《深度学习——Andrew Ng》第五课第一周编程作业_3_Jazz improvisation with LSTM

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于 2018-05-05 21:22:18 发布 · 1k 阅读

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文章标签：

#RNN #LSTM #深度学习 #人工智能

本文探讨了如何使用LSTM进行Jazz即兴创作，通过深度学习模型实现音乐创作的创新应用。详细介绍了LSTM的原理，并提供了Pycharm环境下的编程实践。

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LSTM

这里写图片描述

程序Pycharm版

from __future__ import print_function
import IPython
import sys
from music21 import *                   # music21为一个库，需要用pip进行安装
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


# GRADED FUNCTION: djmodel=
def djmodel(Tx, n_a, n_values):
    """
    Implement the model

    Arguments:
    Tx -- length of the sequence in a corpus
    n_a -- the number of activations used in our model
    n_values -- number of unique values in the music data

    Returns:
    model -- a keras model with the
    """

    # Define the input of your model with a shape
    X = Input(shape=(