CODEWITHSUNDEEP
pythontensorflowgoogle-colaboratorytensorflow2.0tensorboard
Guillermina
Guillermina

Reputation: 3657

Tensorboard in Colab: No dashboards are active for the current data set

I am trying to display a Tensorboard in Google Colab. I import tensorboard: %load_ext tensorboard, then create a log_dir, and fit it as follows:

log_dir = '/gdrive/My Drive/project/' + "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

history = model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size,
    callbacks=[tensorboard_callback])

But when I call it with %tensorboard --logdir logs/fit it doesn't display. Instead, it throws the following message:

No dashboards are active for the current data set.

Is there a solution for this? is the problem in the fixed path I passed in log_dir?

Upvotes: 3

Views: 5316

Answers (2)

hansrajswapnil
hansrajswapnil

Reputation: 639

Maybe you've in some way messed up with the path. If you are working with tensorflow version 2.0+, then please try with this solution

## setup 
# Load the TensorBoard notebook extension.
%load_ext tensorboard

Import necessary packages

from datetime import datetime
from packaging import version

import tensorflow as tf
from tensorflow import keras

import numpy as np

print("TensorFlow version: ", tf.__version__)
assert version.parse(tf.__version__).release[0] >= 2, \
"This notebook requires TensorFlow 2.0 or above."

You need to provide tensorboard_callbacks in the callbacks argument in your model.fit() It will appear something like this --

# define path to save log files
logdir = "logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir, histogram_frequency=1, write_graph=True)

# define & compile your model; here i am moving forward with assumption that you've already defined and compiled your model
model = keras.models.Sequential([
    keras.layers.Dense(16, input_dim=1),
    keras.layers.Dense(1),
    ])

model.compile(
    loss='mse', # keras.losses.mean_squared_error
    optimizer=keras.optimizers.SGD(lr=0.2),
    )

# watch closely the argument passed in 'callbacks'
model.fit(x=x_train, 
      y=y_train, 
      epochs=10, 
      validation_data=(x_test, y_test), 
      callbacks=[tensorboard_callback]))

This will save your log files in the memory allocated in your google colab notebook.

To see the TensorBoard results --

%tensorboard --logdir logs/fit/

Result should appear something like this--- enter image description here

Further resources

  • https://www.tensorflow.org/tensorboard/scalars_and_keras

    • Upvotes: 0

    bsquare
    bsquare

    Reputation: 986

    Please try the below code

    log_dir = '/gdrive/My Drive/project/' + "logs/fit/"
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

history = model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size,
    callbacks=[tensorboard_callback])

    %load_ext tensorboard
    %tensorboard --logdir /gdrive/My Drive/project/logs/fit/

    Upvotes: 2

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