Python 警告：tensorflow:'write_grads'在tensorflow 2.0中对于'TensorBoard'回调将被忽略

我使用以下代码行来可视化使用tensorboard的ANN模型的梯度

  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

我收到一条警告消息，说“警告：tensorflow:

write_grads

将在tensorflow 2.0中忽略

TensorBoard

回调。”

我得到张力板输出，但没有梯度

可能的原因是什么

（注：我使用2.3.0 tensorflow版本）

---

谢谢。

Write\u Grads

未在

TF2.x

中实现。这是一个仍处于开放状态的高期望特性请求。请检查此GitHub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>

从中下载数据https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434[==================================]-0s 0us/步
培训60000个样本，验证10000个样本
警告：tensorflow:From/usr/local/lib/python3.6/dist软件包/tensorflow/python/keras/engine/training_v1.py:2048:Model.state_更新（来自tensorflow.python.keras.engine.training）已被弃用，并将在未来版本中删除。
更新说明：
此属性不应在TensorFlow 2.0中使用，因为会自动应用更新。
32/60000[……]-ETA:0s-损失：2.3311-acc:0.0312警告：tensorflow:Callbacks方法'on_train_batch_end'比批次时间慢（批次时间：0.0055s vs'on_train_batch_end'时间：0.0235s）。检查你的回电。
60000/60000[=========================================================17s 288us/样品-损耗：0.2187-acc:0.9349-val_损耗：0.1012-val_acc:0.9690

---

即使在使用了

tf.compat.v1.keras.callbacks.TensorBoard（write\u grads=True）之后，仍然显示相同的警告消息