old_contents: from tensorflow import keras
  
  from keras.datasets import fashion_mnist
  from keras.models import Sequential
  from keras.layers import Dense, Dropout
  from keras.optimizers import RMSprop
  
  batch_size = 128
  epochs = 5
  dropout = 0.2
  lr = 0.001
  lr_decay = 0.0
  rho = 0.0
  
  (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
  x_train = x_train.reshape(60000, 784)
  x_test = x_test.reshape(10000, 784)
  x_train = x_train.astype('float32')
  x_test = x_test.astype('float32')
  x_train /= 255
  x_test /= 255
  y_train = keras.utils.to_categorical(y_train, 10)
  y_test = keras.utils.to_categorical(y_test, 10)
  
  model = Sequential()
  model.add(Dense(512, activation='relu', input_shape=(784,)))
  model.add(Dropout(dropout))
  model.add(Dense(512, activation='relu'))
  model.add(Dropout(dropout))
  model.add(Dense(10, activation='softmax'))
  
  model.compile(
      loss='categorical_crossentropy',
      optimizer=RMSprop(lr=lr, rho=rho, decay=lr_decay),
      metrics=['accuracy'])
  
  model.fit(
      x_train, y_train,
      batch_size=batch_size,
      epochs=epochs,
      verbose=1,
      validation_data=(x_test, y_test),
      callbacks=[keras.callbacks.TensorBoard()])
  
new_contents: from tensorflow import keras
  
  from keras.datasets import fashion_mnist
  from keras.models import Sequential
  from keras.layers import Dense, Dropout
  from keras.optimizers import RMSprop
  
  batch_size = 128
  epochs = 5
  dropout = 0.2
  lr = 0.001
  lr_decay = 0.0
  rho = 0.0
  val_split = 0.2
  
  (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
  x_train = x_train.reshape(60000, 784)
  x_test = x_test.reshape(10000, 784)
  x_train = x_train.astype('float32')
  x_test = x_test.astype('float32')
  x_train /= 255
  x_test /= 255
  y_train = keras.utils.to_categorical(y_train, 10)
  y_test = keras.utils.to_categorical(y_test, 10)
  
  model = Sequential()
  model.add(Dense(512, activation='relu', input_shape=(784,)))
  model.add(Dropout(dropout))
  model.add(Dense(512, activation='relu'))
  model.add(Dropout(dropout))
  model.add(Dense(10, activation='softmax'))
  
  model.compile(
      loss='categorical_crossentropy',
      optimizer=RMSprop(lr=lr, rho=rho, decay=lr_decay),
      metrics=['accuracy'])
  
  model.fit(
      x_train, y_train,
      batch_size=batch_size,
      epochs=epochs,
      verbose=1,
      validation_split=0.2,
      callbacks=[keras.callbacks.TensorBoard()])
  
  score = model.evaluate(x_test, y_test, verbose=0)
  print('Test loss: %f' % score[0])
  print('Test accuracy: %f' % score[1])
  
subject: Use hold out set at end to test