我的 1D CNN 的验证准确度停留在 0.5，这是因为我总是从平衡的数据集中得到相同的预测。同时，我的训练准确度不断提高，损失按预期减少。
奇怪的是，如果我model.evaluate()在我的训练集上做（在最后一个 epoch 的准确率接近 1），准确率也将是 0.5。这里的准确率与上一个 epoch 的训练准确率有何不同？我还尝试使用 1 的批量大小进行训练和评估，但问题仍然存在。
好吧，我一直在寻找不同的解决方案，但仍然没有运气。我已经调查过的可能问题：
- 我的数据集得到了适当的平衡和洗牌；
- 我的标签是正确的；
- 尝试添加全连接层；
- 尝试从全连接层添加/删除 dropout；
- 尝试了相同的架构，但最后一层有 1 个神经元和 sigmoid 激活；
- 尝试改变学习率（下降到 0.0001 但仍然是同样的问题）。
这是我的代码：
import pathlib
import numpy as np
import ipynb.fs.defs.preprocessDataset as preprocessDataset
import pickle
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras import Input
from tensorflow.keras.layers import Conv1D, BatchNormalization, Activation, MaxPooling1D, Flatten, Dropout, Dense
from tensorflow.keras.optimizers import SGD
main_folder = pathlib.Path.cwd().parent
datasetsFolder=f'{main_folder}\\datasets'
trainDataset = preprocessDataset.loadDataset('DatasetTime_Sg12p5_Ov75_Train',datasetsFolder)
testDataset = preprocessDataset.loadDataset('DatasetTime_Sg12p5_Ov75_Test',datasetsFolder)
X_train,Y_train,Names_train=trainDataset[0],trainDataset[1],trainDataset[2]
X_test,Y_test,Names_test=testDataset[0],testDataset[1],testDataset[2]
model = Sequential()
model.add(Input(shape=X_train.shape[1:]))
model.add(Conv1D(16, 61, strides=1, padding="same"))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling1D(2, strides=2, padding="valid"))
model.add(Conv1D(32, 3, strides=1, padding="same"))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling1D(2, strides=2, padding="valid"))
model.add(Conv1D(64, 3, strides=1, padding="same"))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling1D(2, strides=2, padding="valid"))
model.add(Conv1D(64, 3, strides=1, padding="same"))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling1D(2, strides=2, padding="valid"))
model.add(Conv1D(64, 3, strides=1, padding="same"))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(200))
model.add(Activation('relu'))
model.add(Dense(2))
model.add(Activation('softmax'))
opt = SGD(learning_rate=0.01)
model.compile(loss='binary_crossentropy',optimizer=opt,metrics=['accuracy'])
model.summary()
model.fit(X_train,Y_train,epochs=10,shuffle=False,validation_data=(X_test, Y_test))
model.evaluate(X_train,Y_train)
这是model.fit（）：
model.fit(X_train,Y_train,epochs=10,shuffle=False,validation_data=(X_test, Y_test))
Epoch 1/10
914/914 [==============================] - 277s 300ms/step - loss: 0.6405 - accuracy: 0.6543 - val_loss: 7.9835 - val_accuracy: 0.5000
Epoch 2/10
914/914 [==============================] - 270s 295ms/step - loss: 0.3997 - accuracy: 0.8204 - val_loss: 19.8981 - val_accuracy: 0.5000
Epoch 3/10
914/914 [==============================] - 273s 298ms/step - loss: 0.2976 - accuracy: 0.8730 - val_loss: 1.9558 - val_accuracy: 0.5002
Epoch 4/10
914/914 [==============================] - 278s 304ms/step - loss: 0.2897 - accuracy: 0.8776 - val_loss: 20.2678 - val_accuracy: 0.5000
Epoch 5/10
914/914 [==============================] - 277s 303ms/step - loss: 0.2459 - accuracy: 0.8991 - val_loss: 5.4945 - val_accuracy: 0.5000
Epoch 6/10
914/914 [==============================] - 268s 294ms/step - loss: 0.2008 - accuracy: 0.9181 - val_loss: 32.4579 - val_accuracy: 0.5000
Epoch 7/10
914/914 [==============================] - 271s 297ms/step - loss: 0.1695 - accuracy: 0.9317 - val_loss: 14.9538 - val_accuracy: 0.5000
Epoch 8/10
914/914 [==============================] - 276s 302ms/step - loss: 0.1423 - accuracy: 0.9452 - val_loss: 1.4420 - val_accuracy: 0.4988
Epoch 9/10
914/914 [==============================] - 266s 291ms/step - loss: 0.1261 - accuracy: 0.9497 - val_loss: 4.3830 - val_accuracy: 0.5005
Epoch 10/10
914/914 [==============================] - 272s 297ms/step - loss: 0.1142 - accuracy: 0.9548 - val_loss: 1.6054 - val_accuracy: 0.5009
这是model.evaluate()：
model.evaluate(X_train,Y_train)
914/914 [==============================] - 35s 37ms/step - loss: 1.7588 - accuracy: 0.5009
这是model.summary()：
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv1d (Conv1D) (None, 4096, 16) 992
_________________________________________________________________
batch_normalization (BatchNo (None, 4096, 16) 64
_________________________________________________________________
activation (Activation) (None, 4096, 16) 0
_________________________________________________________________
max_pooling1d (MaxPooling1D) (None, 2048, 16) 0
_________________________________________________________________
conv1d_1 (Conv1D) (None, 2048, 32) 1568
_________________________________________________________________
batch_normalization_1 (Batch (None, 2048, 32) 128
_________________________________________________________________
activation_1 (Activation) (None, 2048, 32) 0
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 1024, 32) 0
_________________________________________________________________
conv1d_2 (Conv1D) (None, 1024, 64) 6208
_________________________________________________________________
batch_normalization_2 (Batch (None, 1024, 64) 256
_________________________________________________________________
activation_2 (Activation) (None, 1024, 64) 0
_________________________________________________________________
max_pooling1d_2 (MaxPooling1 (None, 512, 64) 0
_________________________________________________________________
conv1d_3 (Conv1D) (None, 512, 64) 12352
_________________________________________________________________
batch_normalization_3 (Batch (None, 512, 64) 256
_________________________________________________________________
activation_3 (Activation) (None, 512, 64) 0
_________________________________________________________________
max_pooling1d_3 (MaxPooling1 (None, 256, 64) 0
_________________________________________________________________
conv1d_4 (Conv1D) (None, 256, 64) 12352
_________________________________________________________________
batch_normalization_4 (Batch (None, 256, 64) 256
_________________________________________________________________
activation_4 (Activation) (None, 256, 64) 0
_________________________________________________________________
flatten (Flatten) (None, 16384) 0
_________________________________________________________________
dropout (Dropout) (None, 16384) 0
_________________________________________________________________
dense (Dense) (None, 200) 3277000
_________________________________________________________________
activation_5 (Activation) (None, 200) 0
_________________________________________________________________
dense_1 (Dense) (None, 2) 402
_________________________________________________________________
activation_6 (Activation) (None, 2) 0
=================================================================
Total params: 3,311,834
Trainable params: 3,311,354
Non-trainable params: 480
_________________________________________________________________
下面是 X_train 和 Y_train 的前 5 行：
[[ 3.602187e-04]
[ 8.075248e-04]
[ 4.319834e-04]
...
[ 3.011377e-05]
[-1.693150e-04]
[-8.542318e-05]] [0. 1.]
[[ 2.884359e-04]
[-6.340756e-05]
[-5.905452e-06]
...
[-9.305983e-05]
[ 1.345304e-04]
[-1.366256e-04]] [0. 1.]
[[ 7.720405e-04]
[ 6.031118e-05]
[ 6.691568e-04]
...
[-6.443140e-05]
[ 1.998355e-04]
[ 5.839724e-05]] [1. 0.]
[[-3.294961e-04]
[ 6.234528e-05]
[-2.861797e-04]
...
[-4.983012e-04]
[ 3.897884e-04]
[-1.014846e-05]] [0. 1.]
[[-0.0001479 ]
[ 0.00037975]
[-0.00024007]
...
[ 0.00018743]
[ 0.00044564]
[-0.00025613]] [0. 1.]