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_feature, 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.GlobalMaxPool1D())
model.add(layers.Dense(1))
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,
    )
]

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