(inputs,
                      final_endpoint='Mixed_5c',
                      scope='InceptionV1')
Defines the Inception V1 base architecture.

  This architecture is defined in:
    Going deeper with convolutions
    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
    Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
    http://arxiv.org/pdf/1409.4842v1.pdf.

  Args:
    inputs: a tensor of size [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',
      'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
      'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']
    scope: Optional variable_scope.

  Returns:
    A dictionary from components of the network to the corresponding activation.

  Raises:
    ValueError: if final_endpoint is not set to one of the predefined values.
Defines the Inception V1 base architecture.
Defines the Inception V1 base architecture .
def inception_v1_base(inputs,
                      final_endpoint='Mixed_5c',
                      scope='InceptionV1'):
  """Defines the Inception V1 base architecture.

  This architecture is defined in:
    Going deeper with convolutions
    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
    Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
    http://arxiv.org/pdf/1409.4842v1.pdf.

  Args:
    inputs: a tensor of size [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',
      'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e',
      'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b', 'Mixed_5c']
    scope: Optional variable_scope.

  Returns:
    A dictionary from components of the network to the corresponding activation.

  Raises:
    ValueError: if final_endpoint is not set to one of the predefined values.
  """
  end_points = {}
  with tf.variable_scope(scope, 'InceptionV1', [inputs]):
    with slim.arg_scope(
        [slim.conv2d, slim.fully_connected],
        weights_initializer=trunc_normal(0.01)):
      with slim.arg_scope([slim.conv2d, slim.max_pool2d],
                          stride=1, padding='SAME'):
        end_point = 'Conv2d_1a_7x7'
        net = slim.conv2d(inputs, 64, [7, 7], stride=2, scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points
        end_point = 'MaxPool_2a_3x3'
        net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points
        end_point = 'Conv2d_2b_1x1'
        net = slim.conv2d(net, 64, [1, 1], scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points
        end_point = 'Conv2d_2c_3x3'
        net = slim.conv2d(net, 192, [3, 3], scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points
        end_point = 'MaxPool_3a_3x3'
        net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_3b'
        with tf.variable_scope(end_point):
          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 128, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 32, [3, 3], scope='Conv2d_0b_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, 32, [1, 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 final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_3c'
        with tf.variable_scope(end_point):
          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 192, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_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, 64, [1, 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 final_endpoint == end_point: return net, end_points

        end_point = 'MaxPool_4a_3x3'
        net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_4b'
        with tf.variable_scope(end_point):
          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 96, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 208, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 16, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 48, [3, 3], scope='Conv2d_0b_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, 64, [1, 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 final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_4c'
        with tf.variable_scope(end_point):
          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_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, 64, [1, 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 final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_4d'
        with tf.variable_scope(end_point):
          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 256, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 24, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_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, 64, [1, 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 final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_4e'
        with tf.variable_scope(end_point):
          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 112, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 144, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 288, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 64, [3, 3], scope='Conv2d_0b_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, 64, [1, 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 final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_4f'
        with tf.variable_scope(end_point):
          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_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, 128, [1, 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 final_endpoint == end_point: return net, end_points

        end_point = 'MaxPool_5a_2x2'
        net = slim.max_pool2d(net, [2, 2], stride=2, scope=end_point)
        end_points[end_point] = net
        if final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_5b'
        with tf.variable_scope(end_point):
          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 256, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 320, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 32, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0a_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, 128, [1, 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 final_endpoint == end_point: return net, end_points

        end_point = 'Mixed_5c'
        with tf.variable_scope(end_point):
          with tf.variable_scope('Branch_0'):
            branch_0 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
          with tf.variable_scope('Branch_1'):
            branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
            branch_1 = slim.conv2d(branch_1, 384, [3, 3], scope='Conv2d_0b_3x3')
          with tf.variable_scope('Branch_2'):
            branch_2 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
            branch_2 = slim.conv2d(branch_2, 128, [3, 3], scope='Conv2d_0b_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, 128, [1, 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 final_endpoint == end_point: return net, end_points
    raise ValueError('Unknown final endpoint %s' % final_endpoint)
def inception_v1_base ( inputs , final_endpoint = 'Mixed_5c' , scope = 'InceptionV1' ) : end_points = { } with tf . variable_scope ( scope , 'InceptionV1' , [ inputs ] ) : with slim . arg_scope ( [ slim . conv2d , slim . fully_connected ] , weights_initializer = trunc_normal ( 0.01 ) ) : with slim . arg_scope ( [ slim . conv2d , slim . max_pool2d ] , stride = 1 , padding = 'SAME' ) : end_point = 'Conv2d_1a_7x7' net = slim . conv2d ( inputs , 64 , [ 7 , 7 ] , stride = 2 , scope = end_point ) end_points [ end_point ] = net if final_endpoint == end_point : return net , end_points end_point = 'MaxPool_2a_3x3' net = slim . max_pool2d ( net , [ 3 , 3 ] , stride = 2 , scope = end_point ) end_points [ end_point ] = net if final_endpoint == end_point : return net , end_points end_point = 'Conv2d_2b_1x1' net = slim . conv2d ( net , 64 , [ 1 , 1 ] , scope = end_point ) end_points [ end_point ] = net if final_endpoint == end_point : return net , end_points end_point = 'Conv2d_2c_3x3' net = slim . conv2d ( net , 192 , [ 3 , 3 ] , scope = end_point ) end_points [ end_point ] = net if final_endpoint == end_point : return net , end_points end_point = 'MaxPool_3a_3x3' net = slim . max_pool2d ( net , [ 3 , 3 ] , stride = 2 , scope = end_point ) end_points [ end_point ] = net if final_endpoint == end_point : return net , end_points end_point = 'Mixed_3b' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , 64 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , 96 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , 128 , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , 16 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , 32 , [ 3 , 3 ] , scope = 'Conv2d_0b_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 , 32 , [ 1 , 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 final_endpoint == end_point : return net , end_points end_point = 'Mixed_3c' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , 128 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , 128 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , 192 , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , 32 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , 96 , [ 3 , 3 ] , scope = 'Conv2d_0b_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 , 64 , [ 1 , 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 final_endpoint == end_point : return net , end_points end_point = 'MaxPool_4a_3x3' net = slim . max_pool2d ( net , [ 3 , 3 ] , stride = 2 , scope = end_point ) end_points [ end_point ] = net if final_endpoint == end_point : return net , end_points end_point = 'Mixed_4b' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , 192 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , 96 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , 208 , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , 16 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , 48 , [ 3 , 3 ] , scope = 'Conv2d_0b_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 , 64 , [ 1 , 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 final_endpoint == end_point : return net , end_points end_point = 'Mixed_4c' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , 160 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , 112 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , 224 , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , 24 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , 64 , [ 3 , 3 ] , scope = 'Conv2d_0b_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 , 64 , [ 1 , 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 final_endpoint == end_point : return net , end_points end_point = 'Mixed_4d' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , 128 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , 128 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , 256 , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , 24 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , 64 , [ 3 , 3 ] , scope = 'Conv2d_0b_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 , 64 , [ 1 , 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 final_endpoint == end_point : return net , end_points end_point = 'Mixed_4e' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , 112 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , 144 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , 288 , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , 32 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , 64 , [ 3 , 3 ] , scope = 'Conv2d_0b_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 , 64 , [ 1 , 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 final_endpoint == end_point : return net , end_points end_point = 'Mixed_4f' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , 256 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , 160 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , 320 , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , 32 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , 128 , [ 3 , 3 ] , scope = 'Conv2d_0b_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 , 128 , [ 1 , 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 final_endpoint == end_point : return net , end_points end_point = 'MaxPool_5a_2x2' net = slim . max_pool2d ( net , [ 2 , 2 ] , stride = 2 , scope = end_point ) end_points [ end_point ] = net if final_endpoint == end_point : return net , end_points end_point = 'Mixed_5b' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , 256 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , 160 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , 320 , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , 32 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , 128 , [ 3 , 3 ] , scope = 'Conv2d_0a_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 , 128 , [ 1 , 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 final_endpoint == end_point : return net , end_points end_point = 'Mixed_5c' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , 384 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , 192 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , 384 , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , 48 , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , 128 , [ 3 , 3 ] , scope = 'Conv2d_0b_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 , 128 , [ 1 , 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 final_endpoint == end_point : return net , end_points raise ValueError ( 'Unknown final endpoint %s' % final_endpoint )