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我需要音樂生成器代碼有助於從https://github.com/llSourcell/Music_Generator_Demo音樂發生器演示>類型錯誤：切片索引必須是整數或無或有__index__方法
我試着問原來的海報/社區，但我沒有還幫助 呢。
我是新來的蟒蛇。我不明白髮生了什麼事。我想要做的就是運行並獲得一些輸出。
我使用PyCharm
錯誤：
/Users/ihsan/anaconda/bin/python /Users/ihsan/GitHub/Music_Generator_Demo/rbm_chords.py
99%|█████████▉| 125/126 [00:03<00:00, 41.15it/s]122 songs processed
100%|██████████| 126/126 [00:03<00:00, 38.66it/s]
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
0%| | 0/200 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/Users/ihsan/GitHub/Music_Generator_Demo/rbm_chords.py", line 116, in <module>
song = song[:np.floor(song.shape[0]/num_timesteps)*num_timesteps]
TypeError: slice indices must be integers or None or have an __index__ method
Process finished with exit code 1
CODE：
#This file is heavily based on Daniel Johnson's midi manipulation code in https://github.com/hexahedria/biaxial-rnn-music-composition
import numpy as np
import pandas as pd
import msgpack
import glob
import tensorflow as tf
from tensorflow.python.ops import control_flow_ops
from tqdm import tqdm
###################################################
# In order for this code to work, you need to place this file in the same
# directory as the midi_manipulation.py file and the Pop_Music_Midi directory
import midi_manipulation
def get_songs(path):
files = glob.glob('{}/*.mid*'.format(path))
songs = []
for f in tqdm(files):
try:
song = np.array(midi_manipulation.midiToNoteStateMatrix(f))
if np.array(song).shape[0] > 50:
songs.append(song)
except Exception as e:
raise e
return songs
songs = get_songs('Pop_Music_Midi') #These songs have already been converted from midi to msgpack
print "{} songs processed".format(len(songs))
###################################################
### HyperParameters
# First, let's take a look at the hyperparameters of our model:
lowest_note = midi_manipulation.lowerBound #the index of the lowest note on the piano roll
highest_note = midi_manipulation.upperBound #the index of the highest note on the piano roll
note_range = highest_note-lowest_note #the note range
num_timesteps = 15 #This is the number of timesteps that we will create at a time
n_visible = 2*note_range*num_timesteps #This is the size of the visible layer.
n_hidden = 50 #This is the size of the hidden layer
num_epochs = 200 #The number of training epochs that we are going to run. For each epoch we go through the entire data set.
batch_size = 100 #The number of training examples that we are going to send through the RBM at a time.
lr = tf.constant(0.005, tf.float32) #The learning rate of our model
### Variables:
# Next, let's look at the variables we're going to use:
x = tf.placeholder(tf.float32, [None, n_visible], name="x") #The placeholder variable that holds our data
W = tf.Variable(tf.random_normal([n_visible, n_hidden], 0.01), name="W") #The weight matrix that stores the edge weights
bh = tf.Variable(tf.zeros([1, n_hidden], tf.float32, name="bh")) #The bias vector for the hidden layer
bv = tf.Variable(tf.zeros([1, n_visible], tf.float32, name="bv")) #The bias vector for the visible layer
#### Helper functions.
#This function lets us easily sample from a vector of probabilities
def sample(probs):
#Takes in a vector of probabilities, and returns a random vector of 0s and 1s sampled from the input vector
return tf.floor(probs + tf.random_uniform(tf.shape(probs), 0, 1))
#This function runs the gibbs chain. We will call this function in two places:
# - When we define the training update step
# - When we sample our music segments from the trained RBM
def gibbs_sample(k):
#Runs a k-step gibbs chain to sample from the probability distribution of the RBM defined by W, bh, bv
def gibbs_step(count, k, xk):
#Runs a single gibbs step. The visible values are initialized to xk
hk = sample(tf.sigmoid(tf.matmul(xk, W) + bh)) #Propagate the visible values to sample the hidden values
xk = sample(tf.sigmoid(tf.matmul(hk, tf.transpose(W)) + bv)) #Propagate the hidden values to sample the visible values
return count+1, k, xk
#Run gibbs steps for k iterations
ct = tf.constant(0) #counter
[_, _, x_sample] = control_flow_ops.while_loop(lambda count, num_iter, *args: count < num_iter,
gibbs_step, [ct, tf.constant(k), x])
#This is not strictly necessary in this implementation, but if you want to adapt this code to use one of TensorFlow's
#optimizers, you need this in order to stop tensorflow from propagating gradients back through the gibbs step
x_sample = tf.stop_gradient(x_sample)
return x_sample
### Training Update Code
# Now we implement the contrastive divergence algorithm. First, we get the samples of x and h from the probability distribution
#The sample of x
x_sample = gibbs_sample(1)
#The sample of the hidden nodes, starting from the visible state of x
h = sample(tf.sigmoid(tf.matmul(x, W) + bh))
#The sample of the hidden nodes, starting from the visible state of x_sample
h_sample = sample(tf.sigmoid(tf.matmul(x_sample, W) + bh))
#Next, we update the values of W, bh, and bv, based on the difference between the samples that we drew and the original values
size_bt = tf.cast(tf.shape(x)[0], tf.float32)
W_adder = tf.multiply(lr/size_bt, tf.subtract(tf.matmul(tf.transpose(x), h), tf.matmul(tf.transpose(x_sample), h_sample)))
bv_adder = tf.multiply(lr/size_bt, tf.reduce_sum(tf.subtract(x, x_sample), 0, True))
bh_adder = tf.multiply(lr/size_bt, tf.reduce_sum(tf.subtract(h, h_sample), 0, True))
#When we do sess.run(updt), TensorFlow will run all 3 update steps
updt = [W.assign_add(W_adder), bv.assign_add(bv_adder), bh.assign_add(bh_adder)]
### Run the graph!
# Now it's time to start a session and run the graph!
with tf.Session() as sess:
#First, we train the model
#initialize the variables of the model
init = tf.global_variables_initializer()
sess.run(init)
#Run through all of the training data num_epochs times
for epoch in tqdm(range(num_epochs)):
for song in songs:
#The songs are stored in a time x notes format. The size of each song is timesteps_in_song x 2*note_range
#Here we reshape the songs so that each training example is a vector with num_timesteps x 2*note_range elements
song = np.array(song)
song = song[:np.floor(song.shape[0]/num_timesteps)*num_timesteps]
song = np.reshape(song, [song.shape[0]/num_timesteps, song.shape[1]*num_timesteps])
#Train the RBM on batch_size examples at a time
for i in range(1, len(song), batch_size):
tr_x = song[i:i+batch_size]
sess.run(updt, feed_dict={x: tr_x})
#Now the model is fully trained, so let's make some music!
#Run a gibbs chain where the visible nodes are initialized to 0
sample = gibbs_sample(1).eval(session=sess, feed_dict={x: np.zeros((10, n_visible))})
for i in range(sample.shape[0]):
if not any(sample[i,:]):
continue
#Here we reshape the vector to be time x notes, and then save the vector as a midi file
S = np.reshape(sample[i,:], (num_timesteps, 2*note_range))
midi_manipulation.noteStateMatrixToMidi(S, "generated_chord_{}".format(i))