Sequence Models(Week1)编程--Improvise a Jazz Solo with an LSTM Network

最新推荐文章于 2022-02-24 21:18:11 发布

Zhen大虾

最新推荐文章于 2022-02-24 21:18:11 发布

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文章标签： LSTM

本博客介绍了一种使用长短期记忆(LSTM)神经网络生成音乐的方法，特别是即兴爵士乐独奏。通过加载音乐数据集，构建LSTM模型，并训练网络来预测音乐序列，实现了音乐创作的自动化。

Improvise a Jazz Solo with an LSTM Network

使用LSTM生成音乐

from __future__ import print_function
import IPython
import sys
from music21 import *
import numpy as np
from grammar import *
from qa import *
from preprocess import *
from music_utils import *
from data_utils import *
from keras.models import load_model, Model
from keras.layers import Dense, Activation, Dropout, Input, LSTM, Reshape, Lambda, RepeatVector
from keras.initializers import glorot_uniform
from keras.utils import to_categorical
from keras.optimizers import Adam
from keras import backend as K

1 - Problem statement

X, Y, n_values, indices_values = load_music_utils()
print('shape of X:', X.shape)
print('number of training examples:', X.shape[0])
print('Tx (length of sequence):', X.shape[1])
print('total # of unique values:', n_values)
print('Shape of Y:', Y.shape)

n_a = 64

reshapor = Reshape((1, 78)) # Used in Step 2.B of djmodel(), below
LSTM_cell = LSTM(n_a, return_state = True) # Used in Step 2.C
densor = Dense(n_values, activation='softmax')