old_contents: import numpy as np
  from keras.models import Sequential
  from keras.layers import Dense, Activation, Dropout
  from keras.optimizers import Adam, SGD
  import process_set # helpers
  
  # model params
  # DROPOUT = 0.2
  nb_classes = 3 # three categories
  nb_epochs = 250
  optimizer = Adam()
  batch_size = 15
  n_hidden = 32
  
  def build_neural_network():
  	"""
  	the neural network will have two hidden layers
  	with a relu activation for each.
  	The final output layer will have the softmax function
  
  	"""
  	model = Sequential()
  	model.add(Dense(nb_classes, input_shape = (4,)))
  	model.add(Activation('relu'))
  	model.add(Dense(n_hidden))
  	model.add(Activation('relu'))
  	model.add(Dense(nb_classes))
  	model.add(Activation('softmax'))
  
  	return model
  
  if __name__ == '__main__':
  
  	np.random.seed(1379)
  
  	train, labels = process_set.load_iris_dataset('Iris.csv')
  	# normalize the dataset
  	train = process_set.normalize_data(train)
  	print("dataset loaded and normalized")
  	print("building the neural network.........")
  
  	neural_net = build_neural_network()
  	print("neural network built!!!")
  
  	print("now for the compilation :P")
  	print("compiling")
  	# compile the model after it is built
  	neural_net.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy'])
  
  	print("training the neural network")
  	# train the neural network
  	neural_net.fit(train, labels, batch_size = batch_size, epochs = nb_epochs, verbose = 1)
  
  	score = neural_net.evaluate(train, labels, verbose = 1)
  
  	print("score :", score[0])
  	print("accuracy :", score[1])
  
new_contents: import numpy as np
  from keras.models import Sequential
  from keras.layers import Dense, Activation, Dropout
  from keras.optimizers import Adam, SGD
  import process_set # helpers
  
  # model params
  # DROPOUT = 0.2
  nb_classes = 3 # three categories
  nb_epochs = 250
  optimizer = Adam()
  batch_size = 15
  n_hidden = 32
  
  def build_neural_network():
  	"""
  	the neural network will have two hidden layers
  	with a relu activation for each.
  	The final output layer will have the softmax function
  
  	"""
  	model = Sequential()
  	model.add(Dense(nb_classes, input_shape = (4,)))
  	model.add(Activation('relu'))
  	model.add(Dense(n_hidden))
  	model.add(Activation('relu'))
  	model.add(Dense(nb_classes))
  	model.add(Activation('softmax'))
  	model.summary()
  
  	return model
  
  if __name__ == '__main__':
  
  	np.random.seed(1379)
  
  	train, labels = process_set.load_iris_dataset('Iris.csv')
  	# normalize the dataset
  	train = process_set.normalize_data(train)
  	print("dataset loaded and normalized")
  	print("building the neural network.........")
  
  	neural_net = build_neural_network()
  	print("neural network built!!!")
  
  	print("now for the compilation :P")
  	print("compiling")
  	# compile the model after it is built
  	neural_net.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy'])
  
  	print("training the neural network")
  	# train the neural network
  	neural_net.fit(train, labels, batch_size = batch_size, epochs = nb_epochs, verbose = 1)
  
  	score = neural_net.evaluate(train, labels, verbose = 1)
  
  	print("score :", score[0])
  	print("accuracy :", score[1])
  
subject: Update main.py