text: """
  Inception V3, suitable for images with around 299 x 299
  Reference:
  Szegedy, Christian, et al. "Rethinking the Inception Architecture for Computer Vision." arXiv preprint arXiv:1512.00567 (2015).
  """
  
  import sys
  sys.path.append('/home/ubuntu/yelp/mxnet/python')
  import mxnet as mx
  
  
  def Conv(data, num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name=None, suffix=''):
      conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))
      bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True)
      act = mx.sym.Activation(data=bn, act_type='relu', name='%s%s_relu' %(name, suffix))
      return act
  
  
  def Inception7A(data,
                  num_1x1,
                  num_3x3_red, num_3x3_1, num_3x3_2,
                  num_5x5_red, num_5x5,
                  pool, proj,
                  name):
      tower_1x1 = Conv(data, num_1x1, name=('%s_conv' % name))
      tower_5x5 = Conv(data, num_5x5_red, name=('%s_tower' % name), suffix='_conv')
      tower_5x5 = Conv(tower_5x5, num_5x5, kernel=(5, 5), pad=(2, 2), name=('%s_tower' % name), suffix='_conv_1')
      tower_3x3 = Conv(data, num_3x3_red, name=('%s_tower_1' % name), suffix='_conv')
      tower_3x3 = Conv(tower_3x3, num_3x3_1, kernel=(3, 3), pad=(1, 1), name=('%s_tower_1' % name), suffix='_conv_1')
      tower_3x3 = Conv(tower_3x3, num_3x3_2, kernel=(3, 3), pad=(1, 1), name=('%s_tower_1' % name), suffix='_conv_2')
      pooling = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool, name=('%s_pool_%s_pool' % (pool, name)))
      cproj = Conv(pooling, proj, name=('%s_tower_2' %  name), suffix='_conv')
      concat = mx.sym.Concat(*[tower_1x1, tower_5x5, tower_3x3, cproj], name='ch_concat_%s_chconcat' % name)
      return concat
  
  # First Downsample
  def Inception7B(data,
                  num_3x3,
                  num_d3x3_red, num_d3x3_1, num_d3x3_2,
                  pool,
                  name):
      tower_3x3 = Conv(data, num_3x3, kernel=(3, 3), pad=(0, 0), stride=(2, 2), name=('%s_conv' % name))
      tower_d3x3 = Conv(data, num_d3x3_red, name=('%s_tower' % name), suffix='_conv')
      tower_d3x3 = Conv(tower_d3x3, num_d3x3_1, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=('%s_tower' % name), suffix='_conv_1')
      tower_d3x3 = Conv(tower_d3x3, num_d3x3_2, kernel=(3, 3), pad=(0, 0), stride=(2, 2), name=('%s_tower' % name), suffix='_conv_2')
      pooling = mx.symbol.Pooling(data=data, kernel=(3, 3), stride=(2, 2), pad=(0,0), pool_type="max", name=('max_pool_%s_pool' % name))
      concat = mx.sym.Concat(*[tower_3x3, tower_d3x3, pooling], name='ch_concat_%s_chconcat' % name)
      return concat
  
  def Inception7C(data,
                  num_1x1,
                  num_d7_red, num_d7_1, num_d7_2,
                  num_q7_red, num_q7_1, num_q7_2, num_q7_3, num_q7_4,
                  pool, proj,
                  name):
      tower_1x1 = Conv(data=data, num_filter=num_1x1, kernel=(1, 1), name=('%s_conv' % name))
      tower_d7 = Conv(data=data, num_filter=num_d7_red, name=('%s_tower' % name), suffix='_conv')
      tower_d7 = Conv(data=tower_d7, num_filter=num_d7_1, kernel=(1, 7), pad=(0, 3), name=('%s_tower' % name), suffix='_conv_1')
      tower_d7 = Conv(data=tower_d7, num_filter=num_d7_2, kernel=(7, 1), pad=(3, 0), name=('%s_tower' % name), suffix='_conv_2')
      tower_q7 = Conv(data=data, num_filter=num_q7_red, name=('%s_tower_1' % name), suffix='_conv')
      tower_q7 = Conv(data=tower_q7, num_filter=num_q7_1, kernel=(7, 1), pad=(3, 0), name=('%s_tower_1' % name), suffix='_conv_1')
      tower_q7 = Conv(data=tower_q7, num_filter=num_q7_2, kernel=(1, 7), pad=(0, 3), name=('%s_tower_1' % name), suffix='_conv_2')
      tower_q7 = Conv(data=tower_q7, num_filter=num_q7_3, kernel=(7, 1), pad=(3, 0), name=('%s_tower_1' % name), suffix='_conv_3')
      tower_q7 = Conv(data=tower_q7, num_filter=num_q7_4, kernel=(1, 7), pad=(0, 3), name=('%s_tower_1' % name), suffix='_conv_4')
      pooling = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool, name=('%s_pool_%s_pool' % (pool, name)))
      cproj = Conv(data=pooling, num_filter=proj, kernel=(1, 1), name=('%s_tower_2' %  name), suffix='_conv')
      # concat
      concat = mx.sym.Concat(*[tower_1x1, tower_d7, tower_q7, cproj], name='ch_concat_%s_chconcat' % name)
      return concat
  
  def Inception7D(data,
                  num_3x3_red, num_3x3,
                  num_d7_3x3_red, num_d7_1, num_d7_2, num_d7_3x3,
                  pool,
                  name):
      tower_3x3 = Conv(data=data, num_filter=num_3x3_red, name=('%s_tower' % name), suffix='_conv')
      tower_3x3 = Conv(data=tower_3x3, num_filter=num_3x3, kernel=(3, 3), pad=(0,0), stride=(2, 2), name=('%s_tower' % name), suffix='_conv_1')
      tower_d7_3x3 = Conv(data=data, num_filter=num_d7_3x3_red, name=('%s_tower_1' % name), suffix='_conv')
      tower_d7_3x3 = Conv(data=tower_d7_3x3, num_filter=num_d7_1, kernel=(1, 7), pad=(0, 3), name=('%s_tower_1' % name), suffix='_conv_1')
      tower_d7_3x3 = Conv(data=tower_d7_3x3, num_filter=num_d7_2, kernel=(7, 1), pad=(3, 0), name=('%s_tower_1' % name), suffix='_conv_2')
      tower_d7_3x3 = Conv(data=tower_d7_3x3, num_filter=num_d7_3x3, kernel=(3, 3), stride=(2, 2), name=('%s_tower_1' % name), suffix='_conv_3')
      pooling = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(2, 2), pool_type=pool, name=('%s_pool_%s_pool' % (pool, name)))
      # concat
      concat = mx.sym.Concat(*[tower_3x3, tower_d7_3x3, pooling], name='ch_concat_%s_chconcat' % name)
      return concat
  
  def Inception7E(data,
                  num_1x1,
                  num_d3_red, num_d3_1, num_d3_2,
                  num_3x3_d3_red, num_3x3, num_3x3_d3_1, num_3x3_d3_2,
                  pool, proj,
                  name):
      tower_1x1 = Conv(data=data, num_filter=num_1x1, kernel=(1, 1), name=('%s_conv' % name))
      tower_d3 = Conv(data=data, num_filter=num_d3_red, name=('%s_tower' % name), suffix='_conv')
      tower_d3_a = Conv(data=tower_d3, num_filter=num_d3_1, kernel=(1, 3), pad=(0, 1), name=('%s_tower' % name), suffix='_mixed_conv')
      tower_d3_b = Conv(data=tower_d3, num_filter=num_d3_2, kernel=(3, 1), pad=(1, 0), name=('%s_tower' % name), suffix='_mixed_conv_1')
      tower_3x3_d3 = Conv(data=data, num_filter=num_3x3_d3_red, name=('%s_tower_1' % name), suffix='_conv')
      tower_3x3_d3 = Conv(data=tower_3x3_d3, num_filter=num_3x3, kernel=(3, 3), pad=(1, 1), name=('%s_tower_1' % name), suffix='_conv_1')
      tower_3x3_d3_a = Conv(data=tower_3x3_d3, num_filter=num_3x3_d3_1, kernel=(1, 3), pad=(0, 1), name=('%s_tower_1' % name), suffix='_mixed_conv')
      tower_3x3_d3_b = Conv(data=tower_3x3_d3, num_filter=num_3x3_d3_2, kernel=(3, 1), pad=(1, 0), name=('%s_tower_1' % name), suffix='_mixed_conv_1')
      pooling = mx.sym.Pooling(data=data, kernel=(3, 3), stride=(1, 1), pad=(1, 1), pool_type=pool, name=('%s_pool_%s_pool' % (pool, name)))
      cproj = Conv(data=pooling, num_filter=proj, kernel=(1, 1), name=('%s_tower_2' %  name), suffix='_conv')
      # concat
      concat = mx.sym.Concat(*[tower_1x1, tower_d3_a, tower_d3_b, tower_3x3_d3_a, tower_3x3_d3_b, cproj], name='ch_concat_%s_chconcat' % name)
      return concat
  
  # In[49]:
  
  def get_symbol(num_classes):
      data = mx.symbol.Variable(name="data")
      # stage 1
      conv = Conv(data, 32, kernel=(3, 3), stride=(2, 2), name="conv")
      conv_1 = Conv(conv, 32, kernel=(3, 3), name="conv_1")
      conv_2 = Conv(conv_1, 64, kernel=(3, 3), pad=(1, 1), name="conv_2")
      pool = mx.sym.Pooling(data=conv_2, kernel=(3, 3), stride=(2, 2), pool_type="max", name="pool")
      # stage 2
      conv_3 = Conv(pool, 80, kernel=(1, 1), name="conv_3")
      conv_4 = Conv(conv_3, 192, kernel=(3, 3), name="conv_4")
      pool1 = mx.sym.Pooling(data=conv_4, kernel=(3, 3), stride=(2, 2), pool_type="max", name="pool1")
      # stage 3
      in3a = Inception7A(pool1, 64,
                         64, 96, 96,
                         48, 64,
                         "avg", 32, "mixed")
      in3b = Inception7A(in3a, 64,
                         64, 96, 96,
                         48, 64,
                         "avg", 64, "mixed_1")
      in3c = Inception7A(in3b, 64,
                         64, 96, 96,
                         48, 64,
                         "avg", 64, "mixed_2")
      in3d = Inception7B(in3c, 384,
                         64, 96, 96,
                         "max", "mixed_3")
      # stage 4
      in4a = Inception7C(in3d, 192,
                         128, 128, 192,
                         128, 128, 128, 128, 192,
                         "avg", 192, "mixed_4")
      in4b = Inception7C(in4a, 192,
                         160, 160, 192,
                         160, 160, 160, 160, 192,
                         "avg", 192, "mixed_5")
      in4c = Inception7C(in4b, 192,
                         160, 160, 192,
                         160, 160, 160, 160, 192,
                         "avg", 192, "mixed_6")
      in4d = Inception7C(in4c, 192,
                         192, 192, 192,
                         192, 192, 192, 192, 192,
                         "avg", 192, "mixed_7")
      in4e = Inception7D(in4d, 192, 320,
                         192, 192, 192, 192,
                         "max", "mixed_8")
      # stage 5
      in5a = Inception7E(in4e, 320,
                         384, 384, 384,
                         448, 384, 384, 384,
                         "avg", 192, "mixed_9")
      in5b = Inception7E(in5a, 320,
                         384, 384, 384,
                         448, 384, 384, 384,
                         "max", 192, "mixed_10")
      # pool
      pool = mx.sym.Pooling(data=in5b, kernel=(8, 8), stride=(1, 1), pool_type="avg", name="global_pool")
      flatten = mx.sym.Flatten(data=pool, name="flatten")
      fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=num_classes, name='fcn1')  # renamed from fc1
      softmax = mx.symbol.LogisticRegressionOutput(data=fc1, name='softmax')
      return softmax
  