text: import numpy as np 
  import pickle
  import tensorflow as tf
  from keras import layers, Sequential
  
  
  # load training and testing data
  train_imgs = pickle.load(open("train_imgs.pickle", "rb"))
  train_labels = pickle.load(open("train_labels.pickle", "rb"))
  test_imgs = pickle.load(open("test_imgs.pickle", "rb"))
  test_labels = pickle.load(open("test_labels.pickle", "rb"))
  
  # Create model. Uses are encouraged make modifications to improve accuracy and decrease loss
  model = Sequential([
      layers.Conv2D(128, (3, 3), input_shape=train_imgs.shape[1:]),
      layers.Activation("relu"),
      layers.MaxPooling2D(pool_size=(2, 2)),
      layers.Flatten(),
      layers.Dense(1),
      layers.Activation('sigmoid')
  ])
  
  NAME = "hotdog-not-hotdog-classifier"
  
  # Compile and fit/train model
  model.compile(loss='binary_crossentropy',
                          optimizer='adam',
                          metrics=['accuracy'])
  model.fit(train_imgs, train_labels, batch_size=32, epochs=4, validation_split=0.1)
  
  # Evaluate accuracy
  test_loss, test_acc = model.evaluate(test_imgs, test_labels)
  print(f"Test Accuracy: {test_acc}")
  
  # Save model
  model.save(NAME)