Question

我最近尝试执行一个实验，该实验使用Keras在Python IDE IDLE中编写的神经网络用于分析GTZAN歌曲数据集。我试图改变层次，以查看是否对性能有影响。我的实验基于一篇详细介绍该项目基础的文章：

https://medium.com/@navdeepsingh_2336/identifying-the-genre-of-a-song-with-neural-networks-851db89c42f0

根据Stack Overflow上另一位开发人员的建议，我寻求scikit-learn模块的帮助。

我的代码显示在这里：

import librosa
import librosa.feature
import librosa.display
import glob
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.utils.np_utils import to_categorical

def display_mfcc(song):
    y, _ = librosa.load(song)
    mfcc = librosa.feature.mfcc(y)

    plt.figure(figsize=(10, 4))
    librosa.display.specshow(mfcc, x_axis='time', y_axis='mel')
    plt.colorbar()
    plt.title(song)
    plt.tight_layout()
    plt.show()


def extract_features_song(f):
    y, _ = librosa.load(f)

    mfcc = librosa.feature.mfcc(y)
    mfcc /= np.amax(np.absolute(mfcc))

    return np.ndarray.flatten(mfcc)[:25000]

def generate_features_and_labels():
    all_features = []
    all_labels = []
    genres = ['blues', 'classical', 'country', 'disco', 'hiphop',
    'jazz', 'metal', 'pop', 'reggae', 'rock']

    for genre in genres:
        sound_files = glob.glob('genres/'+genre+'/*.au')
        print('Processing %d songs in %s genre...' % 
        (len(sound_files), genre))
        for f in sound_files:
            features = extract_features_song(f)
            all_features.append(features)
            all_labels.append(genre)

    label_uniq_ids, label_row_ids = np.unique(all_labels,   
    (len(sound_files), genre))
    label_row_ids = label_row_ids.astype(np.int32, copy=False)
    onehot_labels = to_categorical(label_row_ids, 
    len(label_uniq_ids))

    return np.stack(all_features), onehot_labels


features, labels = generate_features_and_labels()

print(np.shape(features))
print(np.shape(labels))

training_split = 0.8

x = features
y = labels

sss = StratifiedShuffleSplit(n_splits=1, test_size=0.20,     
random_state=37)

for train_index, test_index in sss.split(features, labels):
  x_train, x_test = features[train_index], features[test_index]
  y_train, y_test = labels[train_index], labels[test_index]

print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)

train_input = train_index[:,:-10:]
train_labels = train_index[:,-10:]

test_input = test_index[:,:-10:]
test_labels = test_index[:,-10:]

print(np.shape(train_input))
print(np.shape(train_labels))

model = Sequential([
    Dense(100, input_dim=np.shape(train_input)[1]),
    Activation('relu'),
    Dense(10),
    Activation('softmax'),
    ])


model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
print(model.summary())

model.fit(train_input, train_labels, epochs=10, batch_size=32,
          validation_split=0.2) 
loss, acc = model.evaluate(test_input, test_labels, batch_size=32)

print('Done!')
print('Loss: %.4f, accuracy: %.4f' % (loss, acc))

在我运行程序时，Python开始打印预期的响应：

Processing 100 songs in blues genre...
Processing 100 songs in classical genre...
Processing 100 songs in country genre...
Processing 100 songs in disco genre...
Processing 100 songs in hiphop genre...
Processing 100 songs in jazz genre...
Processing 100 songs in metal genre...
Processing 100 songs in pop genre...
Processing 100 songs in reggae genre...
Processing 100 songs in rock genre...
(1000, 25000)
(1000, 10)
(800, 25000) (200, 25000) (800, 10) (200, 10)

但这被一条错误消息打断了：

Traceback (most recent call last):
  File "/Users/surengrigorian/Documents/Stage1.py", line 74, in <module>
    train_input = train_index[:,:-10:]
IndexError: too many indices for array

感谢您提供有关此问题的帮助。

Answer 1

这是因为train_index和test_index是一维数组，其中包含要在训练和测试响应中使用的样本索引。它们本身不是数据。您试图（通过执行[:,:-10:]）访问一维数组上的第二个轴是问题。

请在该行中指定您要做什么：

train_input = train_index[:,:-10:]

Python Librosa Keras神经网络错误：数组的索引过多

1 个答案: