def build_graph(self):
"""Constructs the portion of the graph that belongs to this model."""
tf.logging.info('Initializing melody RNN graph for scope %s',
self.scope)
with self.graph.as_default():
with tf.device(lambda op: ''):
with tf.variable_scope(self.scope):
# Make an LSTM cell with the number and size of layers specified in
# hparams.
if self.note_rnn_type == 'basic_rnn':
self.cell = events_rnn_graph.make_rnn_cell(
self.hparams.rnn_layer_sizes)
else:
self.cell = rl_tuner_ops.make_rnn_cell(
self.hparams.rnn_layer_sizes)
# Shape of melody_sequence is batch size, melody length, number of
# output note actions.
self.melody_sequence = tf.placeholder(
tf.float32, [None, None, self.hparams.one_hot_length],
name='melody_sequence')
self.lengths = tf.placeholder(tf.int32, [None],
name='lengths')
self.initial_state = tf.placeholder(
tf.float32, [None, self.cell.state_size],
name='initial_state')
if self.training_file_list is not None:
# Set up a tf queue to read melodies from the training data tfrecord
(self.train_sequence, self.train_labels,
self.train_lengths
) = sequence_example_lib.get_padded_batch(
self.training_file_list, self.hparams.batch_size,
self.hparams.one_hot_length)
# Closure function is used so that this part of the graph can be
# re-run in multiple places, such as __call__.
def run_network_on_melody(m_seq,
lens,
initial_state,
swap_memory=True,
parallel_iterations=1):
"""Internal function that defines the RNN network structure.
Args:
m_seq: A batch of melody sequences of one-hot notes.
lens: Lengths of the melody_sequences.
initial_state: Vector representing the initial state of the RNN.
swap_memory: Uses more memory and is faster.
parallel_iterations: Argument to tf.nn.dynamic_rnn.
Returns:
Output of network (either softmax or logits) and RNN state.
"""
outputs, final_state = tf.nn.dynamic_rnn(
self.cell,
m_seq,
sequence_length=lens,
initial_state=initial_state,
swap_memory=swap_memory,
parallel_iterations=parallel_iterations)
outputs_flat = tf.reshape(
outputs, [-1, self.hparams.rnn_layer_sizes[-1]])
if self.note_rnn_type == 'basic_rnn':
linear_layer = tf.contrib.layers.linear
else:
linear_layer = tf.contrib.layers.legacy_linear
logits_flat = linear_layer(outputs_flat,
self.hparams.one_hot_length)
return logits_flat, final_state
(self.logits, self.state_tensor) = run_network_on_melody(
self.melody_sequence, self.lengths, self.initial_state)
self.softmax = tf.nn.softmax(self.logits)
self.run_network_on_melody = run_network_on_melody
if self.training_file_list is not None:
# Does not recreate the model architecture but rather uses it to feed
# data from the training queue through the model.
with tf.variable_scope(self.scope, reuse=True):
zero_state = self.cell.zero_state(
batch_size=self.hparams.batch_size,
dtype=tf.float32)
(self.train_logits,
self.train_state) = run_network_on_melody(
self.train_sequence, self.train_lengths,
zero_state)
self.train_softmax = tf.nn.softmax(self.train_logits)
def build_graph(self):
"""Constructs the portion of the graph that belongs to this model."""
tf.logging.info('Initializing melody RNN graph for scope %s', self.scope)
with self.graph.as_default():
with tf.device(lambda op: ''):
with tf.variable_scope(self.scope):
# Make an LSTM cell with the number and size of layers specified in
# hparams.
if self.note_rnn_type == 'basic_rnn':
self.cell = events_rnn_graph.make_rnn_cell(
self.hparams.rnn_layer_sizes)
else:
self.cell = rl_tuner_ops.make_rnn_cell(self.hparams.rnn_layer_sizes)
# Shape of melody_sequence is batch size, melody length, number of
# output note actions.
self.melody_sequence = tf.placeholder(tf.float32,
[None, None,
self.hparams.one_hot_length],
name='melody_sequence')
self.lengths = tf.placeholder(tf.int32, [None], name='lengths')
self.initial_state = tf.placeholder(tf.float32,
[None, self.cell.state_size],
name='initial_state')
if self.training_file_list is not None:
# Set up a tf queue to read melodies from the training data tfrecord
(self.train_sequence,
self.train_labels,
self.train_lengths) = sequence_example_lib.get_padded_batch(
self.training_file_list, self.hparams.batch_size,
self.hparams.one_hot_length)
# Closure function is used so that this part of the graph can be
# re-run in multiple places, such as __call__.
def run_network_on_melody(m_seq,
lens,
initial_state,
swap_memory=True,
parallel_iterations=1):
"""Internal function that defines the RNN network structure.
Args:
m_seq: A batch of melody sequences of one-hot notes.
lens: Lengths of the melody_sequences.
initial_state: Vector representing the initial state of the RNN.
swap_memory: Uses more memory and is faster.
parallel_iterations: Argument to tf.nn.dynamic_rnn.
Returns:
Output of network (either softmax or logits) and RNN state.
"""
outputs, final_state = tf.nn.dynamic_rnn(
self.cell,
m_seq,
sequence_length=lens,
initial_state=initial_state,
swap_memory=swap_memory,
parallel_iterations=parallel_iterations)
outputs_flat = tf.reshape(outputs,
[-1, self.hparams.rnn_layer_sizes[-1]])
if self.note_rnn_type == 'basic_rnn':
linear_layer = tf.contrib.layers.linear
else:
linear_layer = tf.contrib.layers.legacy_linear
logits_flat = linear_layer(
outputs_flat, self.hparams.one_hot_length)
return logits_flat, final_state
(self.logits, self.state_tensor) = run_network_on_melody(
self.melody_sequence, self.lengths, self.initial_state)
self.softmax = tf.nn.softmax(self.logits)
self.run_network_on_melody = run_network_on_melody
if self.training_file_list is not None:
# Does not recreate the model architecture but rather uses it to feed
# data from the training queue through the model.
with tf.variable_scope(self.scope, reuse=True):
zero_state = self.cell.zero_state(
batch_size=self.hparams.batch_size, dtype=tf.float32)
(self.train_logits, self.train_state) = run_network_on_melody(
self.train_sequence, self.train_lengths, zero_state)
self.train_softmax = tf.nn.softmax(self.train_logits)