I'm trying to understand how to use TensorBoard for model debugging, used the example from TF docs (https://www.tensorflow.org/tensorboard/scalars_and_keras#training_the_model_and_logging_loss), but my scalars don't update.
# multi-class classification with Keras
import tensorflow as tf
import datetime as dt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
log_dir = "/home/bartek/Desktop/Kaggle/trees/logs/scalars/" +
dt.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir)
def NN_model():
# create model
model = Sequential()
model.add(Dense(len(features.columns)*2, input_dim=len(features.columns), activation='relu'))
model.add(Dense(len(features.columns)*2, activation='relu'))
model.add(Dense(len(features.columns)*2, activation='relu'))
model.add(Dense(len(features.columns)*2, activation='relu'))
model.add(Dense(len(features.columns)*2, activation='relu'))
model.add(Dense(len(features.columns)*2, activation='relu'))
model.add(Dense(84, activation='relu'))
model.add(Dense(49, activation='relu'))
model.add(Dense(7, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=[tf.keras.metrics.CategoricalAccuracy(),tf.keras.metrics.Precision(), tf.keras.metrics.Recall()],
callbacks=[tensorboard_callback])
return model
classifier = KerasClassifier(build_fn=NN_model, epochs=100, batch_size=5, verbose=0)
classifier.fit(features.values, labels.values, verbose=1, callbacks=[tensorboard_callback])
but all I get is terminal output:
and TensorBoard shows nothing:
Why this is the case? Manual refresh doesn't work either. Sometimes graphs stop after xth epoch and don't refresh.