吴恩达 第5课 week1 Improvise a Jazz Solo with an LSTM Network - v1

# -*- coding:utf-8 -*- 
#Author: shenying
#Date: 18-7-20 下午2:50

from __future__ import  print_function
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
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

def djmodel(Tx,n_a,n_values):
    X=Input(shape=(Tx,n_values))
    a0=Input(shape=(n_a,),name='a0')
    c0=Input(shape=(n_a,),name='c0')
    a=a0
    c=c0

    outputs=[]
    for t in range(Tx):
        x=Lambda(lambda x:X[:,t,:])(X)
        x=reshapor(x)
        a,_,c=LSTM_cell(x,initial_state=[a,c])
        out=densor(a)
        outputs.append(out)
    model=Model([X,a0,c0],outputs)
    return model
def music_inference_model(LSTM_cell,densor,n_values=78,n_a=64,Ty=100):
    x0=Input(shape=(1,n_values))

    a0=Input(shape=(n_a,),name='a0')
    c0=Input(shape=(n_a,),name='c0')
    a=a0
    c=c0
    x=x0

    outputs=[]
    for t in range(Ty):
        a,_,c=LSTM_cell(x,initial_state=[a,c])
        out=densor(a)
        outputs.append(out)
        x=Lambda(one_hot)(out)

    inferentce_model=Model([x0,a0,c0],outputs)
    return inferentce_model
def predict_and_sample(inference_model,x_initializer=x_initializer,a_initilizer=a_initializer,
                       c_initializer=c_initializer):
    pred=inference_model.predict([x_initializer,a_initializer,c_initializer])
    indices=np.argmax(pred,axis=-1)
    result=to_categorical(indices,num_classes=78)
    return result,indices
if __name__=="__main__":
    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))                          # 2.b
    LSTM_cell=LSTM(n_a,return_state=True)             # 2.c
    densor=Dense(n_values,activation='softmax')       # 2.d
    model=djmodel(Tx=30,n_a=64,n_values=78)
    # model.summary()
    opt=Adam(lr=0.01,beta_1=0.9,beta_2=0.999,decay=0.01)
    model.compile(optimizer=opt,loss='categorical_crossentropy',metrics=['accuracy'])

    m=60
    a0=np.zeros((m,n_a))
    c0=np.zeros((m,n_a))
    model.fit([X,a0,c0],list(Y),epochs=100)
    inference_model=music_inference_model(LSTM_cell,densor,n_values=78,n_a=64,Ty=100)
    x_initializer=np.zeros((1,1,78))
    a_initializer=np.zeros((1,n_a))
    c_initializer=np.zeros((1,n_a))
    results, indices = predict_and_sample(inference_model, x_initializer, a_initializer, c_initializer)
    print("np.argmax(results[12]) =", np.argmax(results[12]))
    print("np.argmax(results[17]) =", np.argmax(results[17]))
    print("list(indices[12:18]) =", list(indices[12:18]))
    out_stream = generate_music(inference_model)