tensorflow – Where is the key to make tensorboard work? – Stack Overflow

I’m new to tensorboard and learning the use of it following the tutorial, which goes well and tensorboard works as expected.

Referring to that tutorial, I wrote my own code to train a logic-and model with jupyter notebook

%load_ext tensorboard
import datetime
log_folder = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
import tensorflow as tf
import numpy as np

x_train = np.asarray([[0, 0],[0, 1],[1, 0],[1, 1]], np.float32)
y_train = np.asarray([0, 0, 0, 1], np.float32)

model = tf.keras.models.Sequential([
  tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)
])

def custom_loss(y,a):
    return -(y*tf.math.log(a) + (1-y)*tf.math.log(1-a))

model.compile(loss=custom_loss,
              optimizer='SGD',
              metrics=['accuracy'])

tensorboard_callback = tf.keras.callbacks.TensorBoard(log_folder, histogram_freq=1)

model.fit(x_train, y_train, epochs=2000, verbose=0,
          callbacks=[tensorboard_callback])

the training goes well and needs some improvement.

However, tensorboard shows nothing

%tensorboard --logdir log_folder

where is the key to make tensorboard work?

You’re just using the ipython magic wrong. You need to put a dollar sign in front of your variable name (see e.g. How to pass a variable to magic ´run´ function in IPython).

%tensorboard --logdir $log_folder

For further exploring, try pretending you’re working in the future (at least as far as date goes), and add a cell like this

log_folder_future = "logs/fit/" + (datetime.datetime.now() - datetime.timedelta(days=1)).strftime("%Y%m%d-%H%M%S")
up_dir = './' + '/'.join(log_folder_future.split('/')[:-1])
model.compile(loss=custom_loss,
              optimizer='SGD',
              metrics=['accuracy'])

tensorboard_callback_future = tf.keras.callbacks.TensorBoard(log_folder_future, histogram_freq=1)
model.fit(x_train, y_train, epochs=2500, verbose=0,
          callbacks=[tensorboard_callback_future])

and call like this

%tensorboard --logdir $up_dir

and end up with something like this

For more on tensorboard directory structures and multiple runs, see this page
https://github.com/tensorflow/tensorboard/blob/master/README.md#runs-comparing-different-executions-of-your-model