naive_approach.py

dataset_name = 'cats_vs_dogs'
filePath = f"{getcwd()}/../tmp2"
dataset, info = tfds.load(name=dataset_name, split=tfds.Split.TRAIN, with_info=True, data_dir=filePath)

print(info.version)

def preprocess(features):
    image = features['image']
    image = tf.image.resize(image, (224, 224))
    image = image / 255.0
    return image, features['label']
  
train_dataset = dataset.map(preprocess).batch(32)

#The next step will be to train the model using the following code:

model = create_model()
model.fit(train_dataset, epochs=5)