警告:tensorflow:`write_grads` 在 TensorFlow 2.0 中将被忽略`TensorBoard` 回调 [英] WARNING:tensorflow:`write_grads` will be ignored in TensorFlow 2.0 for the `TensorBoard` Callback

问题描述

我正在使用以下代码行使用张量板可视化 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WARNING:tensorflow:write_grads 将在 TensorFlow 2.0 中被忽略用于 TensorBoard 回调."

我得到了张量板输出，但没有渐变.

可能的原因是什么?

(注意:我使用的是 2.3.0 tensorflow 版本)

谢谢.

解决方案

Write_Grads 未在 TF2.x 中实现.这是仍然开放的备受期待的功能请求之一.请检查此 GitHub

I am using the following lines of codes to visualise the gradients of an ANN model using tensorboard

  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

I received a warning message saying "WARNING:tensorflow:write_grads will be ignored in TensorFlow 2.0 for the TensorBoard Callback."

I get the tensorboard output, but without gradients.

What could be the possible reason?

(Note: I use 2.3.0 tensorflow version)

Thank you.

解决方案

Write_Grads was not implemented in TF2.x. This is one of the highly expected feature request that is still open. Please check this GitHub issue as feature request. So, we only need to import TF1.x modules and use write_grads as shown in the following code.

# 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

Output:

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>

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