text: # Importing the Keras libraries and packages
  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
  # Initialising the CNN
  classifier = Sequential()
  # Step 1 - Convolution
  classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
  # Step 2 - Pooling
  classifier.add(MaxPooling2D(pool_size = (2, 2)))
  # Adding a second convolutional layer
  classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
  classifier.add(MaxPooling2D(pool_size = (2, 2)))
  # Step 3 - Flattening
  classifier.add(Flatten())
  # Step 4 - Full connection
  classifier.add(Dense(units = 128, activation = 'relu'))
  classifier.add(Dense(units = 1, activation = 'sigmoid'))
  # Compiling the CNN
  classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
  # Part 2 - Fitting the CNN to the images
  from keras.preprocessing.image import ImageDataGenerator
  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('dataset/training_set',
  target_size = (64, 64),
  batch_size = 32,
  class_mode = 'binary')
  test_set = test_datagen.flow_from_directory('dataset/test_set',
  target_size = (64, 64),
  batch_size = 32,
  class_mode = 'binary')
  classifier.fit_generator(training_set,
  steps_per_epoch = 8000,
  epochs = 25,
  validation_data = test_set,
  validation_steps = 2000)
  # Part 3 - Making new predictions
  import numpy as np
  from keras.preprocessing import image
  test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.jpg', target_size = (64, 64))
  test_image = image.img_to_array(test_image)
  test_image = np.expand_dims(test_image, axis = 0)
  result = classifier.predict(test_image)
  training_set.class_indices
  if result[0][0] == 1:
      prediction = 'dog'
  else:
      prediction = 'cat'