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WARNING:tensorflow:`write_grads` will be ignored in TensorFlow 2.0 for the `TensorBoard` Callback

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.

enter image description here

What could be the possible reason?

(Note: I use 2.3.0 tensorflow version)

Thank you.

like image 501
Kay Avatar asked Aug 13 '20 04:08

Kay


1 Answers

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>

enter image description here

like image 96
TFer2 Avatar answered Nov 08 '22 11:11

TFer2