如何解决使用train_on_batch在每个时期获得相同的准确性和损失
我正在做一个二进制分类问题，我总共有440张图像。我正在使用“ train_on_batch” 函数对每个批次进行CNN训练。我知道train_on_batch用于大型数据集，但出于测试目的，我在小型数据集上使用它。我正在为5个时期训练我的模型（resnet50V2），批次大小为32，并且每个时期我都收到相同的批次精度和相同的批次损失。可能是什么原因？
数据集结构：
dataset
with_mask
220 images
without_mask
220 images
文件：train_on_batch.py
# importing libraries
import os
import cv2
from glob import glob
import tensorflow as tf
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
import numpy as np
from vgg16_keras import VGG16
import warnings
warnings.filterwarnings('ignore')
def load_data():
# initialize the data and labels
data = []
labels = []
images_list = glob("E:/ai/Mask Detection/dataset/*/*.PNG")
# loop over the input images
for imagePath in images_list:
image = cv2.imread(imagePath)
#image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
image = cv2.resize(image,(224,224))
image = img_to_array(image)
data.append(image)
label = imagePath.split(os.path.sep)[-2]
labels.append(label)
print("Data and Labels are ready to use")
data = np.array(data,dtype = "float") / 255.0
labels = np.array(labels)
return data,labels
def optimizer():
return Adam(lr = 0.001)
def create_cnn():
model = VGG16.build(224,224,3,1)
model.compile(loss = "binary_crossentropy",optimizer = optimizer(),metrics = ["accuracy"])
return model
def get_batch(batch_size,trainX,trainY):
size = len(trainX)
n_batch = size // batch_size
i = 0
while(i < n_batch):
batchY = trainY[(i * n_batch):(i * n_batch + batch_size)]
batchX = trainX[(i * n_batch):(i * n_batch + batch_size)]
batchX = batchX.reshape(batch_size,3)
i += 1
yield batchX,batchY
def training(epoch = 5,batch_size = 32):
data,labels = load_data()
# partition the data into training and testing with 80% data to training and 20% to testing
(trainX,testX,trainY,testY) = train_test_split(data,labels,test_size = 0.2)
# convert the labels from integers to vectors
lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.fit_transform(testY)
model = create_cnn()
n_epochs = epoch
for epoch in range(1,n_epochs+1):
print("=" * 100)
print("Epoch:{}/{}".format(epoch,n_epochs))
train_acc = []
for batchX,batchY in get_batch(batch_size,trainY):
loss,acc = model.train_on_batch(batchX,batchY)
print("batch accuracy: {},batch loss: {}".format(acc,loss))
train_acc.append(acc)
print("Train accuracy",np.mean(train_acc))
training(epoch = 5,batch_size=32)
文件：vgg16_keras
# importing libraries
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D,MaxPooling2D
from tensorflow.keras.layers import Activation,Dense,Flatten,Dropout
from tensorflow.keras import backend as K
class VGG16:
@staticmethod
def build(width,height,depth,classes):
# initialize the model along with input shape to be "channels last" and the
# channels dimensions itself
model = Sequential()
input_shape = (height,width,depth)
if K.image_data_format() == "channels_first":
input_shape = (depth,width)
# Block 1: CONV => RELU => CONV => RELU => POOL layer set
model.add(Conv2D(64,(3,3),input_shape=input_shape,padding='same'))
model.add(Activation("relu"))
model.add(Conv2D(64,padding='same'))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2,2),strides = (2,2)))
# Block 2: CONV => RELU => CONV => RELU => POOL layer set
model.add(Conv2D(128,padding='same'))
model.add(Activation("relu"))
model.add(Conv2D(128,2)))
# Block 3: CONV => RELU => CONV => RELU => CONV => RELU => POOL layer set
model.add(Conv2D(256,padding='same'))
model.add(Activation("relu"))
model.add(Conv2D(256,padding='same'))
model.add(Activation("relu"))
model.add(Conv2D(256,padding='same'))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2,2)))
# Block 4: CONV => RELU => CONV => RELU => CONV => RELU => POOL layer set
model.add(Conv2D(512,padding='same'))
model.add(Activation("relu"))
model.add(Conv2D(512,padding='same'))
model.add(Activation("relu"))
model.add(Conv2D(512,2)))
# Block 5: CONV => RELU => CONV => RELU => CONV => RELU => POOL layer set
model.add(Conv2D(512,2)))
# Block 6: first set of FC => RELU layers
model.add(Flatten())
model.add(Dense(4096))
model.add(Activation("relu"))
model.add(Dropout(0.5))
# Block 7: second set of FC => RELU layers
model.add(Dense(4096))
model.add(Activation("relu"))
model.add(Dropout(0.5))
# softmax classifier
model.add(Dense(classes))
model.add(Activation("softmax"))
return model
输出：
Epoch:1/5
batch accuracy: 0.5625,batch loss: 6.708292007446289
batch accuracy: 0.40625,batch loss: 9.104110717773438
batch accuracy: 0.40625,batch loss: 9.104110717773438
batch accuracy: 0.375,batch loss: 9.583274841308594
batch accuracy: 0.375,batch loss: 9.583274841308594
batch accuracy: 0.3125,batch loss: 10.54160213470459
batch accuracy: 0.34375,batch loss: 10.06243896484375
batch accuracy: 0.4375,batch loss: 8.624947547912598
batch accuracy: 0.53125,batch loss: 7.187456130981445
batch accuracy: 0.625,batch loss: 5.749964714050293
batch accuracy: 0.625,batch loss: 5.749964714050293
====================================================================================================
Epoch:2/5
batch accuracy: 0.5625,batch loss: 5.749964714050293
====================================================================================================
Epoch:3/5
batch accuracy: 0.5625,batch loss: 5.749964714050293
====================================================================================================
Epoch:4/5
batch accuracy: 0.5625,batch loss: 5.749964714050293
====================================================================================================
Epoch:5/5
batch accuracy: 0.5625,batch loss: 5.749964714050293
Train accuracy 0.625
如果我在每个时期后更改学习率，它仍然会给我相同的结果。 我正在使用TensorFlow版本1.14
我需要在自定义批次上使用train_on_batch训练分类模型。如果您可以参考一些相同的示例。
解决方法
希望现在还不算太晚。我认为当您将 acc 添加到向量时，train_acc 不在“for”内。
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