old_contents: # pylint: skip-file
  from data import mnist_iterator
  import mxnet as mx
  import numpy as np
  import logging
  
  
  class NumpySoftmax(mx.operator.NumpyOp):
      def need_top_grad(self):
          return False
      
      def list_arguments(self):
          return ['data', 'label']
  
      def list_outputs(self):
          return ['prob']
  
      def infer_shape(self, in_shape):
      	return [in_shape[0], (in_shape[0][0],)], [in_shape[0]]
  
      def forward(self, in_data, out_data):
      	x = in_data[0]
      	y = out_data[0]
      	y[:] = np.exp(x - x.max(axis=1).reshape((x.shape[0], 1)))
      	y /= y.sum(axis=1).reshape((x.shape[0], 1))
  
      def backward(self, out_grad, in_data, out_data, in_grad):
      	l = in_data[1]
      	l = l.reshape((l.size,)).astype(np.int)
      	y = out_data[0]
      	dx = in_grad[0]
      	dx[:] = y
      	dx[np.arange(l.shape[0]), l] -= 1.0
  
  # define mlp
  
  data = mx.symbol.Variable('data')
  fc1 = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128)
  act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type="relu")
  fc2 = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64)
  act2 = mx.symbol.Activation(data = fc2, name='relu2', act_type="relu")
  fc3 = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=10)
  #mlp = mx.symbol.Softmax(data = fc3, name = 'mlp')
  mysoftmax = NumpySoftmax()
  mlp = mysoftmax(data=fc3, name = 'mlp')
  
  # data
  
  train, val = mnist_iterator(batch_size=100, input_shape = (784,))
  
  # train
  
  logging.basicConfig(level=logging.DEBUG)
  
  model = mx.model.FeedForward(
      ctx = mx.gpu(), symbol = mlp, num_epoch = 20,
      learning_rate = 0.1, momentum = 0.9, wd = 0.00001)
  
  model.fit(X=train, eval_data=val)
  
  
new_contents: # pylint: skip-file
  from data import mnist_iterator
  import mxnet as mx
  import numpy as np
  import logging
  
  
  class NumpySoftmax(mx.operator.NumpyOp):
      def need_top_grad(self):
          return False
      
      def list_arguments(self):
          return ['data', 'label']
  
      def list_outputs(self):
          return ['prob']
  
      def infer_shape(self, in_shape):
      	return [in_shape[0], (in_shape[0][0],)], [in_shape[0]]
  
      def forward(self, in_data, out_data):
      	x = in_data[0]
      	y = out_data[0]
      	y[:] = np.exp(x - x.max(axis=1).reshape((x.shape[0], 1)))
      	y /= y.sum(axis=1).reshape((x.shape[0], 1))
  
      def backward(self, out_grad, in_data, out_data, in_grad):
      	l = in_data[1]
      	l = l.reshape((l.size,)).astype(np.int)
      	y = out_data[0]
      	dx = in_grad[0]
      	dx[:] = y
      	dx[np.arange(l.shape[0]), l] -= 1.0
  
  # define mlp
  
  data = mx.symbol.Variable('data')
  fc1 = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128)
  act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type="relu")
  fc2 = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64)
  act2 = mx.symbol.Activation(data = fc2, name='relu2', act_type="relu")
  fc3 = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=10)
  #mlp = mx.symbol.Softmax(data = fc3, name = 'mlp')
  mysoftmax = NumpySoftmax()
  mlp = mysoftmax(data=fc3, name = 'mlp')
  
  # data
  
  train, val = mnist_iterator(batch_size=100, input_shape = (784,))
  
  # train
  
  logging.basicConfig(level=logging.DEBUG)
  
  model = mx.model.FeedForward(
      ctx = mx.cpu(), symbol = mlp, num_epoch = 20,
      learning_rate = 0.1, momentum = 0.9, wd = 0.00001)
  
  model.fit(X=train, eval_data=val)
  
  
subject: change backend in numpy_softmax to cpu