(inputs,
                      final_endpoint='Mixed_5c',
                      min_depth=16,
                      depth_multiplier=1.0,
                      scope=None)
Inception v2 (6a2).

  Constructs an Inception v2 network from inputs to the given final endpoint.
  This method can construct the network up to the layer inception(5b) as
  described in http://arxiv.org/abs/1502.03167.

  Args:
    inputs: a tensor of shape [batch_size, height, width, channels].
    final_endpoint: specifies the endpoint to construct the network up to. It
      can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
      'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'Mixed_4a',
      'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', 'Mixed_5b',
      'Mixed_5c'].
    min_depth: Minimum depth value (number of channels) for all convolution ops.
      Enforced when depth_multiplier < 1, and not an active constraint when
      depth_multiplier >= 1.
    depth_multiplier: Float multiplier for the depth (number of channels)
      for all convolution ops. The value must be greater than zero. Typical
      usage will be to set this value in (0, 1) to reduce the number of
      parameters or computation cost of the model.
    scope: Optional variable_scope.

  Returns:
    tensor_out: output tensor corresponding to the final_endpoint.
    end_points: a set of activations for external use, for example summaries or
                losses.

  Raises:
    ValueError: if final_endpoint is not set to one of the predefined values,
                or depth_multiplier <= 0
Inception v2 (6a2).
Inception v2 ( 6a2 ) .
def inception_v2_base(inputs,
                      final_endpoint='Mixed_5c',
                      min_depth=16,
                      depth_multiplier=1.0,
                      scope=None):
  """Inception v2 (6a2).

  Constructs an Inception v2 network from inputs to the given final endpoint.
  This method can construct the network up to the layer inception(5b) as
  described in http://arxiv.org/abs/1502.03167.

  Args:
    inputs: a tensor of shape [batch_size, height, width, channels].
    final_endpoint: specifies the endpoint to construct the network up to. It
      can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
      'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'Mixed_4a',
      'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', 'Mixed_5b',
      'Mixed_5c'].
    min_depth: Minimum depth value (number of channels) for all convolution ops.
      Enforced when depth_multiplier < 1, and not an active constraint when
      depth_multiplier >= 1.
    depth_multiplier: Float multiplier for the depth (number of channels)
      for all convolution ops. The value must be greater than zero. Typical
      usage will be to set this value in (0, 1) to reduce the number of
      parameters or computation cost of the model.
    scope: Optional variable_scope.

  Returns:
    tensor_out: output tensor corresponding to the final_endpoint.
    end_points: a set of activations for external use, for example summaries or
                losses.

  Raises:
    ValueError: if final_endpoint is not set to one of the predefined values,
                or depth_multiplier <= 0
  """

  # end_points will collect relevant activations for external use, for example
  # summaries or losses.
  end_points = {}

  # Used to find thinned depths for each layer.
  if depth_multiplier <= 0:
    raise ValueError('depth_multiplier is not greater than zero.')
  depth = lambda d: max(int(d * depth_multiplier), min_depth)

  with tf.variable_scope(scope, 'InceptionV2', [inputs]):
    with slim.arg_scope(
        [slim.conv2d, slim.max_pool2d, slim.avg_pool2d, slim.separable_conv2d],
        stride=1, padding='SAME'):

      # Note that sizes in the comments below assume an input spatial size of
      # 224x224, however, the inputs can be of any size greater 32x32.

      # 224 x 224 x 3
      end_point = 'Conv2d_1a_7x7'
      # depthwise_multiplier here is different from depth_multiplier.
      # depthwise_multiplier determines the output channels of the initial
      # depthwise conv (see docs for tf.nn.separable_conv2d), while
      # depth_multiplier controls the # channels of the subsequent 1x1
      # convolution. Must have
      #   in_channels * depthwise_multipler <= out_channels
      # so that the separable convolution is not overparameterized.
      depthwise_multiplier = min(int(depth(64) / 3), 8)
      net = slim.separable_conv2d(
          inputs, depth(64), [7, 7], depth_multiplier=depthwise_multiplier,
          stride=2, weights_initializer=trunc_normal(1.0),
          scope=end_point)
      end_points[end_point] = net
      if end_point == final_endpoint: return net, end_points
      # 112 x 112 x 64
      end_point = 'MaxPool_2a_3x3'
      net = slim.max_pool2d(net, [3, 3], scope=end_point, stride=2)
      end_points[end_point] = net
      if end_point == final_endpoint: return net, end_points
      # 56 x 56 x 64
      end_point = 'Conv2d_2b_1x1'
      net = slim.conv2d(net, depth(64), [1, 1], scope=end_point,
                        weights_initializer=trunc_normal(0.1))
      end_points[end_point] = net
      if end_point == final_endpoint: return net, end_points
      # 56 x 56 x 64
      end_point = 'Conv2d_2c_3x3'
      net = slim.conv2d(net, depth(192), [3, 3], scope=end_point)
      end_points[end_point] = net
      if end_point == final_endpoint: return net, end_points
      # 56 x 56 x 192
      end_point = 'MaxPool_3a_3x3'
      net = slim.max_pool2d(net, [3, 3], scope=end_point, stride=2)
      end_points[end_point] = net
      if end_point == final_endpoint: return net, end_points
      # 28 x 28 x 192
      # Inception module.
      end_point = 'Mixed_3b'
      with tf.variable_scope(end_point):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(64), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(64), [3, 3],
                                 scope='Conv2d_0b_3x3')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(64), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
                                 scope='Conv2d_0c_3x3')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(32), [1, 1],
              weights_initializer=trunc_normal(0.1),
              scope='Conv2d_0b_1x1')
        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if end_point == final_endpoint: return net, end_points
      # 28 x 28 x 256
      end_point = 'Mixed_3c'
      with tf.variable_scope(end_point):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(64), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(96), [3, 3],
                                 scope='Conv2d_0b_3x3')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(64), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
                                 scope='Conv2d_0c_3x3')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(64), [1, 1],
              weights_initializer=trunc_normal(0.1),
              scope='Conv2d_0b_1x1')
        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if end_point == final_endpoint: return net, end_points
      # 28 x 28 x 320
      end_point = 'Mixed_4a'
      with tf.variable_scope(end_point):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(
              net, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_0 = slim.conv2d(branch_0, depth(160), [3, 3], stride=2,
                                 scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(64), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(
              branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3')
          branch_1 = slim.conv2d(
              branch_1, depth(96), [3, 3], stride=2, scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.max_pool2d(
              net, [3, 3], stride=2, scope='MaxPool_1a_3x3')
        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
        end_points[end_point] = net
        if end_point == final_endpoint: return net, end_points
      # 14 x 14 x 576
      end_point = 'Mixed_4b'
      with tf.variable_scope(end_point):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(224), [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(64), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(
              branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(96), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
                                 scope='Conv2d_0c_3x3')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.1),
              scope='Conv2d_0b_1x1')
        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if end_point == final_endpoint: return net, end_points
      # 14 x 14 x 576
      end_point = 'Mixed_4c'
      with tf.variable_scope(end_point):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(96), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(128), [3, 3],
                                 scope='Conv2d_0b_3x3')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(96), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(128), [3, 3],
                                 scope='Conv2d_0c_3x3')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.1),
              scope='Conv2d_0b_1x1')
        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if end_point == final_endpoint: return net, end_points
      # 14 x 14 x 576
      end_point = 'Mixed_4d'
      with tf.variable_scope(end_point):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(160), [3, 3],
                                 scope='Conv2d_0b_3x3')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, depth(160), [3, 3],
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(160), [3, 3],
                                 scope='Conv2d_0c_3x3')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(96), [1, 1],
              weights_initializer=trunc_normal(0.1),
              scope='Conv2d_0b_1x1')
        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if end_point == final_endpoint: return net, end_points

      # 14 x 14 x 576
      end_point = 'Mixed_4e'
      with tf.variable_scope(end_point):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(96), [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(192), [3, 3],
                                 scope='Conv2d_0b_3x3')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(160), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, depth(192), [3, 3],
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(192), [3, 3],
                                 scope='Conv2d_0c_3x3')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(96), [1, 1],
              weights_initializer=trunc_normal(0.1),
              scope='Conv2d_0b_1x1')
        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if end_point == final_endpoint: return net, end_points
      # 14 x 14 x 576
      end_point = 'Mixed_5a'
      with tf.variable_scope(end_point):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(
              net, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_0 = slim.conv2d(branch_0, depth(192), [3, 3], stride=2,
                                 scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(192), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(256), [3, 3],
                                 scope='Conv2d_0b_3x3')
          branch_1 = slim.conv2d(branch_1, depth(256), [3, 3], stride=2,
                                 scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.max_pool2d(net, [3, 3], stride=2,
                                     scope='MaxPool_1a_3x3')
        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
        end_points[end_point] = net
        if end_point == final_endpoint: return net, end_points
      # 7 x 7 x 1024
      end_point = 'Mixed_5b'
      with tf.variable_scope(end_point):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(352), [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(192), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(320), [3, 3],
                                 scope='Conv2d_0b_3x3')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(160), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
                                 scope='Conv2d_0c_3x3')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.1),
              scope='Conv2d_0b_1x1')
        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if end_point == final_endpoint: return net, end_points

      # 7 x 7 x 1024
      end_point = 'Mixed_5c'
      with tf.variable_scope(end_point):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(352), [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(
              net, depth(192), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(320), [3, 3],
                                 scope='Conv2d_0b_3x3')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(
              net, depth(192), [1, 1],
              weights_initializer=trunc_normal(0.09),
              scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
                                 scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, depth(224), [3, 3],
                                 scope='Conv2d_0c_3x3')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, depth(128), [1, 1],
              weights_initializer=trunc_normal(0.1),
              scope='Conv2d_0b_1x1')
        net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
        end_points[end_point] = net
        if end_point == final_endpoint: return net, end_points
    raise ValueError('Unknown final endpoint %s' % final_endpoint)
def inception_v2_base ( inputs , final_endpoint = 'Mixed_5c' , min_depth = 16 , depth_multiplier = 1.0 , scope = None ) : # end_points will collect relevant activations for external use, for example # summaries or losses. end_points = { } # Used to find thinned depths for each layer. if depth_multiplier <= 0 : raise ValueError ( 'depth_multiplier is not greater than zero.' ) depth = lambda d : max ( int ( d * depth_multiplier ) , min_depth ) with tf . variable_scope ( scope , 'InceptionV2' , [ inputs ] ) : with slim . arg_scope ( [ slim . conv2d , slim . max_pool2d , slim . avg_pool2d , slim . separable_conv2d ] , stride = 1 , padding = 'SAME' ) : # Note that sizes in the comments below assume an input spatial size of # 224x224, however, the inputs can be of any size greater 32x32. # 224 x 224 x 3 end_point = 'Conv2d_1a_7x7' # depthwise_multiplier here is different from depth_multiplier. # depthwise_multiplier determines the output channels of the initial # depthwise conv (see docs for tf.nn.separable_conv2d), while # depth_multiplier controls the # channels of the subsequent 1x1 # convolution. Must have #   in_channels * depthwise_multipler <= out_channels # so that the separable convolution is not overparameterized. depthwise_multiplier = min ( int ( depth ( 64 ) / 3 ) , 8 ) net = slim . separable_conv2d ( inputs , depth ( 64 ) , [ 7 , 7 ] , depth_multiplier = depthwise_multiplier , stride = 2 , weights_initializer = trunc_normal ( 1.0 ) , scope = end_point ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 112 x 112 x 64 end_point = 'MaxPool_2a_3x3' net = slim . max_pool2d ( net , [ 3 , 3 ] , scope = end_point , stride = 2 ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 56 x 56 x 64 end_point = 'Conv2d_2b_1x1' net = slim . conv2d ( net , depth ( 64 ) , [ 1 , 1 ] , scope = end_point , weights_initializer = trunc_normal ( 0.1 ) ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 56 x 56 x 64 end_point = 'Conv2d_2c_3x3' net = slim . conv2d ( net , depth ( 192 ) , [ 3 , 3 ] , scope = end_point ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 56 x 56 x 192 end_point = 'MaxPool_3a_3x3' net = slim . max_pool2d ( net , [ 3 , 3 ] , scope = end_point , stride = 2 ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 28 x 28 x 192 # Inception module. end_point = 'Mixed_3b' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , depth ( 64 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 64 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 64 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 64 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 96 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) branch_2 = slim . conv2d ( branch_2 , depth ( 96 ) , [ 3 , 3 ] , scope = 'Conv2d_0c_3x3' ) with tf . variable_scope ( 'Branch_3' ) : branch_3 = slim . avg_pool2d ( net , [ 3 , 3 ] , scope = 'AvgPool_0a_3x3' ) branch_3 = slim . conv2d ( branch_3 , depth ( 32 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.1 ) , scope = 'Conv2d_0b_1x1' ) net = tf . concat ( axis = 3 , values = [ branch_0 , branch_1 , branch_2 , branch_3 ] ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 28 x 28 x 256 end_point = 'Mixed_3c' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , depth ( 64 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 64 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 96 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 64 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 96 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) branch_2 = slim . conv2d ( branch_2 , depth ( 96 ) , [ 3 , 3 ] , scope = 'Conv2d_0c_3x3' ) with tf . variable_scope ( 'Branch_3' ) : branch_3 = slim . avg_pool2d ( net , [ 3 , 3 ] , scope = 'AvgPool_0a_3x3' ) branch_3 = slim . conv2d ( branch_3 , depth ( 64 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.1 ) , scope = 'Conv2d_0b_1x1' ) net = tf . concat ( axis = 3 , values = [ branch_0 , branch_1 , branch_2 , branch_3 ] ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 28 x 28 x 320 end_point = 'Mixed_4a' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , depth ( 128 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_0 = slim . conv2d ( branch_0 , depth ( 160 ) , [ 3 , 3 ] , stride = 2 , scope = 'Conv2d_1a_3x3' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 64 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 96 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) branch_1 = slim . conv2d ( branch_1 , depth ( 96 ) , [ 3 , 3 ] , stride = 2 , scope = 'Conv2d_1a_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . max_pool2d ( net , [ 3 , 3 ] , stride = 2 , scope = 'MaxPool_1a_3x3' ) net = tf . concat ( axis = 3 , values = [ branch_0 , branch_1 , branch_2 ] ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 14 x 14 x 576 end_point = 'Mixed_4b' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , depth ( 224 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 64 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 96 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 96 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 128 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) branch_2 = slim . conv2d ( branch_2 , depth ( 128 ) , [ 3 , 3 ] , scope = 'Conv2d_0c_3x3' ) with tf . variable_scope ( 'Branch_3' ) : branch_3 = slim . avg_pool2d ( net , [ 3 , 3 ] , scope = 'AvgPool_0a_3x3' ) branch_3 = slim . conv2d ( branch_3 , depth ( 128 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.1 ) , scope = 'Conv2d_0b_1x1' ) net = tf . concat ( axis = 3 , values = [ branch_0 , branch_1 , branch_2 , branch_3 ] ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 14 x 14 x 576 end_point = 'Mixed_4c' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , depth ( 192 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 96 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 128 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 96 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 128 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) branch_2 = slim . conv2d ( branch_2 , depth ( 128 ) , [ 3 , 3 ] , scope = 'Conv2d_0c_3x3' ) with tf . variable_scope ( 'Branch_3' ) : branch_3 = slim . avg_pool2d ( net , [ 3 , 3 ] , scope = 'AvgPool_0a_3x3' ) branch_3 = slim . conv2d ( branch_3 , depth ( 128 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.1 ) , scope = 'Conv2d_0b_1x1' ) net = tf . concat ( axis = 3 , values = [ branch_0 , branch_1 , branch_2 , branch_3 ] ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 14 x 14 x 576 end_point = 'Mixed_4d' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , depth ( 160 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 128 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 160 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 128 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 160 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) branch_2 = slim . conv2d ( branch_2 , depth ( 160 ) , [ 3 , 3 ] , scope = 'Conv2d_0c_3x3' ) with tf . variable_scope ( 'Branch_3' ) : branch_3 = slim . avg_pool2d ( net , [ 3 , 3 ] , scope = 'AvgPool_0a_3x3' ) branch_3 = slim . conv2d ( branch_3 , depth ( 96 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.1 ) , scope = 'Conv2d_0b_1x1' ) net = tf . concat ( axis = 3 , values = [ branch_0 , branch_1 , branch_2 , branch_3 ] ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 14 x 14 x 576 end_point = 'Mixed_4e' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , depth ( 96 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 128 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 192 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 160 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 192 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) branch_2 = slim . conv2d ( branch_2 , depth ( 192 ) , [ 3 , 3 ] , scope = 'Conv2d_0c_3x3' ) with tf . variable_scope ( 'Branch_3' ) : branch_3 = slim . avg_pool2d ( net , [ 3 , 3 ] , scope = 'AvgPool_0a_3x3' ) branch_3 = slim . conv2d ( branch_3 , depth ( 96 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.1 ) , scope = 'Conv2d_0b_1x1' ) net = tf . concat ( axis = 3 , values = [ branch_0 , branch_1 , branch_2 , branch_3 ] ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 14 x 14 x 576 end_point = 'Mixed_5a' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , depth ( 128 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_0 = slim . conv2d ( branch_0 , depth ( 192 ) , [ 3 , 3 ] , stride = 2 , scope = 'Conv2d_1a_3x3' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 192 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 256 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) branch_1 = slim . conv2d ( branch_1 , depth ( 256 ) , [ 3 , 3 ] , stride = 2 , scope = 'Conv2d_1a_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . max_pool2d ( net , [ 3 , 3 ] , stride = 2 , scope = 'MaxPool_1a_3x3' ) net = tf . concat ( axis = 3 , values = [ branch_0 , branch_1 , branch_2 ] ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 7 x 7 x 1024 end_point = 'Mixed_5b' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , depth ( 352 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 192 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 320 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 160 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 224 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) branch_2 = slim . conv2d ( branch_2 , depth ( 224 ) , [ 3 , 3 ] , scope = 'Conv2d_0c_3x3' ) with tf . variable_scope ( 'Branch_3' ) : branch_3 = slim . avg_pool2d ( net , [ 3 , 3 ] , scope = 'AvgPool_0a_3x3' ) branch_3 = slim . conv2d ( branch_3 , depth ( 128 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.1 ) , scope = 'Conv2d_0b_1x1' ) net = tf . concat ( axis = 3 , values = [ branch_0 , branch_1 , branch_2 , branch_3 ] ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 7 x 7 x 1024 end_point = 'Mixed_5c' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , depth ( 352 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 192 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 320 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 192 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.09 ) , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 224 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) branch_2 = slim . conv2d ( branch_2 , depth ( 224 ) , [ 3 , 3 ] , scope = 'Conv2d_0c_3x3' ) with tf . variable_scope ( 'Branch_3' ) : branch_3 = slim . max_pool2d ( net , [ 3 , 3 ] , scope = 'MaxPool_0a_3x3' ) branch_3 = slim . conv2d ( branch_3 , depth ( 128 ) , [ 1 , 1 ] , weights_initializer = trunc_normal ( 0.1 ) , scope = 'Conv2d_0b_1x1' ) net = tf . concat ( axis = 3 , values = [ branch_0 , branch_1 , branch_2 , branch_3 ] ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points raise ValueError ( 'Unknown final endpoint %s' % final_endpoint )