Python Librosa Keras神經網絡錯誤：數組的索引過多

Question

我最近嘗試進行一個實驗，該實驗使用Keras用Python IDE IDLE編寫的神經網絡用於分析GTZAN歌曲數據集。 我試圖改變層次，以查看是否對性能有影響。 我的實驗基於一篇詳細介紹該項目基礎的文章：

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

根據另一位關於Stack Overflow的開發人員的建議，我尋求scikit-learn模塊的幫助。

我的代碼如下所示：

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))

在運行程序時，Python開始打印預期的響應：

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)

但這被一條錯誤消息打斷了：

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

感謝您提供有關此問題的幫助。

Answer 1

這是因為train_index和test_index是一維數組，其中包含要在train和test resp中使用的樣本的索引。 它們本身不是數據。 您試圖訪問一維數組上的第二個軸（通過[:,:-10:] ）是問題所在。

請在一行中指定您要做什么：

train_input = train_index[:,:-10:]