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movie_name_list = tf.train.BytesList(value=[b'The Shawshank Redemption', b'Fight Club']) | |
movie_rating_list = tf.train.FloatList(value=[9.0, 9.7]) |
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movie_names = tf.train.Feature(bytes_list=movie_name_list) | |
movie_ratings = tf.train.Feature(float_list=movie_rating_list) |
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movie_dict = { | |
'Movie Names': movie_names, | |
'Movie Ratings': movie_ratings | |
} | |
movies = tf.train.Features(feature=movie_dict) |
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example = tf.train.Example(features=movies) |
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movie_1_actors = tf.train.Feature( | |
bytes_list=tf.train.BytesList( | |
value=[b'Tim Robbins', b'Morgan Freeman'])) | |
movie_2_actors = tf.train.Feature( | |
bytes_list=tf.train.BytesList( | |
value=[b'Brad Pitt', b'Edward Norton', b'Helena Bonham Carter'])) | |
movie_actors_list = [movie_1_actors, movie_2_actors] | |
movie_actors = tf.train.FeatureList(feature=movie_actors_list) | |
# Short form |
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movies_dict = { | |
'Movie Names': movie_names, | |
'Movie Ratings': movie_ratings, | |
'Movie Actors': movie_actors | |
} | |
movies = tf.train.FeatureLists(feature_list=movies_dict) |
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# We can also add context features (short form) | |
customer = tf.train.Features(feature={ | |
'Age': tf.train.Feature(int64_list=tf.train.Int64List(value=[19])), | |
}) | |
example = tf.train.SequenceExample( | |
context=customer, | |
feature_lists=movies) |
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# "example" is of type tf.train.Example. | |
with tf.python_io.TFRecordWriter('movie_ratings.tfrecord') as writer: | |
writer.write(example.SerializeToString()) |
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