tensorboard.py

print('TensorBoard demonstration')

from hack import hack
hack()

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_tset = sequence.pad_sequences(x_test, maxlen=max_len)

model = keras.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.MaxPool1D(5))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(1))

model.summary()

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])

callbacks = [
  keras.callbacks.TensorBoard(
    log_dir = './logs',
    histogram_freq=1,
    embeddings_freq=1,
  )  
]

history = model.fit(x_train, y_train, 
                    epochs=20,
                    batch_size=128,
                    validation_split=0.2,
                    callbacks=callbacks)