Simple way to visualize a TensorFlow graph in Jupyter?

Question

The official way to visualize a TensorFlow graph is with TensorBoard, but sometimes I just want a quick look at the graph when I\'m working in Jupyter.

Is there a qu

Answer 1

Here's a recipe I copied from one of Alex Mordvintsev deep dream notebook at some point

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 = ""%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 = """
        
        
        

          
        
    """.format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))

    iframe = """
        
    """.format(code.replace('"', '"'))
    display(HTML(iframe))

Then to visualize current graph

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

If your graph is saved as pbtxt, you could do

gdef = tf.GraphDef()
from google.protobuf import text_format
text_format.Merge(open("tf_persistent.pbtxt").read(), gdef)
show_graph(gdef)

You'll see something like this