Why tensor board is showing "no scalar data is found"? - tensorflow

Question

I've copied a piece of code to create a NN and after training it logs are successfully created but when I tried to visualise it using tensorboard it is showing that no scalar data is found.
This is code and logs are successfully created and even event files are there but it is showing
checkpoint_path = "autoencoder.h5" # For each epoch creating a checkpoint
checkpoint_dir = os.path.dirname(checkpoint_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,save_weights_only=False,verbose=0,save_best_only=False) # To save the model if the metric is improved
# Tensorbaord
! rm -rf ./logs_autoencoder/ # Removing all the files present in the directory
logdir = os.path.join("logs_autoencoder", datetime.datetime.now().strftime("%Y%m%d-%H%M%S")) # Directory for storing the logs that are required for tensorboard
%reload_ext tensorboard
%tensorboard --logdir $logdir
tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)
lrScheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',patience=2,factor=0.2,verbose=1)
callbacks = [cp_callback,tensorboard_callback,lrScheduler]
autoencoder.fit( train_dataset,shuffle=True,epochs=10,validation_data= test_dataset,callbacks=callbacks)
The output was like,
Epoch 1/10
1338/1338 [==============================] - 839s 626ms/step - loss: 0.0104 - val_loss: 0.0046 - lr: 0.0010
Epoch 2/10
1338/1338 [==============================] - 818s 611ms/step - loss: 0.0047 - val_loss: 0.0042 - lr: 0.0010
Epoch 3/10
1338/1338 [==============================] - 824s 616ms/step - loss: 0.0043 - val_loss: 0.0041 - lr: 0.0010
Epoch 4/10
1338/1338 [==============================] - 824s 616ms/step - loss: 0.0040 - val_loss: 0.0037 - lr: 0.0010
Epoch 5/10
1338/1338 [==============================] - 829s 619ms/step - loss: 0.0038 - val_loss: 0.0033 - lr: 0.0010
Epoch 6/10
1338/1338 [==============================] - 834s 624ms/step - loss: 0.0036 - val_loss: 0.0032 - lr: 0.0010
Epoch 7/10
1338/1338 [==============================] - 852s 637ms/step - loss: 0.0035 - val_loss: 0.0032 - lr: 0.0010
Epoch 8/10
1338/1338 [==============================] - ETA: 0s - loss: 0.0034
Epoch 8: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.
1338/1338 [==============================] - 953s 712ms/step - loss: 0.0034 - val_loss: 0.0031 - lr: 0.0010
Epoch 9/10
1338/1338 [==============================] - 962s 719ms/step - loss: 0.0033 - val_loss: 0.0031 - lr: 2.0000e-04
Epoch 10/10
1338/1338 [==============================] - ETA: 0s - loss: 0.0033
Epoch 10: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.
1338/1338 [==============================] - 939s 702ms/step - loss: 0.0033 - val_loss: 0.0031 - lr: 2.0000e-04
Out[16]:
<keras.callbacks.History at 0x7f8cfe7b2090>

Answer 1

This could simply be an issue of order of events in Jupyter notebook. I'd recommend breaking things up a little
checkpoint_path = "autoencoder.h5" # For each epoch creating a checkpoint
checkpoint_dir = os.path.dirname(checkpoint_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,save_weights_only=False,verbose=0,save_best_only=False) # To save the model if the metric is improved
# Tensorbaord
! rm -rf ./logs_autoencoder/ # Removing all the files present in the directory
logdir = os.path.join("logs_autoencoder", datetime.datetime.now().strftime("%Y%m%d-%H%M%S")) # Directory for storing the logs that are required for tensorboard
tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)
lrScheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',patience=2,factor=0.2,verbose=1)
callbacks = [cp_callback,tensorboard_callback,lrScheduler]
autoencoder.fit( train_dataset,shuffle=True,epochs=10,validation_data= test_dataset,callbacks=callbacks)
New cell block
%reload_ext tensorboard
%tensorboard --logdir $logdir
The tensorboard is launching before the model training starts, and says nothing is present. Then, if you try to re-run the cell, your !rm -rf line is deleting everything, so the tensorboard doesn't see the previous data