input: Write a function to classify images of cats and dogs using a neural network model in Python. The function should take a dataset directory as input and return a trained Keras model.
  
  
  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
  
  def classify_cats_dogs(dataset_directory):
      # Create the neural network model
      model = Sequential()
  
      # Input layer
      model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3), 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'))
  
      # Compile the model
      model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
  
      # Create data generator
      datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
  
      # Fit the model
      train_generator = datagen.flow_from_directory(directory=dataset_directory, 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, validation_data=test_generator, validation_steps=200)
      
      return model
  
  # Test cases
  assert isinstance(classify_cats_dogs('/path/to/dataset'), keras.models.Sequential)
  
output: def classify_cats_dogs(dataset_directory):
      model = Sequential()
      model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 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))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.3))
      model.add(Flatten())
      model.add(Dense(units=128, activation='relu'))
      model.add(Dense(units=1, activation='sigmoid'))
      model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
      datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
      train_generator = datagen.flow_from_directory(directory=dataset_directory, 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, validation_data=test_generator, validation_steps=200)
      return model