def __init__(self,
graph,
scope,
checkpoint_dir,
checkpoint_file=None,
midi_primer=None,
training_file_list=None,
hparams=None,
note_rnn_type='default',
checkpoint_scope='rnn_model'):
"""Initialize by building the graph and loading a previous checkpoint.
Args:
graph: A tensorflow graph where the MelodyRNN's graph will be added.
scope: The tensorflow scope where this network will be saved.
checkpoint_dir: Path to the directory where the checkpoint file is saved.
checkpoint_file: Path to a checkpoint file to be used if none can be
found in the checkpoint_dir
midi_primer: Path to a single midi file that can be used to prime the
model.
training_file_list: List of paths to tfrecord files containing melody
training data.
hparams: A tf_lib.HParams object. Must match the hparams used to create
the checkpoint file.
note_rnn_type: If 'default', will use the basic LSTM described in the
research paper. If 'basic_rnn', will assume the checkpoint is from a
Magenta basic_rnn model.
checkpoint_scope: The scope in lstm which the model was originally defined
when it was first trained.
"""
self.graph = graph
self.session = None
self.saver = None
self.scope = scope
self.batch_size = 1
self.midi_primer = midi_primer
self.checkpoint_scope = checkpoint_scope
self.note_rnn_type = note_rnn_type
self.training_file_list = training_file_list
self.checkpoint_dir = checkpoint_dir
self.checkpoint_file = checkpoint_file
if hparams is not None:
tf.logging.info('Using custom hparams')
self.hparams = hparams
else:
tf.logging.info('Empty hparams string. Using defaults')
self.hparams = rl_tuner_ops.default_hparams()
self.build_graph()
self.state_value = self.get_zero_state()
if midi_primer is not None:
self.load_primer()
self.variable_names = rl_tuner_ops.get_variable_names(
self.graph, self.scope)
self.transpose_amount = 0
def __init__(self, graph, scope, checkpoint_dir, checkpoint_file=None,
midi_primer=None, training_file_list=None, hparams=None,
note_rnn_type='default', checkpoint_scope='rnn_model'):
"""Initialize by building the graph and loading a previous checkpoint.
Args:
graph: A tensorflow graph where the MelodyRNN's graph will be added.
scope: The tensorflow scope where this network will be saved.
checkpoint_dir: Path to the directory where the checkpoint file is saved.
checkpoint_file: Path to a checkpoint file to be used if none can be
found in the checkpoint_dir
midi_primer: Path to a single midi file that can be used to prime the
model.
training_file_list: List of paths to tfrecord files containing melody
training data.
hparams: A tf_lib.HParams object. Must match the hparams used to create
the checkpoint file.
note_rnn_type: If 'default', will use the basic LSTM described in the
research paper. If 'basic_rnn', will assume the checkpoint is from a
Magenta basic_rnn model.
checkpoint_scope: The scope in lstm which the model was originally defined
when it was first trained.
"""
self.graph = graph
self.session = None
self.scope = scope
self.batch_size = 1
self.midi_primer = midi_primer
self.checkpoint_scope = checkpoint_scope
self.note_rnn_type = note_rnn_type
self.training_file_list = training_file_list
self.checkpoint_dir = checkpoint_dir
self.checkpoint_file = checkpoint_file
if hparams is not None:
tf.logging.info('Using custom hparams')
self.hparams = hparams
else:
tf.logging.info('Empty hparams string. Using defaults')
self.hparams = rl_tuner_ops.default_hparams()
self.build_graph()
self.state_value = self.get_zero_state()
if midi_primer is not None:
self.load_primer()
self.variable_names = rl_tuner_ops.get_variable_names(self.graph,
self.scope)
self.transpose_amount = 0
