old_contents: import numpy as np
  from keras.models import Sequential
  from keras.layers import Dense, Activation
  from keras.optimizers import Adam
  # the helpers
  from process_data import *
  
  # model parameters
  n_hidden1 = 32
  n_hidden2 = 64
  nb_classes = 19
  nb_epochs = 1750
  optimizer = Adam()
  
  def build_model():
  	"""
  	builds the neural network with three hidden layers
  	with a relu activation function.
  	The final output layer will have softmax as the
  	activation function
  
  	"""
  	model = Sequential()
  	model.add(Dense(nb_classes, input_shape = (8,)))
  	model.add(Activation('relu'))
  	model.add(Dense(n_hidden2))
  	model.add(Activation('relu'))
  	model.add(Dense(n_hidden2))
  	model.add(Activation('relu'))
  	model.add(Dense(n_hidden2))
  	model.add(Activation('relu'))
  	model.add(Dense(nb_classes))
  	model.add(Activation('softmax'))
  
  	return model
  
  if __name__ == '__main__':
  
  	# seed
  	np.random.seed(1379)
  
  	# get the dataset
  	print("loading the dataset.........")
  	train, labels = load_dataset('Pokemon.csv')
  	train = normalize_poke_data(train)
  	print("Dataset loaded!")
  	# normalize the train data
  	#train = normalize_poke_data(train)
  
  	# build the neural network
  	print("building the neural network.........")
  	net = build_model()
  
  	# compile the network
  	net.compile(loss = 'categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])
  
  	# train the network
  	print("training.........")
  	net.fit(train, labels, epochs = nb_epochs, verbose = 1)
  
  	score = 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
  from keras.optimizers import Adam
  # the helpers
  from process_data import *
  
  # model parameters
  n_hidden1 = 32
  n_hidden2 = 64
  nb_classes = 19
  nb_epochs = 1750
  optimizer = Adam()
  
  def build_model():
  	"""
  	builds the neural network with three hidden layers
  	with a relu activation function.
  	The final output layer will have softmax as the
  	activation function
  
  	"""
  	model = Sequential()
  	model.add(Dense(nb_classes, input_shape = (8,)))
  	model.add(Activation('relu'))
  	model.add(Dense(n_hidden2))
  	model.add(Activation('relu'))
  	model.add(Dense(n_hidden2))
  	model.add(Activation('relu'))
  	model.add(Dense(n_hidden2))
  	model.add(Activation('relu'))
  	model.add(Dense(nb_classes))
  	model.add(Activation('softmax'))
  	model.summary()
  
  	return model
  
  if __name__ == '__main__':
  
  	# seed
  	np.random.seed(1379)
  
  	# get the dataset
  	print("loading the dataset.........")
  	train, labels = load_dataset('Pokemon.csv')
  	train = normalize_poke_data(train)
  	print("Dataset loaded!")
  	# normalize the train data
  	#train = normalize_poke_data(train)
  
  	# build the neural network
  	print("building the neural network.........")
  	net = build_model()
  
  	# compile the network
  	net.compile(loss = 'categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])
  
  	# train the network
  	print("training.........")
  	net.fit(train, labels, epochs = nb_epochs, verbose = 1)
  
  	score = net.evaluate(train, labels, verbose = 1)
  
  	print("score :", score[0])
  	print("accuracy :", score[1])
  
subject: Update poke_run.py