如何在 Tensorflow-2.0 中绘制 tf.keras 模型? [英] How to graph tf.keras model in Tensorflow-2.0?

问题描述

我升级到 Tensorflow 2.0 并且没有 tf.summary.FileWriter("tf_graphs", sess.graph).我正在查看有关此的其他一些 StackOverflow 问题，他们说使用 tf.compat.v1.summary etc.当然，在 Tensorflow 版本 2 中必须有一种方法来绘制和可视化 tf.keras 模型.它是什么?我正在寻找像下面这样的张量板输出.谢谢！

解决方案

根据

你会看到一个看起来很像这样的图表:

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 and they said to use tf.compat.v1.summary etc. Surely there must be a way to graph and visualize a tf.keras model in Tensorflow version 2. What is it? I'm looking for a tensorboard output like the one below. Thank you!

解决方案

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.

---

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:

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