old_contents: #!squezee/bin/python
  from keras.datasets import cifar10
  from eve import Eve
  from keras.utils import np_utils
  
  from keras.preprocessing.image import ImageDataGenerator
  
  from models import SqueezeNetBuilder
  
  
  datasets = {
    "cifar10": cifar10
  }
  
  (x_train, y_train), (x_test, y_test) = datasets["cifar10"].load_data()
  
  x_train = x_train.astype("float32") / 255.
  x_test  = x_test.astype("float32") / 255.
  
  augumented = ImageDataGenerator(featurewise_center=True,
                                  featurewise_std_normalization=True,
                                  rotation_range=20,
                                  width_shift_range=0.2,
                                  height_shift_range=0.2,
                                  horizontal_flip=True
                                 )
  augumented.fit(x_train)
  
  y_train = np_utils.to_categorical(y_train)
  y_test  = np_utils.to_categorical(y_test)
  
  model = SqueezeNetBuilder(7, use_batch_norm = True, use_bypasses = True)(x_train.shape[1:], 10)
  
  eve = Eve()
  
  model.compile(loss = "categorical_crossentropy",
                optimizer = eve, metrics = ["accuracy"])
  
  model.fit_generator(augumented.flow(x_train, y_train),
                      samples_per_epoch = x_train.shape[0],
                      nb_epoch = 20
                     )
  
  model.predict(x_test, y_test)
  model.save("squeezenet.dump")
  
new_contents: #!squezee/bin/python
  from keras.datasets import cifar10
  from eve import Eve
  from keras.utils import np_utils
  
  from keras.preprocessing.image import ImageDataGenerator
  
  from models import SqueezeNetBuilder
  
  import argparse
  
  config = {
    "description" : "CLI to run the slighlty enhanced SqueezeNet model with Eve optimizer.\
                     All parameters have default values.",
    "epilog" : "It is not the finished version, there's still functionality to be implemented."
  }
  
  BN_HELP = "Parameter to switch on/off batch normalization."
  BY_HELP = "Parameter to switch on/off usage of bypasses."
  NS_HELP = "Parameter to switch on/off usage of Gaussian Noise."
  CONV_HELP = "It should be choosed in accordance to the dataset used. \
               Currently it is recomended not to tune this parameter."
  
  parser = argparse.ArgumentParser(**config)
  parser.add_argument("-bn", "--batch_norm", default = True, type = bool, help = BN_HELP)
  parser.add_argument("-by", "--bypasses", default = True, type = bool, help = BY_HELP)
  parser.add_argument("-ns", "--noise", default = False, type = bool, help = NS_HELP)
  parser.add_argument("--fst_conv", default = 7, type = int, choices = range(1, 13), help = CONV_HELP)
  args = parser.parse_args()
  
  
  datasets = {
    "cifar10": cifar10
  }
  
  (x_train, y_train), (x_test, y_test) = datasets["cifar10"].load_data()
  
  x_train = x_train.astype("float32") / 255.
  x_test  = x_test.astype("float32") / 255.
  
  augumented = ImageDataGenerator(featurewise_center=True,
                                  featurewise_std_normalization=True,
                                  rotation_range=20,
                                  width_shift_range=0.2,
                                  height_shift_range=0.2,
                                  horizontal_flip=True
                                 )
  augumented.fit(x_train)
  
  y_train = np_utils.to_categorical(y_train)
  y_test  = np_utils.to_categorical(y_test)
  
  model = SqueezeNetBuilder(fst_conv_size = args.fst_conv,
  	                      use_batch_norm = args.batch_norm,
  	                      use_bypasses = args.bypasses,
  	                      use_noise = args.noise)(x_train.shape[1:], 10)
  
  eve = Eve()
  
  model.compile(loss = "categorical_crossentropy",
                optimizer = eve, metrics = ["accuracy"])
  
  model.fit_generator(augumented.flow(x_train, y_train),
                      samples_per_epoch = x_train.shape[0],
                      nb_epoch = 20
                     )
  
  model.predict(x_test, y_test)
  model.save("squeezenet.dump")
  
subject: add simple CLI