我正在尝试为28x28x5图像构建卷积自动编码器。以下是我的模型摘要:
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 28, 28, 16) 736
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 14, 16) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 14, 14, 8) 1160
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 7, 7, 8) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 7, 7, 8) 584
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 4, 4, 8) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 4, 4, 8) 584
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 8, 8, 8) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 8, 8, 8) 584
_________________________________________________________________
up_sampling2d_2 (UpSampling2 (None, 16, 16, 8) 0
_________________________________________________________________
conv2d_6 (Conv2D) (None, 14, 14, 16) 1168
_________________________________________________________________
up_sampling2d_3 (UpSampling2 (None, 28, 28, 16) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 28, 28, 5) 725
=================================================================
Total params: 5,541
Trainable params: 5,541
Non-trainable params: 0
在绘制每个时期的训练和测试错误时,我得到以下图表: 学习率从0.01开始并达到0.01 / 750 = 0,000013333
我虽然波动可能是由于学习率太高,所以我再次尝试使用0.00001到0,0000002,结果:
为什么在测试误差几乎恒定的情况下,训练损耗总是如此波动?这是正常行为吗?在我看来,它们的行为应该相似。在第一张图中两个损失的最初减少使我相信代码至少在做正确的事情,但是其余的感觉不对。
下面是我的训练代码:
(trainX, testX, trainY, testY) = train_test_split(newData,
newData, test_size=0.25)
# construct the image generator for data augmentation
aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1,
height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,
horizontal_flip=True, fill_mode="nearest")
model = AutoEncoder.build(width=28, height=28, depth=5)
# initialize our initial learning rate, # of epochs to train for,
# and batch size
INIT_LR = 0.001
EPOCHS = 50
BS = 16
opt = SGD(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="mean_squared_error", optimizer = opt)
H = model.fit_generator(aug.flow(trainX, trainY, batch_size=BS),
validation_data=(testX, testY), steps_per_epoch=len(trainX) // BS,
epochs=EPOCHS)
编辑:
训练集中的图像层中的示例行:
[ 7.41672516e-03 3.14044952e-03 -1.39656067e-02 -1.18265152e-02
6.34765625e-03 1.27620697e-02 1.49002075e-02 -6.48117065e-03
1.00231171e-03 -2.20489502e-03 3.14044952e-03 1.00231171e-03
1.00231171e-03 3.14044952e-03 1.00231171e-03 4.20951843e-03
-1.13582611e-03 1.06239319e-02 6.34765625e-03 4.20951843e-03
1.49002075e-02 4.20951843e-03 -6.67572021e-05 -6.67572021e-05
-1.13582611e-03 -3.27396393e-03 -5.41210175e-03 -6.48117065e-03]
测试集:
[ 1.3831139e-02 1.2762070e-02 7.4167252e-03 2.0713806e-03
-1.1826515e-02 -7.5502396e-03 1.0023117e-03 3.1404495e-03
2.6660919e-02 -6.6757202e-05 6.3476562e-03 2.0713806e-03
-7.5502396e-03 -1.1358261e-03 1.0023117e-03 1.0023117e-03
-1.1358261e-03 -6.4811707e-03 5.2785873e-03 -4.3430328e-03
1.0023117e-03 1.1693001e-02 2.3453712e-02 1.3831139e-02
1.9177437e-02 1.5969276e-02 2.0713806e-03 2.0713806e-03]
因为它们都来自同一个输入数据集,所以我认为这不是问题
添加更多的训练数据似乎可以解决该问题,尽管我仍然很奇怪为什么训练误差波动如此之大而验证误差却没有。
编辑3:
答案 0 :(得分:0)
我认为模型输出是一个常数。你能检查一下吗?您的验证集中有多少样本?如果模型输出恒定,则训练损失会波动(因为不同批次的样本不同),而验证损失将保持不变。也许尝试改变学习率/批量大小或添加更多数据(也到验证集)。