如何使用张量板以 Eager 模式可视化 tensorflow 1.15 中的网络图? [英] How to visualize the network graph in tensorflow 1.15 with Eager mode using tensorboard?

问题描述

嗨~我想在 tf1.15 中以 Eager 模式可视化 NN(无法切换到 2.0.0).并且实现基于 Tensorflow 1.15 的低级 API.我想使用张量板来可视化它.我写了一个日志跟踪代码但得到错误:

Hi~ I want to visualize the NN in Eager mode in tf1.15 (can not switch to 2.0.0). And the implementation is based on low-level API of Tensorflow 1.15. I want to use the tensorboard to visualize it. I write a log tracing code but get the error:

WARNING:tensorflow:
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
  * https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.

Traceback (most recent call last):
  File "/home/frank/PycharmProjects/reconstruction_NN/my_test.py", line 78, in <module>
    tf.contrib.summary.trace_on(graph=True, profiler=True)
AttributeError: module 'tensorflow.contrib.summary.summary' has no attribute 'trace_on'

环境信息(必填)

张量板 1.15.0
张量流估计器 1.15.1
张量流-GPU 1.15.0
Ubuntu16.04

Environment information (required)

tensorboard 1.15.0
tensorflow-estimator 1.15.1
tensorflow-gpu 1.15.0
Ubuntu16.04

代码:

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D,Dropout
from tensorflow.keras import Model
tf.compat.v1.enable_eager_execution()
print(tf.__version__)
print(tf.executing_eagerly())


mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train, x_test = x_train / 255.0, x_test / 255.0


batch_size = 32

train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(batch_size)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size)


class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.flatten = Flatten()
        self.d1 = Dense(128, activation='relu')
        self.dropout = Dropout(0.5)
        self.d2 = Dense(10, activation='softmax')

    def call(self, x):
        x = self.flatten(x)
        x = self.d1(x)
        x = self.dropout(x)
        return self.d2(x)

model = MyModel()

loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()

train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    train_loss(loss)
    train_accuracy(labels, predictions)

@tf.function
def test_step(images, labels):
    predictions = model(images)
    t_loss = loss_object(labels, predictions)
    test_loss(t_loss)
    test_accuracy(labels, predictions)


EPOCHS = 5
from datetime import *
stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
logdir = 'logs/func/%s' % stamp
writer = tf.contrib.summary.create_file_writer(logdir)
tf.summary.trace_on(graph=True, profiler=True)

for epoch in range(EPOCHS):

    for images, labels in train_ds:

        train_step(images, labels)

    for test_images, test_labels in test_ds:
        test_step(test_images, test_labels)

    template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'

    print(template.format(epoch + 1, train_loss.result(),
                              train_accuracy.result() * 100,
                              test_loss.result(),
                              test_accuracy.result() * 100))
with writer.as_default():
  tf.summary.trace_export(
      name="my_func_trace",
      step=0,
      profiler_outdir=logdir)

推荐答案

警告即将到来，因为您正在访问已弃用的 tf.contrib 命名空间.文档 指定您应该将 writer 对象用作

The warning is coming because you are accessing the tf.contrib namespace, which is deprecated. The documentation specifies that you should use the writer object as

writer = tf.summary.create_file_writer(logdir)

这篇关于如何使用张量板以 Eager 模式可视化 tensorflow 1.15 中的网络图?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！