Deep Learning for Computer Vision

Benchmarking with simple CNN

Let's run the previous simple_cnn model on this dataset and see how it performs. This model's performance will be the basic benchmark against which we judge other techniques. We will define a few variables for data loading and training, as shown here:

image_height, image_width = 150, 150
train_dir = os.path.join(work_dir, 'train')
test_dir = os.path.join(work_dir, 'test')
no_classes = 2
no_validation = 800
epochs = 2
batch_size = 200
no_train = 2000
no_test = 800
input_shape = (image_height, image_width, 3)
epoch_steps = no_train // batch_size
test_steps = no_test // batch_size

This constant is used for the techniques discussed in this section of training a model for predicting cats and dogs. Here, we are using ...