Learn to set-up a typical end-to-end pipeline for training CNNs. In this article, we will go through the end-to-end pipeline of training convolution neural networks, i.e. organizing the data into directories, preprocessing, data augmentation, model building, etc.
In this article, we will go through the end-to-end pipeline of training convolution neural networks, i.e. organizing the data into directories, preprocessing, data augmentation, model building, etc.
We will spend a good amount of time on data preprocessing techniques commonly used with image processing. This is because preprocessing takes about 50–80% of your time in most deep learning projects, and knowing some useful tricks will help you a lot in your projects. We will be using the flowers dataset from Kaggle to demonstrate the key concepts. To get into the codes directly, an accompanying notebook is published on Kaggle(Please use a CPU for running the initial parts of the code and** GPU** for model training).
Let’s begin with importing the necessary libraries and loading the dataset. This is a requisite step in every data analysis process.
## Importing necessary libraries import keras import tensorflow from skimage import io import os import glob import numpy as np import random import matplotlib.pyplot as plt %matplotlib inline ## Importing and Loading the data into data frame #class 1 - Rose, class 0- Daisy DATASET_PATH = '../input/flowers-recognition/flowers/' flowers_cls = ['daisy', 'rose'] ## glob through the directory (returns a list of all file paths) flower_path = os.path.join(DATASET_PATH, flowers_cls, '*') flower_path = glob.glob(flower_path) ## access some element (a file) from the list image = io.imread(flower_path)
Images come in different shapes and sizes. They also come through different sources. For example, some images are what we call “natural images”, which means they are taken in color, in the** real world**. For example:
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