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

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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