new_contents: import numpy as np 
  import glob
  import os
  import re
  
  np.random.seed(123)
  from keras.models import Sequential
  from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, pooling
  from keras.utils import np_utils
  
  def extract_number(s):
    return int(s.split('/')[-1].split('th')[0])
  
  all_train_files = sorted(glob.glob('./Desktop/KAKAO_TRAIN_DATA/*.npy'), key=extract_number)
  all_test_files = sorted(glob.glob('./Desktop/KAKAO_TEST_DATA/*.npy'), key=extract_number)
  
  X_train=[]
  X_test=[]
  Y_train=[]
  Y_test=[]
  
  for i in range(np.shape(all_train_files)[0]):
  	X_train.append(np.load(all_train_files[i]))
  for i in range(np.shape(all_test_files)[0]):
  	X_test.append(np.load(all_test_files[i]))
  
  
  for i in range(1400):
  	Y_train.append(1)
  for i in range(1400):
  	Y_train.append(0)
  
  for i in range(713):
  	Y_test.append(1)
  for i in range(713):
  	Y_test.append(0)
  
  
  X_train=np.asarray(X_train)
  X_test=np.asarray(X_test)
  Y_train=np.asarray(Y_train)
  Y_test=np.asarray(Y_test)
  
  X_train=X_train.reshape(X_train.shape[0], 180, 180, 1)
  X_test=X_test.reshape(X_test.shape[0], 180, 180, 1)
  
  X_train /= np.max(X_train)
  X_test /= np.max(X_test)
  
  Y_train = np_utils.to_categorical(Y_train,2)
  Y_test = np_utils.to_categorical(Y_test,2)
  
  model = Sequential()
  model.add(Conv2D(filters=32, kernel_size=(5,5), strides=2, padding='valid', activation='relu', input_shape=(180,180,1)))
  model.add(Conv2D(32,kernel_size=(3,3), strides=2, padding='valid', activation='relu'))
  model.add(pooling.MaxPooling2D(pool_size=(2,2)))
  model.add(Dropout(0.25))
  
  model.add(Flatten())
  model.add(Dense(1024,activation='relu'))
  model.add(Dropout(0.5))
  model.add(Dense(2, activation='softmax'))
  
  model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
  
  model.fit(X_train, Y_train, batch_size=16, nb_epoch=20, verbose=1)
  
  
  score = model.evaluate(X_test, Y_test, verbose=0)
  print(model.metrics_names)
  print(score)
  
subject: Create CNN_Cover_song.py