Extracting latent context vectors about keras-tcn HOT 4 CLOSED

"""
#Trains a TCN on the IMDB sentiment classification task.
Output after 1 epochs on CPU: ~0.8611
Time per epoch on CPU (Core i7): ~64s.
Based on: https://github.com/keras-team/keras/blob/master/examples/imdb_bidirectional_lstm.py
"""
import numpy as np
from keras import Model, Input
from keras.datasets import imdb
from keras.layers import Dense, Dropout, Embedding
from keras.preprocessing import sequence
import keract

from tcn import TCN

max_features = 20000
# cut texts after this number of words
# (among top max_features most common words)
maxlen = 100
batch_size = 32

print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')

print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
y_train = np.array(y_train)
y_test = np.array(y_test)

i = Input(shape=(maxlen,))
x = Embedding(max_features, 128)(i)
x = TCN(nb_filters=64,
        kernel_size=6,
        dilations=[1, 2, 4, 8, 16, 32, 64])(x)
x = Dropout(0.5)(x)
x = Dense(1, activation='sigmoid')(x)

model = Model(inputs=[i], outputs=[x])

model.summary()

# try using different optimizers and different optimizer configs
model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])

activations = keract.get_activations(model, x_train[0:1])

print('Train...')
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=1,
          validation_data=[x_test, y_test])