我正在使用Tensorflow生成.mid文件。追溯看起来像 this （我无法将其复制到问题中，因为我将PuTTy与tmux一起使用，并且该软件不允许我复制长度超过行的内容）。 我正在使用的代码：
""" This module prepares midi file data and feeds it to the neural
network for training """
import glob
import pickle
import numpy
from music21 import converter, instrument, note, chord
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.layers import Activation
from keras.utils import np_utils
from keras.callbacks import ModelCheckpoint
def train_network():
""" Train a Neural Network to generate music """
notes = get_notes()
# get amount of pitch names
n_vocab = len(set(notes))
network_input, network_output = prepare_sequences(notes, n_vocab)
model = create_network(network_input, n_vocab)
train(model, network_input, network_output)
def get_notes():
""" Get all the notes and chords from the midi files in the ./midi_songs directory """
notes = []
for file in glob.glob("midi_songs/*.mid"):
midi = converter.parse(file)
print("Parsing %s" % file)
notes_to_parse = None
try: # file has instrument parts
s2 = instrument.partitionByInstrument(midi)
notes_to_parse = s2.parts[0].recurse()
except: # file has notes in a flat structure
notes_to_parse = midi.flat.notes
for element in notes_to_parse:
if isinstance(element, note.Note):
notes.append(str(element.pitch))
elif isinstance(element, chord.Chord):
notes.append('.'.join(str(n) for n in element.normalOrder))
with open('data/notes', 'wb') as filepath:
pickle.dump(notes, filepath)
return notes
def prepare_sequences(notes, n_vocab):
""" Prepare the sequences used by the Neural Network """
sequence_length = 100
# get all pitch names
pitchnames = sorted(set(item for item in notes))
# create a dictionary to map pitches to integers
note_to_int = dict((note, number) for number, note in enumerate(pitchnames))
network_input = []
network_output = []
# create input sequences and the corresponding outputs
for i in range(0, len(notes) - sequence_length, 1):
sequence_in = notes[i:i + sequence_length]
sequence_out = notes[i + sequence_length]
network_input.append([note_to_int[char] for char in sequence_in])
network_output.append(note_to_int[sequence_out])
n_patterns = len(network_input)
# reshape the input into a format compatible with LSTM layers
network_input = numpy.reshape(network_input, (n_patterns, sequence_length, 1))
# normalize input
network_input = network_input / float(n_vocab)
network_output = np_utils.to_categorical(network_output)
return (network_input, network_output)
def create_network(network_input, n_vocab):
""" create the structure of the neural network """
model = Sequential()
model.add(LSTM(
512,
input_shape=(network_input.shape[1], network_input.shape[2]),
return_sequences=True
))
model.add(Dropout(0.3))
model.add(LSTM(512, return_sequences=True))
model.add(Dropout(0.3))
model.add(LSTM(512))
model.add(Dense(256))
model.add(Dropout(0.3))
model.add(Dense(n_vocab))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
return model
def train(model, network_input, network_output):
""" train the neural network """
filepath = "weights-improvement-{epoch:02d}-{loss:.4f}-bigger.hdf5"
checkpoint = ModelCheckpoint(
filepath,
monitor='loss',
verbose=0,
save_best_only=True,
mode='min'
)
callbacks_list = [checkpoint]
model.fit(network_input, network_output, epochs=200, batch_size=64, callbacks=callbacks_list)
if __name__ == '__main__':
train_network()
我确定我已经安装了每个软件包，我尝试更新pip并重新安装所有功能，但是没有运气。您能告诉我这段代码是什么问题吗？