text: # Copyright (c) 2019-2021 CRS4
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  #
  # The above copyright notice and this permission notice shall be included in
  # all copies or substantial portions of the Software.
  #
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  
  """\
  Basic Conv for CIFAR10.
  """
  
  import argparse
  import sys
  
  import pyeddl.eddl as eddl
  from pyeddl.tensor import Tensor
  
  
  def Normalization(layer):
      # return eddl.BatchNormalization(Layer)
      return layer
  
  
  MEM_CHOICES = ("low_mem", "mid_mem", "full_mem")
  
  
  def main(args):
      eddl.download_cifar10()
  
      num_classes = 10
  
      in_ = eddl.Input([3, 32, 32])
  
      layer = in_
      layer = eddl.MaxPool(eddl.ReLu(Normalization(
          eddl.Conv(layer, 32, [3, 3], [1, 1])
      )), [2, 2])
      layer = eddl.MaxPool(eddl.ReLu(Normalization(
          eddl.Conv(layer, 64, [3, 3], [1, 1])
      )), [2, 2])
      layer = eddl.MaxPool(eddl.ReLu(Normalization(
          eddl.Conv(layer, 128, [3, 3], [1, 1])
      )), [2, 2])
      layer = eddl.MaxPool(eddl.ReLu(Normalization(
          eddl.Conv(layer, 256, [3, 3], [1, 1])
      )), [2, 2])
      layer = eddl.GlobalMaxPool(layer)
      layer = eddl.Flatten(layer)
      layer = eddl.Activation(eddl.Dense(layer, 128), "relu")
  
      out = eddl.Softmax(eddl.Dense(layer, num_classes))
      net = eddl.Model([in_], [out])
  
      eddl.build(
          net,
          eddl.adam(0.001),
          ["soft_cross_entropy"],
          ["categorical_accuracy"],
          eddl.CS_GPU(mem=args.mem) if args.gpu else eddl.CS_CPU(mem=args.mem)
      )
  
      eddl.summary(net)
      eddl.plot(net, "model.pdf")
  
      x_train = Tensor.load("cifar_trX.bin")
      y_train = Tensor.load("cifar_trY.bin")
      x_train.div_(255.0)
  
      x_test = Tensor.load("cifar_tsX.bin")
      y_test = Tensor.load("cifar_tsY.bin")
      x_test.div_(255.0)
  
      if args.small:
          x_train = x_train.select([":5000"])
          y_train = y_train.select([":5000"])
          x_test = x_test.select([":1000"])
          y_test = y_test.select([":1000"])
  
      for i in range(args.epochs):
          eddl.fit(net, [x_train], [y_train], args.batch_size, 1)
          eddl.evaluate(net, [x_test], [y_test], bs=args.batch_size)
      print("All done")
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser(description=__doc__)
      parser.add_argument("--epochs", type=int, metavar="INT", default=10)
      parser.add_argument("--batch-size", type=int, metavar="INT", default=100)
      parser.add_argument("--gpu", action="store_true")
      parser.add_argument("--small", action="store_true")
      parser.add_argument("--mem", metavar="|".join(MEM_CHOICES),
                          choices=MEM_CHOICES, default="low_mem")
      main(parser.parse_args(sys.argv[1:]))
  