input: 
  import numpy as np
  import keras
  from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
  from keras.models import Sequential
  from keras.preprocessing.image import ImageDataGenerator
  
  # Create the neural network model
  def create_cat_dog_classifier(input_shape=(64, 64, 3)):
      """Create a neural network model that can distinguish between cats and dogs.
      >>> model = create_cat_dog_classifier()
      >>> model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
      >>> datagen = ImageDataGenerator(rescale = 1./255)
      >>> train_generator = datagen.flow_from_directory(directory = '/path/to/dataset', target_size = (64, 64), color_mode = 'rgb', class_mode = 'binary', batch_size = 32)
      >>> model.fit_generator(generator = train_generator, steps_per_epoch = 800, epochs = 5)
      """
      model = Sequential()
  
      # Input layer
      model.add(Conv2D(32, (3, 3), input_shape = input_shape, activation = 'relu'))
  
      # Hidden layers
      model.add(MaxPooling2D(pool_size = (2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation = 'relu'))
      model.add(MaxPooling2D(pool_size = (2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation = 'relu'))
      model.add(MaxPooling2D(pool_size = (2, 2)))
      model.add(Dropout(0.3))
  
      # Output layer
      model.add(Flatten())
      model.add(Dense(units = 128, activation = 'relu'))
      model.add(Dense(units = 1, activation = 'sigmoid'))
  
      return model
  
output:     model = Sequential()
  
      # Input layer
      model.add(Conv2D(32, (3, 3), input_shape = input_shape, activation = 'relu'))
  
      # Hidden layers
      model.add(MaxPooling2D(pool_size = (2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation = 'relu'))
      model.add(MaxPooling2D(pool_size = (2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation = 'relu'))
      model.add(MaxPooling2D(pool_size = (2, 2)))
      model.add(Dropout(0.3))
  
      # Output layer
      model.add(Flatten())
      model.add(Dense(units = 128, activation = 'relu'))
      model.add(Dense(units = 1, activation = 'sigmoid'))
  
      return model