(self, note)
Given a note, uses the model to predict the most probable next note.

    Args:
      note: A one-hot encoding of the note.
    Returns:
      Next note in the same format.
Given a note, uses the model to predict the most probable next note.
Given a note uses the model to predict the most probable next note .
def get_next_note_from_note(self, note):
    """Given a note, uses the model to predict the most probable next note.

    Args:
      note: A one-hot encoding of the note.
    Returns:
      Next note in the same format.
    """
    with self.graph.as_default():
      with tf.variable_scope(self.scope, reuse=True):
        singleton_lengths = np.full(self.batch_size, 1, dtype=int)

        input_batch = np.reshape(note,
                                 (self.batch_size, 1, rl_tuner_ops.NUM_CLASSES))

        softmax, self.state_value = self.session.run(
            [self.softmax, self.state_tensor],
            {self.melody_sequence: input_batch,
             self.initial_state: self.state_value,
             self.lengths: singleton_lengths})

        return self.get_note_from_softmax(softmax)
def get_next_note_from_note ( self , note ) : with self . graph . as_default ( ) : with tf . variable_scope ( self . scope , reuse = True ) : singleton_lengths = np . full ( self . batch_size , 1 , dtype = int ) input_batch = np . reshape ( note , ( self . batch_size , 1 , rl_tuner_ops . NUM_CLASSES ) ) softmax , self . state_value = self . session . run ( [ self . softmax , self . state_tensor ] , { self . melody_sequence : input_batch , self . initial_state : self . state_value , self . lengths : singleton_lengths } ) return self . get_note_from_softmax ( softmax )