How can I use a GPU with Keras?

Question

My problem is that I am trying to train a convolution neural.network with Keras in google colab that is able to distinguish between dogs and cats, but at the time of passing to the training phase my model takes a long time to train and I wanted to know how I can use the GPU in the right way in order to make the training time take less time.

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('/content/drive/MyDrive/Colab Notebooks/files/dataset_CNN/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('/content/drive/MyDrive/Colab Notebooks/files/dataset_CNN/test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
  raise SystemError('GPU device not found')
with tf.device('/device:GPU:0'):#I tried to put this part that I found by researching on the internet 
  
  classifier = Sequential()
  classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
  classifier.add(MaxPooling2D(pool_size = (2, 2)))
  classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
  classifier.add(MaxPooling2D(pool_size = (2, 2)))
  classifier.add(Flatten())
  classifier.add(Dense(units = 128, activation = 'relu'))
  classifier.add(Dense(units = 1, activation = 'sigmoid'))
  classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
  classifier.fit(training_set,
                         steps_per_epoch = 8000,
                         epochs = 25,
                         validation_data = test_set,
                         validation_steps = 2000)

Previously, I did not put this part of the code "with tf.device('/device:GPU:0')", I saw this part of the code in an example on the inte.net, but it is still slow when training. I already checked the Available GPUs using:

device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
  raise SystemError('GPU device not found')

Answer 1

To Select GPU in Google Colab -
Select Edit - Notebook Setting - Hardware accelerator - GPU - Save

ImageDataGenerator is not recommended for new code. Instead you can use these augmentation features directly through layers in model training as below:

classifier = tf.keras.Sequential([
#data augmention layers
  layers.Resizing(IMG_SIZE, IMG_SIZE),
  layers.Rescaling(1./255),
  layers.RandomFlip("horizontal"),
  layers.RandomZoom(0.1),
#model building layers
  layers.Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'),
  layers.MaxPooling2D(pool_size = (2, 2)),
  layers.Conv2D(32, (3, 3), activation = 'relu'),
  layers.MaxPooling2D(pool_size = (2, 2)),
  layers.Flatten(),
  layers.Dense(units = 128, activation = 'relu'),
  layers.Dense(units = 1, activation = 'sigmoid')
])

Also, you should use image_dataset_from_directory to import the images dataset which generates a tf.data.Dataset from image files in a directory. Please refer to this gist of replicated code.

Note: fit() automatically calculates Steps_per_epoch from the training set as per batch_size.
Steps_per_epoch = len(training_dataset)//batch_size