8.2 TensorFlow and Keras

TensorFlow is a library to train deep learning models and Keras is higher level abstraction on the top of TensorFlow. Keras used to be separate library but from tensorflow 2+ version, keras became part of the tensorflow library. The libraries can be installed using pip install tensorflow (for CPU and GPU). However, additional setup is required to integrate TensorFlow with GPU.
Neural networks expect an image of a certain size, therefore, we need to provide the image size in target_size parameter of the load_img function.
Each image consists of pixel and each of these pixels has the shape of 3 dimensions (height, width, color channels)
A typical color image consists of three color channels: red, green and blue. Each color channel has 8 bits or 1 byte and can represent distinct values between 0-256 (uint8 type).

Classes, functions, and methods:

import tensorflow as tf: to import tensorflow library
from tensorflow import keras: to import keras
from tensorflow.keras.preprocessing.image import load_img: to import load_img function
load_img('path/to/image', targe_size=(150,150)): to load the image of 150 x 150 size in PIL format
np.array(img): convert image into a numpy array of 3D shape, where each row of the array represents the value of red, green, and blue color channels of one pixel in the image.

Notes

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tensorflow and keras as deep learning libraries
end-to-end open source machine learning framework
tensorflow as library for training deep learning models
keras as high-level abstraction on top of tensorflow
installing tensorflow
local vs cloud configuration
loading and preprocessing images
keras is part of tensorflow since version 2.0
working with different image sizes
processing images using the python pillow library
encoding images as numpy arrays
image size (i.e. 150 x 150 pixels) multiplied by number of colors (i.e. RGB) equals shape of array
numpy array dtype as unsigned int8 (uint8) which includes the range from 0 to 255

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