警告：tensorflow：`write_grads`在TensorFlow 2.0中将被`TensorBoard`回调所忽略

如何解决警告：tensorflow：`write_grads`在TensorFlow 2.0中将被`TensorBoard`回调所忽略

我正在使用以下代码行使用张量板可视化ANN模型的梯度

C:\bin>DISM.exe /Online /Cleanup-image /Scanhealth

Deployment Image Servicing and Management tool
Version: 6.3.9600.17031

Image Version: 6.3.9600.17031

[==========================100.0%==========================]
No component store corruption detected.
The operation completed successfully.

C:\bin> DISM.exe /Online /Cleanup-image /Restorehealth

Deployment Image Servicing and Management tool
Version: 6.3.9600.17031

Image Version: 6.3.9600.17031

[==========================100.0%==========================]
The restore operation completed successfully. The component store corruption was repaired.
The operation completed successfully.

我收到一条警告消息，内容为“警告：tensorflow： tensorboard_callback = tf.compat.v1.keras.callbacks.TensorBoard(log_dir='./Graph',histogram_freq=1,write_graph = True,write_grads =True,write_images = False) tensorboard_callback .set_model(model) %tensorboard --logdir ./Graph 在TensorFlow 2.0中将被write_grads回调忽略。”

我得到了张量板输出，但是没有渐变。

可能是什么原因？

（注意：我使用2.3.0 tensorflow版本）

谢谢。

解决方法

Write_Grads未在TF2.x中实现。这是仍处于高度期望状态的功能请求之一。请检查此GitHub issue作为功能请求。因此，我们只需要导入TF1.x模块并使用write_grads，如下面的代码所示。

# Load the TensorBoard notebook extension
%load_ext tensorboard

import tensorflow as tf
import datetime

# Clear any logs from previous runs
!rm -rf ./logs/ 

# Disable V2 behavior
tf.compat.v1.disable_v2_behavior()

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


def create_model():

  return tf.keras.models.Sequential([

    tf.keras.layers.Flatten(input_shape=(28,28)),tf.keras.layers.Dense(512,activation='relu'),tf.keras.layers.Dropout(0.2),tf.keras.layers.Dense(10,activation='softmax')

  ])

 

model = create_model()

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


log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")

tensorboard_callback = tf.compat.v1.keras.callbacks.TensorBoard(log_dir=log_dir,histogram_freq=1,write_grads =True)

model.fit(x=x_train,y=y_train,epochs=1,validation_data=(x_test,y_test),callbacks=[tensorboard_callback]) 

%tensorboard --logdir logs/fit

输出：

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step

Train on 60000 samples,validate on 10000 samples
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_v1.py:2048: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
This property should not be used in TensorFlow 2.0,as updates are applied automatically.
   32/60000 [..............................] - ETA: 0s - loss: 2.3311 - acc: 0.0312WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0055s vs `on_train_batch_end` time: 0.0235s). Check your callbacks.
60000/60000 [==============================] - 17s 288us/sample - loss: 0.2187 - acc: 0.9349 - val_loss: 0.1012 - val_acc: 0.9690
<tensorflow.python.keras.callbacks.History at 0x7f7ebd1d3d30>