ModelCheckpoint 不保存 hdf5 文件

ModelCheckpoint not saving the hdf5 file

我在构建语音到文本模型时遇到了一个奇怪的问题。

模型已训练和创建,但是当我尝试将其保存为 hdf5 文件时,为了将其重新用于测试目的,操作根本不起作用,控制台中也没有弹出任何错误…

完整代码如下:

import os
import librosa   
import IPython.display as ipd
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import wavfile 
import warnings
from sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
from keras.layers import Dense, Dropout, Flatten, Conv1D, Input, MaxPooling1D
from keras.models import Model
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras import backend as K
from keras.models import load_model
import random

warnings.filterwarnings("ignore")

samples, sample_rate = librosa.load(r'C:\Users\makra\OneDrive\Desktop\Conda\Speech-to-text\train\audio\yes[=10=]a7c2a8d_nohash_0.wav', sr = 16000)

ipd.Audio(samples, rate=sample_rate)
samples = librosa.resample(samples, sample_rate, 8000)
ipd.Audio(samples, rate=8000)

labels=os.listdir(r'C:\Users\makra\OneDrive\Desktop\Conda\Speech-to-text\train\audio')

labels=["yes", "no", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "zero"]

all_wave = []
all_label = []
for label in labels:
    print(label)
    waves = [f for f in os.listdir(r'C:\Users\makra\OneDrive\Desktop\Conda\Speech-to-text\train\audio' + '/'+ label) if f.endswith('.wav')]
    for wav in waves:
        samples, sample_rate = librosa.load(r'C:\Users\makra\OneDrive\Desktop\Conda\Speech-to-text\train\audio' + '/' + label + '/' + wav, sr = 16000)
        samples = librosa.resample(samples, sample_rate, 8000)
        if(len(samples)== 8000) : 
            all_wave.append(samples)
            all_label.append(label)
            
le = LabelEncoder()
y=le.fit_transform(all_label)
classes= list(le.classes_)

y=np_utils.to_categorical(y, num_classes=len(labels))
all_wave = np.array(all_wave).reshape(-1,8000,1)

x_tr, x_val, y_tr, y_val = train_test_split(np.array(all_wave),np.array(y),stratify=y,test_size = 0.2,random_state=777,shuffle=True)

K.clear_session()

inputs = Input(shape=(8000,1))

#First Conv1D layer
conv = Conv1D(8,13, padding='valid', activation='relu', strides=1)(inputs)
conv = MaxPooling1D(3)(conv)
conv = Dropout(0.3)(conv)

#Second Conv1D layer
conv = Conv1D(16, 11, padding='valid', activation='relu', strides=1)(conv)
conv = MaxPooling1D(3)(conv)
conv = Dropout(0.3)(conv)

#Third Conv1D layer
conv = Conv1D(32, 9, padding='valid', activation='relu', strides=1)(conv)
conv = MaxPooling1D(3)(conv)
conv = Dropout(0.3)(conv)

#Fourth Conv1D layer
conv = Conv1D(64, 7, padding='valid', activation='relu', strides=1)(conv)
conv = MaxPooling1D(3)(conv)
conv = Dropout(0.3)(conv)

#Flatten layer
conv = Flatten()(conv)

#Dense Layer 1
conv = Dense(256, activation='relu')(conv)
conv = Dropout(0.3)(conv)

#Dense Layer 2
conv = Dense(128, activation='relu')(conv)
conv = Dropout(0.3)(conv)

outputs = Dense(len(labels), activation='softmax')(conv)

model = Model(inputs, outputs)
model.summary()

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

es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10, min_delta=0.0001) 
mc = ModelCheckpoint('best_model.hdf5', monitor='val_acc', verbose=1, save_best_only=True, mode='max')

history=model.fit(x_tr, y_tr ,epochs=100, callbacks=[es,mc], batch_size=32, validation_data=(x_val,y_val))

非常感谢您的帮助,

马克拉姆

我建议您在 ModelCheckpoint 回调中使用 save_weights_only=True 选项并使用 API model.load_weights(checkpoint_path)。这将加载最新的模型权重,如 here 所示。这只是一个临时解决方案,我认为您的模型精度没有提高,这就是您的 save_best_only 选项无法保存任何模型的原因在 ModelCheckpoint 回调中设置 accuracy 应该增加 的条件。