为什么我的 val_accuracy 停滞在 0.0000e+00 而我的 val_loss 从一开始就在增加？
Why is my val_accuracy stagnant at 0.0000e+00 while my val_loss is increasing from the start?
我正在训练一个分类模型来对细胞进行分类，我的模型是基于这篇论文：https://www.nature.com/articles/s41598-019-50010-9。由于我的数据集只有 10 张图像，我进行了图像增强，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。
然而，虽然训练损失和准确性在更多迭代后得到改善，但验证损失迅速增加，而验证准确性仍然停滞在 0.0000e+00。
我的模型是否从一开始就严重过度拟合？
我使用的代码如下图：
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)
我的模型输出如下图：
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
我意识到错误的发生是因为我在将数据用作模型的训练数据之前没有手动打乱数据。我以为 validation_split 和 shuffle 参数只会在训练期间发生，但实际上这发生在训练时间之前。换句话说，拟合函数会先将您的数据拆分为训练集和验证集，然后再对每组中的数据进行混洗（但不会交叉）。
对于我的扩充数据集，拆分发生在验证集包含训练集中未找到的图像类型的位置。因此，该模型正在对它在训练集中没有看到的数据类型执行验证，导致验证损失和准确性较差。在将数据拟合到模型之前手动改组数据解决了这个问题。
我正在训练一个分类模型来对细胞进行分类，我的模型是基于这篇论文：https://www.nature.com/articles/s41598-019-50010-9。由于我的数据集只有 10 张图像，我进行了图像增强，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。
然而，虽然训练损失和准确性在更多迭代后得到改善，但验证损失迅速增加，而验证准确性仍然停滞在 0.0000e+00。
我的模型是否从一开始就严重过度拟合？
我使用的代码如下图：
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)
我的模型输出如下图：
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
我意识到错误的发生是因为我在将数据用作模型的训练数据之前没有手动打乱数据。我以为 validation_split 和 shuffle 参数只会在训练期间发生，但实际上这发生在训练时间之前。换句话说，拟合函数会先将您的数据拆分为训练集和验证集，然后再对每组中的数据进行混洗（但不会交叉）。
对于我的扩充数据集，拆分发生在验证集包含训练集中未找到的图像类型的位置。因此，该模型正在对它在训练集中没有看到的数据类型执行验证，导致验证损失和准确性较差。在将数据拟合到模型之前手动改组数据解决了这个问题。