My Personal Collection of Essential TensorFlow Strategies: 7 Key Techniques for ML | by Gabe Araujo, M.Sc. | Nov, 2023

As an experienced Machine Learning (ML) practitioner, I’ve found TensorFlow to be an indispensable tool in my arsenal. Over the years, I’ve honed a collection of strategies that have become essential to my work. In this article, I’ll share seven key techniques that have significantly improved my ML models.

Data preprocessing is crucial for the success of any ML model. TensorFlow offers several tools for efficient data handling.

Code Snippet:

import tensorflow as tf
def preprocess_data(data):
# Data preprocessing steps
return processed_data
# Example of dataset preprocessing
dataset = tf.data.Dataset.list_files("<file_pattern>")
dataset = dataset.map(preprocess_data)

Keras, integrated into TensorFlow, is perfect for quick prototyping due to its user-friendly interface.

Code Snippet:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
model = Sequential([
Dense(64, activation='relu', input_shape=(input_shape,)),
Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Sometimes, predefined layers are not enough. Creating custom layers and models allows for greater flexibility.

Code Snippet:

from tensorflow.keras.layers import Layer
class MyCustomLayer(Layer):
def __init__(self, output_dim, **kwargs):
self.output_dim = output_dim
super(MyCustomLayer, self).__init__(**kwargs)
def build(self, input_shape):
# Build custom layer
pass
def call(self, inputs):
# Layer logic
return modified_inputs