How to graph tf.keras model in Tensorflow-2.0?

Question

I upgraded to Tensorflow 2.0 and there is no tf.summary.FileWriter(\"tf_graphs\", sess.graph). I was looking through some other StackOverflow questions on this

Answer 1

According to the docs, you can use Tensorboard to visualise graphs once your model has been trained.

First, define your model and run it. Then, open Tensorboard and switch to the Graph tab.

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Minimal Compilable Example

This example is taken from the docs. First, define your model and data.

# Relevant imports.
%load_ext tensorboard

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

from datetime import datetime
from packaging import version

import tensorflow as tf
from tensorflow import keras

# Define the model.
model = keras.models.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(32, activation='relu'),
    keras.layers.Dropout(0.2),
    keras.layers.Dense(10, activation='softmax')
])

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy'])

(train_images, train_labels), _ = keras.datasets.fashion_mnist.load_data()
train_images = train_images / 255.0

Next, train your model. Here, you will need to define a callback for Tensorboard to use for visualising stats and graphs.

# Define the Keras TensorBoard callback.
logdir="logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)

# Train the model.
model.fit(
    train_images,
    train_labels, 
    batch_size=64,
    epochs=5, 
    callbacks=[tensorboard_callback])

After training, in your notebook, run

%tensorboard --logdir logs

And switch to the Graph tab in the navbar:

You will see a graph that looks a lot like this: