visually monitoring metrics during training visualizing your model architecture visualizing histograms of activations and gradients exploring embeddings in let' demonstrate these features on simple example you'll train convnet on the imdb sentiment-analysis task the model is similar to the one you saw in the last section of you'll consider only the top , words in the imdb vocabularyto make visualizing word embeddings more tractable listing text -classificat ion model use wit tensorboard import keras from keras import layers from keras datasets import imdb from keras preprocessing import sequence number of words to consider as features max_features max_len cuts off texts after this number of words (among max_features most common words(x_trainy_train)(x_testy_testimdb load_data(num_words=max_featuresx_train sequence pad_sequences(x_trainmaxlen=max_lenx_test sequence pad_sequences(x_testmaxlen=max_lenmodel keras models sequential(model add(layers embedding(max_features input_length=max_lenname='embed')model add(layers conv ( activation='relu')model add(layers maxpooling ( )model add(layers conv ( activation='relu')model add(layers globalmaxpooling ()model add(layers dense( )model summary(model compile(optimizer='rmsprop'loss='binary_crossentropy'metrics=['acc']before you start using tensorboardyou need to create directory where you'll store the log files it generates listing creat ing direct ory for tensorboard log files mkdir my_log_dir let' launch the training with tensorboard callback instance this callback will write log events to disk at the specified location licensed to