49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
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# Copyright 2016 Google Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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r"""Create a dataset for training and evaluating the attention RNN model.
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Example usage:
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$ bazel build magenta/models/attention_rnn:attention_rnn_create_dataset
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$ ./bazel-bin/magenta/models/attention_rnn/attention_rnn_create_dataset \
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--input=/tmp/note_sequences.tfrecord \
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--output_dir=/tmp/attention_rnn
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--eval_ratio=0.10
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See /magenta/models/shared/melody_rnn_create_dataset.py for flag descriptions.
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"""
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# internal imports
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import tensorflow as tf
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from magenta.models.attention_rnn import attention_rnn_encoder_decoder
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from magenta.models.shared import melody_rnn_create_dataset
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def get_pipeline():
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return melody_rnn_create_dataset.get_pipeline(
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attention_rnn_encoder_decoder.MelodyEncoderDecoder())
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def main(unused_argv):
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melody_rnn_create_dataset.run_from_flags(get_pipeline())
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def console_entry_point():
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tf.app.run(main)
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if __name__ == '__main__':
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console_entry_point()
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