Tensorflow: how to restore a inception v3 pre trained network weights after having expanded the graph with new final layer

I solved the problem!!

You need to call saver.restore (sess, FLAGS.checkpoint) after initializing the network with sess.run (tf.global_variables_initializer ()).

Important: The saver = tf_saver.Saver () must be instantiated before adding new layers to the graph.

This way, when the saver.restore(sess, FLAGS.checkpoint) is performed, it only knows the computation graph prior to creating new layers.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import os.path

import tensorflow as tf
from tensorflow.contrib import layers
from tensorflow.contrib.slim.python.slim.nets import inception
from tensorflow.python.ops import array_ops
from tensorflow.python.training import saver as tf_saver

slim = tf.contrib.slim
FLAGS = None


def PreprocessImage(image, central_fraction=0.875):
    """Load and preprocess an image.

    Args:
      image: a tf.string tensor with an JPEG-encoded image.
      central_fraction: do a central crop with the specified
        fraction of image covered.
    Returns:
      An ops.Tensor that produces the preprocessed image.
    """

    # Decode Jpeg data and convert to float.
    image = tf.cast(tf.image.decode_jpeg(image, channels=3), tf.float32)

    image = tf.image.central_crop(image, central_fraction=central_fraction)
    # Make into a 4D tensor by setting a 'batch size' of 1.
    image = tf.expand_dims(image, [0])
    image = tf.image.resize_bilinear(image,
                                     [FLAGS.image_size, FLAGS.image_size],
                                     align_corners=False)

    # Center the image about 128.0 (which is done during training) and normalize.
    image = tf.multiply(image, 1.0 / 127.5)
    return tf.subtract(image, 1.0)


def main(args):
    if not os.path.exists(FLAGS.checkpoint):
        tf.logging.fatal(
            'Checkpoint %s does not exist. Have you download it? See tools/download_data.sh',
            FLAGS.checkpoint)
    g = tf.Graph()
    with g.as_default():
        input_image = tf.placeholder(tf.string)
        processed_image = PreprocessImage(input_image)

        with slim.arg_scope(inception.inception_v3_arg_scope()):
            logits, end_points = inception.inception_v3(
                processed_image, num_classes=FLAGS.num_classes, is_training=False)

        predictions = end_points['multi_predictions'] = tf.nn.sigmoid(
            logits, name='multi_predictions')

        sess = tf.Session()

        saver = tf_saver.Saver()

        logits_2 = layers.conv2d(
            end_points['PreLogits'],
            FLAGS.num_classes, [1, 1],
            activation_fn=None,
            normalizer_fn=None,
            scope='Conv2d_final_1x1')

        logits_2 = array_ops.squeeze(logits_2, [1, 2], name='SpatialSqueeze_2')

        predictions_2 = end_points['multi_predictions_2'] = tf.nn.sigmoid(logits_2, name='multi_predictions_2')

        sess.run(tf.global_variables_initializer())

        saver.restore(sess, FLAGS.checkpoint)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--checkpoint', type=str, default='2016_08/model.ckpt',
                        help='Checkpoint to run inference on.')
    parser.add_argument('--dict', type=str, default='dict.csv',
                        help='Path to a dict.csv that translates from mid to a display name.')
    parser.add_argument('--image_size', type=int, default=299,
                        help='Image size to run inference on.')
    parser.add_argument('--num_classes', type=int, default=6012,
                        help='Number of output classes.')
    parser.add_argument('--image_path', default='test_set/0a9ed4def08fe6d1')
    FLAGS = parser.parse_args()
    tf.app.run()