为什么我的val_accuracy停滞在0.0000e + 00，而我的val_loss从一开始就增加了？

Question

我正在训练用于对细胞进行分类的分类模型，而我的模型基于本文：https://www.nature.com/articles/s41598-019-50010-9。由于我的数据集仅包含10张图像，因此我进行了图像增强，以人为地将数据集的大小增加到3000张图像，然后将其分为2400张训练图像和600张验证图像。

但是，尽管训练损失和准确性在更多迭代中有所改善，但验证损失迅速增加，而验证准确性却停滞在0.0000e + 00。

我的模型从一开始就严重过度拟合吗？

我使用的代码如下所示：

import keras
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.keras.models import Model, load_model, Sequential, model_from_json, load_model
from tensorflow.keras.layers import Input, BatchNormalization, Activation, Flatten, Dense, LeakyReLU
from tensorflow.python.keras.layers.core import Lambda, Dropout
from tensorflow.python.keras.layers.convolutional import Conv2D, Conv2DTranspose, UpSampling2D
from tensorflow.python.keras.layers.pooling import MaxPooling2D, AveragePooling2D
from tensorflow.python.keras.layers.merge import Concatenate, Add
from tensorflow.keras import backend as K
from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, ReduceLROnPlateau, EarlyStopping
from tensorflow.keras.optimizers import *

img_channel = 1
input_size = (512, 512, 1)
inputs = Input(shape = input_size)
initial_input = Lambda(lambda x: x) (inputs) #Ensure input value is between 0 and 1 to avoid negative loss

kernel_size = (3,3)
pad = 'same'
model = Sequential()
filters = 2

c1 = Conv2D(filters, kernel_size, padding = pad, kernel_initializer = 'he_normal')(initial_input)
b1 = BatchNormalization()(c1) 
a1 = Activation('elu')(b1)
p1 = AveragePooling2D()(a1)

c2 = Conv2D(filters, kernel_size, padding = pad, kernel_initializer = 'he_normal')(p1)
b2 = BatchNormalization()(c2) 
a2 = Activation('elu')(b2)
p2 = AveragePooling2D()(a2)

c3 = Conv2D(filters, kernel_size, padding = pad, kernel_initializer = 'he_normal')(p2)
b3 = BatchNormalization()(c3) 
a3 = Activation('elu')(b3)
p3 = AveragePooling2D()(a3)

c4 = Conv2D(filters, kernel_size, padding = pad, kernel_initializer = 'he_normal')(p3)
b4 = BatchNormalization()(c4) 
a4 = Activation('elu')(b4)
p4 = AveragePooling2D()(a4)

c5 = Conv2D(filters, kernel_size, padding = pad, kernel_initializer = 'he_normal')(p4)
b5 = BatchNormalization()(c5) 
a5 = Activation('elu')(b5)
p5 = AveragePooling2D()(a5)

f = Flatten()(p5)

d1 = Dense(128, activation = 'elu')(f)
d2 = Dense(no_of_img, activation = 'softmax')(d1)

model = Model(inputs = [inputs], outputs = [d2])
print(model.summary())

learning_rate = 0.001
decay_rate = 0.0001
model.compile(optimizer = SGD(lr = learning_rate, decay = decay_rate, momentum = 0.9, nesterov = False), 
      loss = 'categorical_crossentropy', metrics = ['accuracy'])

perf_lr_scheduler = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.9, patience = 3,
        verbose = 1, min_delta = 0.01, min_lr = 0.000001)

model_earlystop = EarlyStopping(monitor = 'val_loss', min_delta = 0.001, patience = 10, restore_best_weights = True) 

#Convert labels to binary matrics
img_aug_label = to_categorical(img_aug_label, num_classes = no_of_img)

#Convert images to float to between 0 and 1
img_aug = np.float32(img_aug)/255

plt.imshow(img_aug[0,:,:,0])
plt.show()

#Train on augmented images
model.fit(
  img_aug, 
  img_aug_label, 
  batch_size = 4,
  epochs = 100, 
  validation_split = 0.2,
  shuffle = True,
  callbacks = [perf_lr_scheduler], 
  verbose = 2)

我的模型的输出如下所示：

Train on 2400 samples, validate on 600 samples
Epoch 1/100
2400/2400 - 12s - loss: 0.6474 - accuracy: 0.8071 - val_loss: 9.8161 - val_accuracy: 0.0000e+00
Epoch 2/100
2400/2400 - 10s - loss: 0.0306 - accuracy: 0.9921 - val_loss: 10.1733 - val_accuracy: 0.0000e+00
Epoch 3/100
2400/2400 - 10s - loss: 0.0058 - accuracy: 0.9996 - val_loss: 10.9820 - val_accuracy: 0.0000e+00
Epoch 4/100

Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.0009000000427477062.
2400/2400 - 10s - loss: 0.0019 - accuracy: 1.0000 - val_loss: 11.3029 - val_accuracy: 0.0000e+00
Epoch 5/100
2400/2400 - 10s - loss: 0.0042 - accuracy: 0.9992 - val_loss: 11.9037 - val_accuracy: 0.0000e+00
Epoch 6/100
2400/2400 - 10s - loss: 0.0024 - accuracy: 0.9996 - val_loss: 11.5218 - val_accuracy: 0.0000e+00
Epoch 7/100

Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.0008100000384729356.
2400/2400 - 10s - loss: 9.9053e-04 - accuracy: 1.0000 - val_loss: 11.7658 - val_accuracy: 0.0000e+00
Epoch 8/100
2400/2400 - 10s - loss: 0.0011 - accuracy: 1.0000 - val_loss: 12.0437 - val_accuracy: 0.0000e+00
Epoch 9/100

Answer 1

我意识到发生错误是因为在将其用作模型的训练数据之前，我没有手动进行数据整理。我认为validate_split和shuffle参数只会在训练期间发生，但实际上这是在训练之前发生的。换句话说，fit函数会先将您的数据分为训练和验证集，然后再对每个数据集中的数据进行混排（但不能跨组）。

对于我的扩充数据集，拆分发生在验证集包含在训练集中找不到的图像类型的位置。因此，该模型正在对训练集中未看到的数据类型进行验证，从而导致验证损失和准确性差。在将数据拟合到模型之前手动改组数据可以解决此问题。