How to read weights saved in tensorflow checkpoint file?

Question

I'd like to read the weights and visualize them as images. But I don't see any documentation about model format and how to read the trained weights.

Answer 1

There's this utility which has on print_tensors_in_checkpoint_file method http://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/inspect_checkpoint.py

Alternatively, you can use Saver to restore the model and use session.run on variable tensors to get values as numpy arrays

Answer 2

I wrote snippet in Python

def extracting(meta_dir):
    num_tensor = 0
    var_name = ['2-convolutional/kernel']
    model_name = meta_dir
    configfiles = [os.path.join(dirpath, f)  
        for dirpath, dirnames, files in os.walk(model_name)
        for f in fnmatch.filter(files, '*.meta')] # List of META files

    with tf.Session() as sess:
        try:
            # A MetaGraph contains both a TensorFlow GraphDef
            # as well as associated metadata necessary
            # for running computation in a graph when crossing a process boundary.
            saver = tf.train.import_meta_graph(configfiles[0])
       except:
           print("Unexpected error:", sys.exc_info()[0])
       else:
           # It will get the latest check point in the directory
           saver.restore(sess, configfiles[-1].split('.')[0])  # Specific spot

           # Now, let's access and create placeholders variables and
           # create feed-dict to feed new data
           graph = tf.get_default_graph()
           inside_list = [n.name for n in graph.as_graph_def().node]

           print('Step: ', configfiles[-1])

           print('Tensor:', var_name[0] + ':0')
           w2 = graph.get_tensor_by_name(var_name[0] + ':0')
           print('Tensor shape: ', w2.get_shape())
           print('Tensor value: ', sess.run(w2))
           w2_saved = sess.run(w2)  # print out tensor

You could run it by giving meta_dir as your pre-trained model directory.