Question

我正在尝试完成一个实验，使用Keras创建一个神经网络音乐分析程序，并修改神经网络的层数以发现对演奏的影响。 This is my source用于该程序。

我之前在程序中遇到了多个错误，并且在Stack Overflow上的另一位开发人员的建议下，我决定寻求sklearn库部分的帮助。

这是我正在使用的代码：

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
from sklearn.model_selection import train_test_split,        
StratifiedShuffleSplit, StratifiedKFold



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, 
    return_inverse=True)
    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

alldata = np.column_stack((features, labels))
sss = StratifiedShuffleSplit(n_splits=1, test_size=0.20,  
random_state=37)
for train_index, test_index in sss.split(X, y):
  X_train, X_test = X[train_index], X[test_index]
  y_train, y_test = y[train_index], y[test_index]


print(np.shape(train))
print(np.shape(test))

train_input = test[:,:-10]
train_labels = train[:,-10:]

test_input = test[:,:-10]
test_labels = test[:,-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))

Python开始打印预期的响应：

Using TensorFlow backend.
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)

但这被一条错误消息打断了：

Traceback (most recent call last):
  File "/Users/surengrigorian/Documents/Stage1.py", line 70, in <module>
    print(np.shape(train))
NameError: name 'train' is not defined

感谢您在此问题上可以提供的任何帮助。

Answer 1

由于没有名为train的NumPy数组，因此遇到了上述错误。

因此，您使用以下行生成了数据集：

features, labels = generate_features_and_labels()

但是，当您将数据集划分为训练和测试数据集时，您将它们存储在以下代码段的X_train, X_test和y_train, y_test中。

for train_index, test_index in sss.split(X, y):
  X_train, X_test = X[train_index], X[test_index]
  y_train, y_test = y[train_index], y[test_index]

此外，上述数组自前一行起将不包含任何内容，

alldata = np.column_stack((features, labels))
sss = StratifiedShuffleSplit(n_splits=1, test_size=0.20,  
random_state=37)

表示您将数据集打包在alldata中，但是实际上使用StratifiedShuffleSplit拆分的数据是X和y，其中您还没有数组X开头。

鉴于此，您可以按照以下方式拆分它们：

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]

然后，您可以按照以下方式检查其形状

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

NameError：名称“ train”未定义

1 个答案: