Comparing TensorFlow (Keras), PyTorch, & MLX – Day 46

  Comparing Deep Learning on TensorFlow (Keras), PyTorch, and Apple’s MLX Deep learning frameworks such as TensorFlow (Keras), PyTorch, and Apple’s MLX offer powerful tools to build and train machine learning models. Despite solving similar problems, these frameworks have different philosophies, APIs, and optimizations under the hood. In this post, we will examine how the same model is implemented on each platform and why the differences in code arise, especially focusing on why MLX is more similar to PyTorch than TensorFlow. 1. Model in PyTorch PyTorch is known for giving developers granular control over model-building and training processes. The framework encourages writing custom training loops, making it highly flexible, especially for research purposes. PyTorch Code: What’s Happening Behind the Scenes in PyTorch? PyTorch gives the developer direct control over every step of the model training process. The training loop is written manually, where: Forward pass: Defined in the forward() method, explicitly computing the output layer by layer. Backward pass: After calculating the loss, the gradients are computed using loss.backward(). Gradient updates: The optimizer manually updates the weights after each batch using optimizer.step(). This manual training loop allows researchers and developers to experiment with unconventional architectures or optimization methods. The gradient...

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122

TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras _. day 16

Mastering TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras Mastering TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras TensorFlow and Keras provide powerful tools for building, training, and evaluating deep learning models. In this blog post, we will explore three essential techniques: Using TensorBoard for visualization Utilizing callbacks to enhance model training Saving and restoring models Using TensorBoard for Visualization TensorBoard is an interactive visualization tool that helps you understand your model’s training dynamics. It allows you to view learning curves, compare metrics between multiple runs, and analyze training statistics. Installation !pip install -q -U tensorflow tensorboard-plugin-profile Setting Up Logging Directory We need a directory to save our logs. This directory will contain event files that TensorBoard reads to visualize the training process. from pathlib import Path from time import strftime def get_run_logdir(root_logdir="my_logs"): return Path(root_logdir) / strftime("run_%Y_%m_%d_%H_%M_%S") run_logdir = get_run_logdir() Saving and Restoring a Model Keras allows you to save the entire model (architecture, weights, and training configuration) to a single file or a folder. Saving a Model model.save("my_keras_model", save_format="tf") Loading a Model model = tf.keras.models.load_model("my_keras_model") Saving Weights Only model.save_weights("my_weights.h5") model.load_weights("my_weights.h5") Using Callbacks Callbacks in Keras allow you to perform actions at various stages of training (e.g., saving...

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14

Sequential vs Functional Keras API Part 2 explanation _ Day 15

Keras API Example Let’s continue from day 14 which we explained the 3 Keras API types and compare them Understanding Sequential vs. Functional API in Keras with a Simple Example When building neural networks in Keras, there are two main ways to define models: the Sequential API and the Functional API. In this post, we’ll explore the differences between these two approaches using a simple mathematical example. Sequential API The Sequential API in Keras is a linear stack of layers. It’s easy to use but limited to single-input, single-output stacks of layers. Here’s a simple example to illustrate how it works. Objective: Multiply the input $x$ by 2. Add 3 to the result. Let’s implement this using the Sequential API: from keras.models import Sequential from keras.layers import Lambda # Define a simple sequential model model = Sequential() model.add(Lambda(lambda x: 2 * x, input_shape=(1,))) model.add(Lambda(lambda x: x + 3)) model.summary() Functional API The Functional API in Keras is more flexible and allows for the creation of complex models with multiple inputs and outputs. We’ll use the same mathematical operations to illustrate how it works. Objective: Multiply the input $x$ by 2. Add 3 to the result. Mathematical Operations: $y_1 = 2...

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25

sequential , functional and model subclassing API in Keras _ day 14

In our last blog on day 13, we explained what’s Keras and we showed a code example which was using the Sequential API but did not discuss its API type. Understanding Keras APIs and Their Use Cases In our previous blog post on Day 13, we introduced Keras and provided a code example using the Sequential API. In this post, we will delve into the different types of Keras APIs: Sequential, Functional, and Model Subclassing. We will explain each API, its inventor, appropriate use cases, and whether they can be used interchangeably. We will also analyze the code examples provided to illustrate the differences between these approaches. Sequential API Inventor: François Chollet, the creator of Keras. Overview: The Sequential API is the simplest and most straightforward way to build a neural network in Keras. It allows you to create a model layer-by-layer in a linear stack. Use Cases: – Simple models with a single input and a single output.– Beginners and quick prototyping.– Basic feedforward neural networks and simple CNNs. Mathematical Foundation: The Sequential API models are compositions of functions, where each layer applies a transformation :     This means the output of one layer is the input to...

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49

What is Keras _ day 13

Understanding Keras and Its Role in Deep Learning What is Keras? Keras is an open-source software library that provides a Python interface for artificial neural networks. It serves as a high-level API, simplifying the process of building and training deep learning models. Developed by François Chollet, a researcher at Google, Keras was first released in March 2015. It is designed to enable fast experimentation with deep neural networks, which is crucial for research and development in machine learning and artificial intelligence (AI). Who Invented Keras and Why? François Chollet created Keras to democratize deep learning by making it accessible and easy to use. His goal was to provide a tool that allows for rapid experimentation with neural networks, enabling researchers and developers to prototype and test ideas quickly. The vision behind Keras was to lower the barrier to entry in deep learning, making it possible for more people to contribute to the field. What’s Behind Keras? Keras itself is a high-level wrapper for deep learning frameworks. Initially, it supported multiple backends, including TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). With the release of Keras 3, it now seamlessly integrates with TensorFlow, JAX, and PyTorch, allowing users to choose their preferred...

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