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pythontensorflowneural-networktensorboardtf.keras

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.


Solution

  • 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