input: Write a function to create a machine learning model using Keras that can distinguish between cats and dogs, given a dataset of 800 images. The function should initialize the model structure, compile it, create a data generator, and fit the model using the provided dataset path. Return the trained model.
  
  def create_cat_dog_model(dataset_path):
      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
  
      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_path, 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)
      
      return model
  
  assert type(create_cat_dog_model('/path/to/dataset')) == keras.engine.sequential.Sequential
  assert create_cat_dog_model('/path/to/dataset').evaluate_generator(generator = test_generator, steps = 200)[1] > 0.7
output: def create_cat_dog_model(dataset_path):
      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
  
      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_path, 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)
      
      return model