Building CNN models from scratch

Let's start building our image categorization classifier. Our approach will be to build models on our training dataset and validate it on our validation dataset. In the end, we will test the performance of all our models on the test dataset. Before we jump into modeling, let's load and prepare our datasets. To start with, we load up some basic dependencies:

import glob 
import numpy as np 
import matplotlib.pyplot as plt 
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array, array_to_img 
 
%matplotlib inline 

Let's now load our datasets, using the following code snippet:

IMG_DIM = (150, 150) 
 
train_files = glob.glob('training_data/*') 
train_imgs = [img_to_array(load_img(img, target_size=IMG_DIM)) for img  
              in train_files] 
train_imgs = np.array(train_imgs) 
train_labels = ...