前馈神经网络未使用Keras函数发生器进行训练

Question

我正在使用前馈神经网络进行分类任务。我的数据是9个类的100万个示例（不平衡）。由于Keras中的内存限制，我使用function generator自动生成了批量大小为200的批量。我训练了一个简单的模型，该模型具有3个具有ReLU激活功能的隐藏层。输入层是39维MFCC，输出是9类。当我使用大量数据的子集时，此模型可以正常工作（确定！），但是现在，当使用函数生成器（即model.fit_generator）时，我看到训练精度只是在徘徊，验证精度太低了。看起来该模型根本没有学习。这种行为可能是什么原因？

关于数据

数据：语音

提到的大量数据子集是完全干净的，但是我使用均衡，时间拉伸，时间压缩，噪声，混响等数据增强技术从1300个示例中生成了100万个示例

import numpy as np
import os
import random
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.utils import np_utils
from keras.optimizers import SGD,Adam
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from matplotlib import pyplot
from sklearn.model_selection import train_test_split
from keras.models import load_model
from keras.models import model_from_json
from keras.callbacks import Callback
from keras.callbacks import ModelCheckpoint
from keras.callbacks import EarlyStopping
from sklearn.metrics import classification_report, confusion_matrix
from numpy.random import seed
seed(1)    

THEANO_FLAGS='device=cuda,floatX=float32'
epochs=200
exp_no=3
train_file_name='train_without_time_equalized_reverb_noise.txt'
lines=open(train_file_name).read().splitlines()
#train_generator(lines,labels)
labels={'user_1':0,'user_2':1,'user_3':2,'user_4':3,'user_5':4,'user_6':5,'user_7':6,'user_8':7,'user_9':8}
nb_classes=9
batch_size=200
test_file_name='val_without_time_equalized_reverb_noise.txt'
test_lines=open(test_file_name).read().splitlines()    

def get_input(path):
  feat=np.load(path)
  return(feat)    

def get_output(path,label_file=None):
   spkr_name=path.split('/')[-2]
   lab=labels[spkr_name]
   lab=np.int64(lab)
   #lab=keras.utils.to_categorical(lab,nb_classes)
   return(lab)    

def train_generator(files,labels,batch_size=200):
   while True:
      # select files for the batch
      batch_paths=np.random.choice(a=files,size=batch_size)
      batch_input=[]
      batch_output=[]
      b=0
      # read in each input and get labels
      for input_path in batch_paths:
        b+=1
        input_name=get_input(input_path)
        # MVN
        mean=np.mean(input_name,axis=0)
        std=np.std(input_name,axis=0)+0.00001
        input_name=(input_name-mean)/std
        output=get_output(input_path,labels)
        output=np.repeat(output,input_name.shape[0])
        output=keras.utils.to_categorical(output,nb_classes)
        #print(input_name.shape)
        #print(output.shape)
        #batch_input+=[input_name]
        batch_input.append(input_name)
        #batch_output+=[output]
        batch_output.append(output)
        # return a tuple of (input,output) to feed the network
        batch_x=np.array(batch_input)
        batch_y=np.array(batch_output)
        yield(batch_x[b-1],batch_y[b-1])    

def validation_generator(files,labels,batch_size=1):
  while True:
    batch_paths=np.random.choice(a=files,size=batch_size)
    batch_input=[]
    batch_output=[]
    c=0
    for input_path in batch_paths:
       c+=1
       input_name=get_input(input_path)
       # MVN
       mean=np.mean(input_name)
       std=np.std(input_name)+0.00001
       input_name=(input_name-mean)/std
       output=get_output(input_path,labels)
       output=np.repeat(output,input_name.shape[0])
       output=keras.utils.to_categorical(output,nb_classes)
       batch_input.append(input_name)
       batch_output.append(output)
       batch_x=np.array(batch_input)
       batch_y=np.array(batch_output)
       yield(batch_x[c-1],batch_y[c-1])    


# model    

model=Sequential()
model.add(Dense(300,activation='relu',input_dim=39))
model.add(Dropout(0.2))
model.add(Dense(200,activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(100,activation='relu'))
model.add(Dense(9,activation='softmax'))    

print(model.summary())
sgd=SGD(lr=0.001,decay=1e-6,momentum=0.9,nesterov=True) # Stochastic Gradient Descent, decay is the learning rate scheduler    

model.compile(loss='categorical_crossentropy',optimizer=sgd,metrics=['accuracy'])
#model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])    

model_dir='/media//results/'    

filepath=model_dir+'exp_'+str(exp_no)+'/weights.{epoch:02d}-{val_loss:.2f}-{val_acc:.2f}.hdf5' # Saving the model weights at each epoch that can be loaded later
checkpointer=ModelCheckpoint(filepath=filepath,monitor='val_loss',verbose=0,save_best_only=False,mode='auto')    

model.fit_generator(train_generator(lines,labels),steps_per_epoch=994931/batch_size,validation_data=validation_generator(test_lines,labels),validation_steps=4500/batch_size,epochs=epochs,verbose=1,shuffle=True,initial_epoch=0,callbacks=[checkpointer])

结果：