TensorFlow Functions to make Deep Learning Easier

Accessing various datasets in the TensorFlow library

Install and import all TensorFlow datasets

pip install tensorflow-datasets

import tensorflow_datasets as tfds

There are different widely used datasets available in this library.

MNIST Digits Dataset

mnist_data = tfds.load("mnist")
mnist_train, mnist_test = mnist_data["train"], mnist_data["test"]
print(len(mnist_train))
print(len(mnist_test))

Output

Titanic Dataset

data = tfds.load('titanic', split='train', shuffle_files=True)

data = data.shuffle(1024).batch(32).prefetch(tf.data.experimental.AUTOTUNE)
for i in data.take(1):
   print("Features")
   print(i['features'])
   
   print("Labels")
   print(i['survived'])
   

Output

Saving Checkpoints of a Model

We use checkpoints to capture the exact parameters of a model; when source code to use the parameters is available, we use the checkpoints.

checkpoint_path = “random_save_path”

# Define a Checkpoint
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)

# Create a CheckpointManager Object
checkpoint_manager = tf.train.CheckpointManager(checkpoint, checkpoint_path, max_to_keep=5)

# Save the Model
checkpoint_manager.save()

TensorFlow uses the following directed graph to load from the latest Checkpoint.

if checkpoint_manager.latest_checkpoint:
   checkpoint.restore(checkpoint_manager.latest_checkpoint)

Frequently Asked Questions

How can you create a Black Image with the same shape as the input image?

A Black image contains all zeros; hence we can use tf.zeros_like. The tf.zeros_like takes a tensor (input_image) as input & creates a tensor of zeros of the same shape and size.

tensor = tf.constant([[1, 2, 3], [7, 8, 9], [4, 5, 6])

tf.zeros_like(tensor) #[[0, 0, 0], [0, 0, 0], [0, 0, 0]]

What is ImageDataGenerator?

ImageDataGenerator is one of the features of tensorflow.keras API. We use it to perform data augmentation, such that we can train our model with different new combinations of data. With ImageDataGenerator, we do not generate new images directly, but the dataset images are transformed dynamically.

What are the different types of datasets available in the TensorFlow library?

There are many types of datasets that are available in TensorFlow, such as:

• Audio Datasets
• Graphs type
• Image Datasets
• Image Classification
• Object Detection
• Text

Conclusion

With TensorFlow’s powerful and flexible set of built-in functions, we can significantly reduce development time, streamline complex tasks, and focus more on improving model performance and innovation.

From data preprocessing and model creation to training, evaluation, and deployment, TensorFlow offers a function for almost every step of your machine learning workflow. By leveraging these tools, developers and researchers can build scalable, production-ready models with greater ease and efficiency.