(inputs,
                      final_endpoint='Mixed_7c',
                      min_depth=16,
                      depth_multiplier=1.0,
                      scope=None)
Inception model from http://arxiv.org/abs/1512.00567.

  Constructs an Inception v3 network from inputs to the given final endpoint.
  This method can construct the network up to the final inception block
  Mixed_7c.

  Note that the names of the layers in the paper do not correspond to the names
  of the endpoints registered by this function although they build the same
  network.

  Here is a mapping from the old_names to the new names:
  Old name          | New name
  =======================================
  conv0             | Conv2d_1a_3x3
  conv1             | Conv2d_2a_3x3
  conv2             | Conv2d_2b_3x3
  pool1             | MaxPool_3a_3x3
  conv3             | Conv2d_3b_1x1
  conv4             | Conv2d_4a_3x3
  pool2             | MaxPool_5a_3x3
  mixed_35x35x256a  | Mixed_5b
  mixed_35x35x288a  | Mixed_5c
  mixed_35x35x288b  | Mixed_5d
  mixed_17x17x768a  | Mixed_6a
  mixed_17x17x768b  | Mixed_6b
  mixed_17x17x768c  | Mixed_6c
  mixed_17x17x768d  | Mixed_6d
  mixed_17x17x768e  | Mixed_6e
  mixed_8x8x1280a   | Mixed_7a
  mixed_8x8x2048a   | Mixed_7b
  mixed_8x8x2048b   | Mixed_7c

  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_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
      'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
      'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c',
      'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'].
    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 model from http://arxiv.org/abs/1512.00567.
Inception model from http : // arxiv . org / abs / 1512 . 00567 .
def inception_v3_base(inputs,
                      final_endpoint='Mixed_7c',
                      min_depth=16,
                      depth_multiplier=1.0,
                      scope=None):
  """Inception model from http://arxiv.org/abs/1512.00567.

  Constructs an Inception v3 network from inputs to the given final endpoint.
  This method can construct the network up to the final inception block
  Mixed_7c.

  Note that the names of the layers in the paper do not correspond to the names
  of the endpoints registered by this function although they build the same
  network.

  Here is a mapping from the old_names to the new names:
  Old name          | New name
  =======================================
  conv0             | Conv2d_1a_3x3
  conv1             | Conv2d_2a_3x3
  conv2             | Conv2d_2b_3x3
  pool1             | MaxPool_3a_3x3
  conv3             | Conv2d_3b_1x1
  conv4             | Conv2d_4a_3x3
  pool2             | MaxPool_5a_3x3
  mixed_35x35x256a  | Mixed_5b
  mixed_35x35x288a  | Mixed_5c
  mixed_35x35x288b  | Mixed_5d
  mixed_17x17x768a  | Mixed_6a
  mixed_17x17x768b  | Mixed_6b
  mixed_17x17x768c  | Mixed_6c
  mixed_17x17x768d  | Mixed_6d
  mixed_17x17x768e  | Mixed_6e
  mixed_8x8x1280a   | Mixed_7a
  mixed_8x8x2048a   | Mixed_7b
  mixed_8x8x2048b   | Mixed_7c

  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_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
      'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
      'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c',
      'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'].
    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 = {}

  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, 'InceptionV3', [inputs]):
    with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                        stride=1, padding='VALID'):
      # 299 x 299 x 3
      end_point = 'Conv2d_1a_3x3'
      net = slim.conv2d(inputs, depth(32), [3, 3], stride=2, scope=end_point)
      end_points[end_point] = net
      if end_point == final_endpoint: return net, end_points
      # 149 x 149 x 32
      end_point = 'Conv2d_2a_3x3'
      net = slim.conv2d(net, depth(32), [3, 3], scope=end_point)
      end_points[end_point] = net
      if end_point == final_endpoint: return net, end_points
      # 147 x 147 x 32
      end_point = 'Conv2d_2b_3x3'
      net = slim.conv2d(net, depth(64), [3, 3], padding='SAME', scope=end_point)
      end_points[end_point] = net
      if end_point == final_endpoint: return net, end_points
      # 147 x 147 x 64
      end_point = 'MaxPool_3a_3x3'
      net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
      end_points[end_point] = net
      if end_point == final_endpoint: return net, end_points
      # 73 x 73 x 64
      end_point = 'Conv2d_3b_1x1'
      net = slim.conv2d(net, depth(80), [1, 1], scope=end_point)
      end_points[end_point] = net
      if end_point == final_endpoint: return net, end_points
      # 73 x 73 x 80.
      end_point = 'Conv2d_4a_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
      # 71 x 71 x 192.
      end_point = 'MaxPool_5a_3x3'
      net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
      end_points[end_point] = net
      if end_point == final_endpoint: return net, end_points
      # 35 x 35 x 192.

    # Inception blocks
    with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                        stride=1, padding='SAME'):
      # mixed: 35 x 35 x 256.
      end_point = 'Mixed_5b'
      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(48), [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
                                 scope='Conv2d_0b_5x5')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, depth(64), [1, 1], 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],
                                 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

      # mixed_1: 35 x 35 x 288.
      end_point = 'Mixed_5c'
      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(48), [1, 1], scope='Conv2d_0b_1x1')
          branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
                                 scope='Conv_1_0c_5x5')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, depth(64), [1, 1],
                                 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],
                                 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

      # mixed_2: 35 x 35 x 288.
      end_point = 'Mixed_5d'
      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(48), [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
                                 scope='Conv2d_0b_5x5')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, depth(64), [1, 1], 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],
                                 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

      # mixed_3: 17 x 17 x 768.
      end_point = 'Mixed_6a'
      with tf.variable_scope(end_point):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(384), [3, 3], stride=2,
                                 padding='VALID', scope='Conv2d_1a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, depth(64), [1, 1], 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,
                                 padding='VALID', scope='Conv2d_1a_1x1')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                     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

      # mixed4: 17 x 17 x 768.
      end_point = 'Mixed_6b'
      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(128), [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(128), [1, 7],
                                 scope='Conv2d_0b_1x7')
          branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
                                 scope='Conv2d_0c_7x1')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, depth(128), [7, 1],
                                 scope='Conv2d_0b_7x1')
          branch_2 = slim.conv2d(branch_2, depth(128), [1, 7],
                                 scope='Conv2d_0c_1x7')
          branch_2 = slim.conv2d(branch_2, depth(128), [7, 1],
                                 scope='Conv2d_0d_7x1')
          branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
                                 scope='Conv2d_0e_1x7')
        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(192), [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 end_point == final_endpoint: return net, end_points

      # mixed_5: 17 x 17 x 768.
      end_point = 'Mixed_6c'
      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(160), [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(160), [1, 7],
                                 scope='Conv2d_0b_1x7')
          branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
                                 scope='Conv2d_0c_7x1')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
                                 scope='Conv2d_0b_7x1')
          branch_2 = slim.conv2d(branch_2, depth(160), [1, 7],
                                 scope='Conv2d_0c_1x7')
          branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
                                 scope='Conv2d_0d_7x1')
          branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
                                 scope='Conv2d_0e_1x7')
        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(192), [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 end_point == final_endpoint: return net, end_points
      # mixed_6: 17 x 17 x 768.
      end_point = 'Mixed_6d'
      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(160), [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(160), [1, 7],
                                 scope='Conv2d_0b_1x7')
          branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
                                 scope='Conv2d_0c_7x1')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
                                 scope='Conv2d_0b_7x1')
          branch_2 = slim.conv2d(branch_2, depth(160), [1, 7],
                                 scope='Conv2d_0c_1x7')
          branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
                                 scope='Conv2d_0d_7x1')
          branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
                                 scope='Conv2d_0e_1x7')
        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(192), [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 end_point == final_endpoint: return net, end_points

      # mixed_7: 17 x 17 x 768.
      end_point = 'Mixed_6e'
      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(192), [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(192), [1, 7],
                                 scope='Conv2d_0b_1x7')
          branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
                                 scope='Conv2d_0c_7x1')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, depth(192), [7, 1],
                                 scope='Conv2d_0b_7x1')
          branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
                                 scope='Conv2d_0c_1x7')
          branch_2 = slim.conv2d(branch_2, depth(192), [7, 1],
                                 scope='Conv2d_0d_7x1')
          branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
                                 scope='Conv2d_0e_1x7')
        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(192), [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 end_point == final_endpoint: return net, end_points

      # mixed_8: 8 x 8 x 1280.
      end_point = 'Mixed_7a'
      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')
          branch_0 = slim.conv2d(branch_0, depth(320), [3, 3], stride=2,
                                 padding='VALID', scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, depth(192), [1, 7],
                                 scope='Conv2d_0b_1x7')
          branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
                                 scope='Conv2d_0c_7x1')
          branch_1 = slim.conv2d(branch_1, depth(192), [3, 3], stride=2,
                                 padding='VALID', scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                     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
      # mixed_9: 8 x 8 x 2048.
      end_point = 'Mixed_7b'
      with tf.variable_scope(end_point):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = tf.concat(axis=3, values=[
              slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
              slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1')])
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(
              branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
          branch_2 = tf.concat(axis=3, values=[
              slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
              slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
        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(192), [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 end_point == final_endpoint: return net, end_points

      # mixed_10: 8 x 8 x 2048.
      end_point = 'Mixed_7c'
      with tf.variable_scope(end_point):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = tf.concat(axis=3, values=[
              slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
              slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1')])
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(
              branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
          branch_2 = tf.concat(axis=3, values=[
              slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
              slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
        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(192), [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 end_point == final_endpoint: return net, end_points
    raise ValueError('Unknown final endpoint %s' % final_endpoint)
def inception_v3_base ( inputs , final_endpoint = 'Mixed_7c' , 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 = { } 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 , 'InceptionV3' , [ inputs ] ) : with slim . arg_scope ( [ slim . conv2d , slim . max_pool2d , slim . avg_pool2d ] , stride = 1 , padding = 'VALID' ) : # 299 x 299 x 3 end_point = 'Conv2d_1a_3x3' net = slim . conv2d ( inputs , depth ( 32 ) , [ 3 , 3 ] , stride = 2 , scope = end_point ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 149 x 149 x 32 end_point = 'Conv2d_2a_3x3' net = slim . conv2d ( net , depth ( 32 ) , [ 3 , 3 ] , scope = end_point ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 147 x 147 x 32 end_point = 'Conv2d_2b_3x3' net = slim . conv2d ( net , depth ( 64 ) , [ 3 , 3 ] , padding = 'SAME' , scope = end_point ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 147 x 147 x 64 end_point = 'MaxPool_3a_3x3' net = slim . max_pool2d ( net , [ 3 , 3 ] , stride = 2 , scope = end_point ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 73 x 73 x 64 end_point = 'Conv2d_3b_1x1' net = slim . conv2d ( net , depth ( 80 ) , [ 1 , 1 ] , scope = end_point ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 73 x 73 x 80. end_point = 'Conv2d_4a_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 # 71 x 71 x 192. end_point = 'MaxPool_5a_3x3' net = slim . max_pool2d ( net , [ 3 , 3 ] , stride = 2 , scope = end_point ) end_points [ end_point ] = net if end_point == final_endpoint : return net , end_points # 35 x 35 x 192. # Inception blocks with slim . arg_scope ( [ slim . conv2d , slim . max_pool2d , slim . avg_pool2d ] , stride = 1 , padding = 'SAME' ) : # mixed: 35 x 35 x 256. end_point = 'Mixed_5b' 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 ( 48 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 64 ) , [ 5 , 5 ] , scope = 'Conv2d_0b_5x5' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 64 ) , [ 1 , 1 ] , 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 ] , 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 # mixed_1: 35 x 35 x 288. end_point = 'Mixed_5c' 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 ( 48 ) , [ 1 , 1 ] , scope = 'Conv2d_0b_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 64 ) , [ 5 , 5 ] , scope = 'Conv_1_0c_5x5' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 64 ) , [ 1 , 1 ] , 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 ] , 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 # mixed_2: 35 x 35 x 288. end_point = 'Mixed_5d' 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 ( 48 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 64 ) , [ 5 , 5 ] , scope = 'Conv2d_0b_5x5' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 64 ) , [ 1 , 1 ] , 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 ] , 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 # mixed_3: 17 x 17 x 768. end_point = 'Mixed_6a' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , depth ( 384 ) , [ 3 , 3 ] , stride = 2 , padding = 'VALID' , scope = 'Conv2d_1a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 64 ) , [ 1 , 1 ] , 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 , padding = 'VALID' , scope = 'Conv2d_1a_1x1' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . max_pool2d ( net , [ 3 , 3 ] , stride = 2 , padding = 'VALID' , 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 # mixed4: 17 x 17 x 768. end_point = 'Mixed_6b' 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 ( 128 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 128 ) , [ 1 , 7 ] , scope = 'Conv2d_0b_1x7' ) branch_1 = slim . conv2d ( branch_1 , depth ( 192 ) , [ 7 , 1 ] , scope = 'Conv2d_0c_7x1' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 128 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 128 ) , [ 7 , 1 ] , scope = 'Conv2d_0b_7x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 128 ) , [ 1 , 7 ] , scope = 'Conv2d_0c_1x7' ) branch_2 = slim . conv2d ( branch_2 , depth ( 128 ) , [ 7 , 1 ] , scope = 'Conv2d_0d_7x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 192 ) , [ 1 , 7 ] , scope = 'Conv2d_0e_1x7' ) 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 ( 192 ) , [ 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 end_point == final_endpoint : return net , end_points # mixed_5: 17 x 17 x 768. end_point = 'Mixed_6c' 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 ( 160 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 160 ) , [ 1 , 7 ] , scope = 'Conv2d_0b_1x7' ) branch_1 = slim . conv2d ( branch_1 , depth ( 192 ) , [ 7 , 1 ] , scope = 'Conv2d_0c_7x1' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 160 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 160 ) , [ 7 , 1 ] , scope = 'Conv2d_0b_7x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 160 ) , [ 1 , 7 ] , scope = 'Conv2d_0c_1x7' ) branch_2 = slim . conv2d ( branch_2 , depth ( 160 ) , [ 7 , 1 ] , scope = 'Conv2d_0d_7x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 192 ) , [ 1 , 7 ] , scope = 'Conv2d_0e_1x7' ) 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 ( 192 ) , [ 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 end_point == final_endpoint : return net , end_points # mixed_6: 17 x 17 x 768. end_point = 'Mixed_6d' 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 ( 160 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 160 ) , [ 1 , 7 ] , scope = 'Conv2d_0b_1x7' ) branch_1 = slim . conv2d ( branch_1 , depth ( 192 ) , [ 7 , 1 ] , scope = 'Conv2d_0c_7x1' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 160 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 160 ) , [ 7 , 1 ] , scope = 'Conv2d_0b_7x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 160 ) , [ 1 , 7 ] , scope = 'Conv2d_0c_1x7' ) branch_2 = slim . conv2d ( branch_2 , depth ( 160 ) , [ 7 , 1 ] , scope = 'Conv2d_0d_7x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 192 ) , [ 1 , 7 ] , scope = 'Conv2d_0e_1x7' ) 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 ( 192 ) , [ 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 end_point == final_endpoint : return net , end_points # mixed_7: 17 x 17 x 768. end_point = 'Mixed_6e' 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 ( 192 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 192 ) , [ 1 , 7 ] , scope = 'Conv2d_0b_1x7' ) branch_1 = slim . conv2d ( branch_1 , depth ( 192 ) , [ 7 , 1 ] , scope = 'Conv2d_0c_7x1' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 192 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 192 ) , [ 7 , 1 ] , scope = 'Conv2d_0b_7x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 192 ) , [ 1 , 7 ] , scope = 'Conv2d_0c_1x7' ) branch_2 = slim . conv2d ( branch_2 , depth ( 192 ) , [ 7 , 1 ] , scope = 'Conv2d_0d_7x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 192 ) , [ 1 , 7 ] , scope = 'Conv2d_0e_1x7' ) 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 ( 192 ) , [ 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 end_point == final_endpoint : return net , end_points # mixed_8: 8 x 8 x 1280. end_point = 'Mixed_7a' 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' ) branch_0 = slim . conv2d ( branch_0 , depth ( 320 ) , [ 3 , 3 ] , stride = 2 , padding = 'VALID' , scope = 'Conv2d_1a_3x3' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 192 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 192 ) , [ 1 , 7 ] , scope = 'Conv2d_0b_1x7' ) branch_1 = slim . conv2d ( branch_1 , depth ( 192 ) , [ 7 , 1 ] , scope = 'Conv2d_0c_7x1' ) branch_1 = slim . conv2d ( branch_1 , depth ( 192 ) , [ 3 , 3 ] , stride = 2 , padding = 'VALID' , scope = 'Conv2d_1a_3x3' ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . max_pool2d ( net , [ 3 , 3 ] , stride = 2 , padding = 'VALID' , 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 # mixed_9: 8 x 8 x 2048. end_point = 'Mixed_7b' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , depth ( 320 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 384 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = tf . concat ( axis = 3 , values = [ slim . conv2d ( branch_1 , depth ( 384 ) , [ 1 , 3 ] , scope = 'Conv2d_0b_1x3' ) , slim . conv2d ( branch_1 , depth ( 384 ) , [ 3 , 1 ] , scope = 'Conv2d_0b_3x1' ) ] ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 448 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 384 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) branch_2 = tf . concat ( axis = 3 , values = [ slim . conv2d ( branch_2 , depth ( 384 ) , [ 1 , 3 ] , scope = 'Conv2d_0c_1x3' ) , slim . conv2d ( branch_2 , depth ( 384 ) , [ 3 , 1 ] , scope = 'Conv2d_0d_3x1' ) ] ) 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 ( 192 ) , [ 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 end_point == final_endpoint : return net , end_points # mixed_10: 8 x 8 x 2048. end_point = 'Mixed_7c' with tf . variable_scope ( end_point ) : with tf . variable_scope ( 'Branch_0' ) : branch_0 = slim . conv2d ( net , depth ( 320 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) with tf . variable_scope ( 'Branch_1' ) : branch_1 = slim . conv2d ( net , depth ( 384 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_1 = tf . concat ( axis = 3 , values = [ slim . conv2d ( branch_1 , depth ( 384 ) , [ 1 , 3 ] , scope = 'Conv2d_0b_1x3' ) , slim . conv2d ( branch_1 , depth ( 384 ) , [ 3 , 1 ] , scope = 'Conv2d_0c_3x1' ) ] ) with tf . variable_scope ( 'Branch_2' ) : branch_2 = slim . conv2d ( net , depth ( 448 ) , [ 1 , 1 ] , scope = 'Conv2d_0a_1x1' ) branch_2 = slim . conv2d ( branch_2 , depth ( 384 ) , [ 3 , 3 ] , scope = 'Conv2d_0b_3x3' ) branch_2 = tf . concat ( axis = 3 , values = [ slim . conv2d ( branch_2 , depth ( 384 ) , [ 1 , 3 ] , scope = 'Conv2d_0c_1x3' ) , slim . conv2d ( branch_2 , depth ( 384 ) , [ 3 , 1 ] , scope = 'Conv2d_0d_3x1' ) ] ) 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 ( 192 ) , [ 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 end_point == final_endpoint : return net , end_points raise ValueError ( 'Unknown final endpoint %s' % final_endpoint )