tensorboard_generator.py

import keras
from keras import layers
from keras.datasets import imdb
from keras.preprocessing import sequence
max_features = 2000
max_len = 500
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
x_train = sequence.pad_sequences(x_train, maxlen=max_len)
x_test = sequence.pad_sequences(x_test, maxlen=max_len)

model = keras.models.Sequential()
model.add(layers.Embedding(max_features, 128,input_length=max_len,name='embed'))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.MaxPooling1D(5))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(1,activation='sigmoid'))
model.summary()
model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['acc'])

callbacks = [
    keras.callbacks.TensorBoard(log_dir='my_log_dir',histogram_freq=1,embeddings_freq=1,embeddings_data = x_train[:100].astype("float32"))
]
history = model.fit(x_train, y_train,epochs=20,batch_size=128,validation_split=0.2,callbacks=callbacks)
# tensorboard --logdir=my_log_dir