神经网络正则化子L1和L2

Question

我在做音乐流派分类。我用我的模型构建了一个文件.h5，它是一个神经网络。现在我想用它。以下是预测音乐类型的代码：

#%%
import librosa
import tensorflow as tf
import numpy as np
from collections import Counter
SAVED_MODEL_PATH = "modelLast.h5"
SAMPLES_TO_CONSIDER = 22050
DURATION = 30
SAMPLE_PER_TRACK = SAMPLES_TO_CONSIDER * DURATION
#%%
class _Keyword_Spotting_Service:
"""Singleton class for keyword spotting inference with trained models.
:param model: Trained model
"""
model = None
_mapping = [
"blues",
"classical",
"country",
"disco",
"hiphop",
"jazz",
"metal",
"pop",
"reggae",
"rock"
]
_instance = None
def predict(self, file_path, num_mfcc=13, n_fft=2048, hop_length=512):
"""Extract MFCCs from audio file.
:param file_path (str): Path of audio file
:param num_mfcc (int): # of coefficients to extract
:param n_fft (int): Interval we consider to apply STFT. Measured in # of samples
:param hop_length (int): Sliding window for STFT. Measured in # of samples
:return MFCCs (ndarray): 2-dim array with MFCC data of shape (# time steps, # coefficients)
"""
num_segments = 10
num_samples_per_segment = int(SAMPLE_PER_TRACK / num_segments) # num  of segments
# load audio file
signal, sample_rate = librosa.load(file_path)

# a faire
predicted_indexes = [0] * num_segments
predicted_mfcc = [0] * num_segments
for s in range(num_segments):
start_sample = num_samples_per_segment * s  # s=0 -> 0
finish_sample = start_sample + num_samples_per_segment  # s=0 -> num_samples_per_segment
mfcc = librosa.feature.mfcc(signal[start_sample:finish_sample],
sample_rate,
n_fft=n_fft, n_mfcc=num_mfcc,
hop_length=hop_length)
MFCCs = mfcc.T
MFCCs = MFCCs[np.newaxis, ..., np.newaxis]

# get the predicted label
predictions = self.model.predict(MFCCs)                
print ("nPredictions: {}".format(predictions))

predicted_indexes [s] = np.argmax(predictions)
predicted_mfcc [s] = np.max(predictions)

print("nIndex list: {}".format(predicted_indexes))
print("nIndex list Mfccs : {}".format(predicted_mfcc))


#predicted_index = np.bincount(predicted_indexes).argmax()   # Méthode pour avoir l'index qui se répète le plus de fois

# Ajout de précision du code : 
"""
Nous ressort de la liste les indexs qui se répètent le plus de fois et s'il y a plusieurs doublons
triplés, compare la valeurs des indexs et choisi l'index à la valeur la plus élevée
Voir le code python Liste.py pour plus de précision
"""

indices = list(map(lambda x: x[0], Counter(predicted_indexes).most_common()))
counts = list(map(lambda x: x[1], Counter(predicted_indexes).most_common()))

print("nIndices présents dans la liste : ", indices)
print("nNombre d'apparition des indices : ", counts)

max_indices = [indices[i] for i, x in enumerate(counts) if x == max(counts)]
result_mcfccs = []
for idx, id in enumerate(predicted_indexes):
if id in max_indices:
result_mcfccs.append(predicted_mfcc[idx])

result = max(result_mcfccs)

print("n Indice se répétant le plus : ", max_indices)
print("nValeur maximale de l'indice se répétant le plus : ",result)


indice = predicted_mfcc.index(result)
print("nEmplacement de la valeur dans la lsite :",indice)
F= predicted_indexes.pop(indice)
print("nRésultat final : ", F)

predicted_keyword = self._mapping[F]

return predicted_keyword

def Keyword_Spotting_Service():
"""Factory function for Keyword_Spotting_Service class.
:return _Keyword_Spotting_Service._instance (_Keyword_Spotting_Service):
"""
# ensure an instance is created only the first time the factory function is called
if _Keyword_Spotting_Service._instance is None:
_Keyword_Spotting_Service._instance = _Keyword_Spotting_Service()
_Keyword_Spotting_Service.model = tf.keras.models.load_model(SAVED_MODEL_PATH)
return _Keyword_Spotting_Service._instance

if __name__ == "__main__":
# create 2 instances of the keyword spotting service
kss = Keyword_Spotting_Service()
kss1 = Keyword_Spotting_Service()
# check that different instances of the keyword spotting service point back to the same object (singleton)
assert kss is kss1
# make a prediction
keyword = kss.predict("discoTrain.wav")                        # Disco
#keyword = kss.predict("TheRiversGoingWildCUT.mp3")             # Blues
#keyword = kss.predict("QuantumJazz.mp3")                       # Jazz
#keyword = kss.predict("QuantumJazzCUT.mp3")                    # Jazz
#keyword = kss.predict("AbsconseResilience.mp3")                # Metal
#keyword = kss.predict("Nature.wav")
#keyword = kss.predict("elvis-presley-jailhouse-rock-music-video.mp3")           # Rock
#keyword = kss.predict("bob-marley-no-woman-no-cry-official-video.mp3")              # Reggae
#keyword = kss.predict("alan-jackson-chattahoochee-official-music-videoCUT.mp3")    # Country
print(keyword)

问题是它给我返回了一个我从未在任何论坛上看到过的值错误，它是：

File "C:ProgramDataAnaconda3envsPMIlibsite-packagestensorflow_corepythonkerasutilsgeneric_utils.py", line 165, in class_and_config_for_serialized_keras_object
raise ValueError('Unknown ' + printable_module_name + ': ' + class_name)
ValueError: Unknown regularizer: L2

我该怎么解决这个问题？

Answer 1

我终于找到了出现此错误的原因。它来自我的路径变量。我只需要添加"；ffmpeg"；在我从互联网上下载文件夹后，添加到我的路径变量。以下是链接：https://ffmpeg.org/download.html我把文件夹直接复制到我的"；C"；光盘，并将路径添加到我的路径变量中。

祝你好运！