tensorboard inline

tensorboard_inline.py

from IPython.display import clear_output, Image, display, HTML
import numpy as np

def strip_consts(graph_def, max_const_size=32):
    """Strip large constant values from graph_def."""
    strip_def = tf.GraphDef()
    for n0 in graph_def.node:
        n = strip_def.node.add() 
        n.MergeFrom(n0)
        if n.op == 'Const':
            tensor = n.attr['value'].tensor
            size = len(tensor.tensor_content)
            if size > max_const_size:
                tensor.tensor_content = "<stripped %d bytes>"%size
    return strip_def

def show_graph(graph_def, max_const_size=32):
    """Visualize TensorFlow graph."""
    if hasattr(graph_def, 'as_graph_def'):
        graph_def = graph_def.as_graph_def()
    strip_def = strip_consts(graph_def, max_const_size=max_const_size)
    code = """
        <script>
          function load() {{
            document.getElementById("{id}").pbtxt = {data};
          }}
        </script>
        <link rel="import" href="https://tensorboard.appspot.com/tf-graph-basic.build.html" onload=load()>
        <div style="height:600px">
          <tf-graph-basic id="{id}"></tf-graph-basic>
        </div>
    """.format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))

    iframe = """
        <iframe seamless style="width:1200px;height:620px;border:0" srcdoc="{}"></iframe>
    """.format(code.replace('"', '&quot;'))
    display(HTML(iframe))

Sample usage

Build Graph

import tensorflow as tf
from tensorflow.contrib import slim
from tensorflow.contrib.slim.nets import resnet_utils, resnet_v1

num_gpus = 1

tf.reset_default_graph()

for i in range(num_gpus):
    with tf.name_scope('tower_%d' % i):
        image = tf.ones(shape=[1,1,1,3], name='input_image')
        
        with slim.arg_scope(resnet_v1.resnet_arg_scope()):

            blocks = [
                resnet_v1.resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                resnet_v1.resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                resnet_v1.resnet_v1_block('block3', base_depth=256, num_units=23, stride=2),
            ]

            net, end_points = resnet_v1.resnet_v1(
                inputs=image,
                blocks=blocks,
                scope='resnet_demo')
            
        tf.get_variable_scope().reuse_variables()

Vizualization

show_graph(tf.get_default_graph().as_graph_def())