text: #!/usr/bin/env python
  
  import sklearn as sk
  
  import numpy as np
  np.random.seed(1337)
  
  import et_cleartk_io as ctk_io
  import nn_models
  
  import sys
  import os.path
  
  import dataset
  
  import keras as k
  from keras.utils.np_utils import to_categorical
  from keras.optimizers import RMSprop
  from keras.preprocessing.sequence import pad_sequences
  from keras.models import Sequential,Model
  from keras.layers import Input,merge
  from keras.layers.core import Dense, Dropout, Activation, Flatten
  from keras.layers.convolutional import Convolution1D, MaxPooling1D, AveragePooling1D
  from keras.layers.embeddings import Embedding
  from keras.layers.normalization import BatchNormalization
  
  import pickle
  
  def _conv_bn_relu(nb_filter, subsample=1):
      def f(input):
          conv = Convolution1D(nb_filter=nb_filter, subsample_length=subsample,
                               filter_length=int(3),border_mode="same")(input)
          norm = BatchNormalization(mode=2)(conv)
          return Activation("relu")(norm)
  
      return f
  
  def _bn_relu_conv(nb_filter, subsample=1):
      def f(input):
          norm = BatchNormalization(mode=2)(input)
          activation = Activation("relu")(norm)
          return Convolution1D(nb_filter=nb_filter, subsample_length=subsample,
                               filter_length=int(3),border_mode="same")(activation)
  
      return f
  
  def _residual_block(block_function, nb_filters, repetations):
      def f(input):
          for i in range(repetations):
              input = block_function(nb_filters=nb_filters)(input)
          return input
  
      return f
  
  def _bottleneck(nb_filters, subsample=1):
      def f(input):
          conv_1   = _bn_relu_conv(nb_filters, subsample=subsample)(input)
          residual = _bn_relu_conv(nb_filters)(conv_1)
          return merge([input, residual], mode='sum')
  
      return f
  
  def resnet(maxlen, alphabet, classes):
      input = Input(shape=(maxlen,),dtype='int32')
      embeds = Embedding(len(alphabet),
                           200,
                           input_length=maxlen,
                           weights=None)(input)
  
      conv1 = _conv_bn_relu(nb_filter=100, subsample=1)(embeds)
      pool1 = MaxPooling1D(pool_length=2)(conv1)
  
      #build residual block 16 layers:
      block_fn = _bottleneck
      block1   = _residual_block(block_fn, nb_filters=100,repetations=2)(pool1)
      block2   = _residual_block(block_fn, nb_filters=100,repetations=2)(block1)
      block3   = _residual_block(block_fn, nb_filters=100,repetations=2)(block2)
      block4   = _residual_block(block_fn, nb_filters=100,repetations=2)(block3)
  
      pool2 = AveragePooling1D(pool_length=4)(block4)
      flat  = Flatten()(pool2)
      dense = Dense(classes, init="he_normal", activation='softmax')(flat)
  
      model = Model(input=input, output=dense)
      return model
  
  def main(args):
      if len(args) < 1:
          sys.stderr.write("Error - one required argument: <data directory>\n")
          sys.exit(-1)
  
      working_dir = args[0]
  
      #read in data file
  #    print("Reading data...")
      #Y, X = ctk_io.read_liblinear(working_dir) # ('data_testing/multitask_assertion/train_and_test')
      data_file = os.path.join(working_dir, 'training-data.liblinear')
  
      # learn alphabet from training and test data
      dataset1 = dataset.DatasetProvider([data_file])
      # now load training examples and labels
      train_x, train_y = dataset1.load(data_file)
  
      init_vectors = None #used for pre-trained embeddings
      
      # turn x and y into numpy array among other things
      maxlen = max([len(seq) for seq in train_x])
      outcomes = set(train_y)
      classes = len(outcomes)
  
      train_x = pad_sequences(train_x, maxlen=maxlen)
      train_y = to_categorical(np.array(train_y), classes)
  
      pickle.dump(maxlen, open(os.path.join(working_dir, 'maxlen.p'),"wb"))
      pickle.dump(dataset1.alphabet, open(os.path.join(working_dir, 'alphabet.p'),"wb"))
      #test_x = pad_sequences(test_x, maxlen=maxlen)
      #test_y = to_categorical(np.array(test_y), classes)
  
      print 'train_x shape:', train_x.shape
      print 'train_y shape:', train_y.shape
  
      #branches = [] # models to be merged
      #train_xs = [] # train x for each branch
      #test_xs = []  # test x for each branch
  
      model   = resnet(maxlen, dataset1.alphabet, classes)
  
      optimizer = RMSprop(lr=0.0001,#cfg.getfloat('cnn', 'learnrt'),
                        rho=0.9, epsilon=1e-08)
      model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])
      model.fit(train_x,
              train_y,
              nb_epoch=3,#cfg.getint('cnn', 'epochs'),
              batch_size=50,#cfg.getint('cnn', 'batches'),
              verbose=1,
              validation_split=0.1,
              class_weight=None)
  
      model.summary()
  
      json_string = model.to_json()
      open(os.path.join(working_dir, 'model_0.json'), 'w').write(json_string)
      model.save_weights(os.path.join(working_dir, 'model_0.h5'), overwrite=True)
      sys.exit(0)
  
  if __name__ == "__main__":
      main(sys.argv[1:])