new_contents: 
  '''Trains a simple convnet on the MNIST dataset.
  
  Gets to 99.25% test accuracy after 12 epochs
  (there is still a lot of margin for parameter tuning).
  16 seconds per epoch on a GRID K520 GPU.
  '''
  
  from __future__ import print_function
  import numpy as np
  np.random.seed(1337)  # for reproducibility
  SEED = 1337
  
  from keras.datasets import mnist
  from keras.utils import np_utils
  from keras import backend as K
  
  from bird.models.resnet import ResNetBuilder
  from bird.preprocessing.sound import SoundDataGenerator
  
  batch_size = 128
  nb_classes = 3
  nb_epoch = 12
  
  # input image dimensions
  img_rows, img_cols = 224, 1024
  # number of channels
  nb_channels = 3
  # number of convolutional filters to use
  nb_filters = 32
  # size of pooling area for max pooling
  pool_size = (2, 2)
  # convolution kernel size
  kernel_size = (3, 3)
  
  model = ResNetBuilder.build_resnet_18((img_rows, img_cols, nb_channels), nb_classes)
  model.compile(loss='categorical_crossentropy',
                optimizer='adadelta',
                metrics=['accuracy'])
  
  # this is the augmentation configuration we will use for training
  train_datagen = SoundDataGenerator(
          rescale=1./255,
          shear_range=0.2,
          zoom_range=0.2,
          horizontal_flip=True)
  
  # this is a generator that will read pictures found in
  # subfolers of 'data/train', and indefinitely generate
  # batches of augmented image data
  train_generator = train_datagen.flow_from_directory(
          './resnet_sound_data/train',  # this is the target directory
          target_size=(img_rows, img_cols),  # all images will be resized to 150x150
          batch_size=16,
          class_mode='categorical',
          save_to_dir='./resnet_sound_images',
          seed=SEED)  # since we use binary_crossentropy loss, we need binary labels
  
  model.fit_generator(
          train_generator,
          samples_per_epoch=3,
          nb_epoch=1)
  
  model.save_weights('resnet.h5')
  
  # score = model.evaluate(X_test, Y_test, verbose=0)
  print('Test score:', score[0])
  print('Test accuracy:', score[1])
  
subject: Add resnet training scheme (draft)