(loc="best")
plt.savefig('data.png') 
plt.show()
plt.close()

# Convert the saved image to a PDF
ImageToPdfConverter.ImageToPdf("data.png").SaveAs("plot.pdf")

import keras
import numpy as np
import matplotlib.pyplot as plt
from ironpdf import License, ImageToPdfConverter

# Apply your license key
License.LicenseKey = "your key goes here"

def readucr(filename):
    """Read and parse UCR formatted data."""
    data = np.loadtxt(filename, delimiter="\t")
    y = data[:, 0]
    x = data[:, 1:]
    return x, y.astype(int)

# Define the root URL for the dataset
root_url = "https://raw.githubusercontent.com/hfawaz/cd-diagram/master/FordA/"
x_train, y_train = readucr(root_url + "FordA_TRAIN.tsv")
x_test, y_test = readucr(root_url + "FordA_TEST.tsv")

# Extract unique classes from the dataset
classes = np.unique(np.concatenate((y_train, y_test), axis=0))

# Plot example data for each class
plt.figure()
for c in classes:
    c_x_train = x_train[y_train == c]
    plt.plot(c_x_train[0], label="class " + str(c))
plt.legend(loc="best")
plt.savefig('data.png') 
plt.show()
plt.close()

# Convert the saved image to a PDF
ImageToPdfConverter.ImageToPdf("data.png").SaveAs("plot.pdf")

Code Explanation

1. Importing Libraries:

   • The code begins by importing the necessary libraries:
     • Keras: A popular deep-learning library.
     • numpy (as np): Used for numerical operations.
     • matplotlib.pyplot (as plt): Used for creating plots.
     • ironpdf: The IronPDF library for working with PDFs.

2. Setting the License Key:

   • The line License.LicenseKey = "your key" sets the license key for IronPDF.

3. Reading Data:

   • The readucr function reads data from files with a specific format (tab-separated values).
   • It extracts labels (y) and features (x) from the data.

4. Loading Training and Test Data:

   • The code constructs URLs for training and test data files related to the “FordA” dataset.
   • It loads the data using the readucr function.

5. Plotting Data:

   • The code identifies unique classes in the dataset.
   • For each class, it selects the first instance (c_x_train[0]) and plots it.
   • The legend indicates the class label.

6. Saving the Plot:

   • The plot is saved as an image file named “data.png”.

7. Converting Image to PDF:

   • The ImageToPdfConverter from IronPDF converts the saved image (“data.png”) to a PDF file (“plot.pdf”).

Outputted PDF

IronPDF License

IronPDF requires a license to run, as shown in the code above. Set the license key at the beginning of the script as follows:

# Apply your license key
License.LicenseKey = "your key goes here"

# Apply your license key
License.LicenseKey = "your key goes here"

If you are interested in a trial license for the IronPDF library, a trial license key can be obtained from here.

Conclusion

The Keras artificial intelligence Python library stands out in the deep learning community due to its simplicity and flexibility. It abstracts much of the complexity involved in building neural networks, allowing developers to focus on designing and experimenting with models. Whether you're a beginner just starting with deep learning or an experienced practitioner, Keras provides the tools needed to bring your ideas to life through its emulation of the human brain.

IronPDF, on the other hand, is a versatile PDF generation and manipulation library that makes it easy to export results to PDFs. Having these two skills will help users write modern data science models and export the output to PDF for results documentation.