Python 为什么我的检测图像旋转的卷积模型预测每幅图片的类别相同？

我想让我的模型使用自己生成的文本图片来检测角度（360类）。 为了获得更多的训练信息，每个历元都会以新的随机旋转生成训练集图片。 然而，这个模型似乎没有学习，因为它预测的每一张图片都是同一个类。我尝试过改变批量大小、优化器、学习率、更复杂的模型，但没有任何东西能帮助解决问题

在这个例子中，我使用 500个培训样本、50个验证样本和10个测试样本。我尝试了多达2000个训练样本，但同样的问题也出现了

这是我的输出：

Using TensorFlow backend.
WARNING:tensorflow:From /home/lisa/.local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.

Model: "model_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 224, 224, 3)       0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 222, 222, 32)      896       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 111, 111, 32)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 109, 109, 64)      18496     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 54, 54, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 52, 52, 128)       73856     
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 26, 26, 128)       0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 24, 24, 128)       147584    
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 12, 12, 128)       0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 18432)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 512)               9437696   
_________________________________________________________________
dense_2 (Dense)              (None, 360)               184680    
=================================================================
Total params: 9,863,208
Trainable params: 9,863,208
Non-trainable params: 0
_________________________________________________________________
2019-11-06 11:08:47.885295: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-06 11:08:47.901431: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3408000000 Hz
2019-11-06 11:08:47.902091: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55f4487aac50 executing computations on platform Host. Devices:
2019-11-06 11:08:47.902139: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2019-11-06 11:08:47.903354: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2019-11-06 11:08:47.921001: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-06 11:08:47.921953: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: 
name: GeForce GTX 970 major: 5 minor: 2 memoryClockRate(GHz): 1.1775
pciBusID: 0000:01:00.0
2019-11-06 11:08:47.922112: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-11-06 11:08:47.922988: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-11-06 11:08:47.923739: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2019-11-06 11:08:47.923921: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2019-11-06 11:08:47.924921: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2019-11-06 11:08:47.925684: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2019-11-06 11:08:47.928111: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2019-11-06 11:08:47.928199: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-06 11:08:47.929103: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-06 11:08:47.929818: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-11-06 11:08:47.929844: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-11-06 11:08:47.976192: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-06 11:08:47.976213: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 
2019-11-06 11:08:47.976219: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N 
2019-11-06 11:08:47.976372: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-06 11:08:47.977217: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-06 11:08:47.978039: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-06 11:08:47.978851: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3466 MB memory) -> physical GPU (device: 0, name: GeForce GTX 970, pci bus id: 0000:01:00.0, compute capability: 5.2)
2019-11-06 11:08:47.980313: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55f449158000 executing computations on platform CUDA. Devices:
2019-11-06 11:08:47.980326: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): GeForce GTX 970, Compute Capability 5.2
WARNING:tensorflow:From /home/lisa/.local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

Epoch 1/50
2019-11-06 11:08:48.922378: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-11-06 11:08:49.080712: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
16/16 [==============================] - 3s 199ms/step - loss: 10271548.3852 - mse_angle: 88.4758 - val_loss: 6.0310 - val_mse_angle: 83.5972
Epoch 2/50
16/16 [==============================] - 1s 84ms/step - loss: 6.0294 - mse_angle: 87.3988 - val_loss: 6.2498 - val_mse_angle: 90.8889
Epoch 3/50
16/16 [==============================] - 1s 82ms/step - loss: 6.9000 - mse_angle: 90.9215 - val_loss: 6.2606 - val_mse_angle: 96.1042
Epoch 4/50
16/16 [==============================] - 1s 82ms/step - loss: 6.0261 - mse_angle: 90.2238 - val_loss: 6.1281 - val_mse_angle: 89.1111
Epoch 5/50
16/16 [==============================] - 1s 82ms/step - loss: 6.0339 - mse_angle: 90.6246 - val_loss: 6.1609 - val_mse_angle: 84.5764
Epoch 6/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9953 - mse_angle: 90.6105 - val_loss: 6.0373 - val_mse_angle: 97.3819
Epoch 7/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9419 - mse_angle: 90.0617 - val_loss: 6.0082 - val_mse_angle: 99.2257
Epoch 8/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9563 - mse_angle: 89.2258 - val_loss: 6.0243 - val_mse_angle: 99.2257
Epoch 9/50
16/16 [==============================] - 1s 83ms/step - loss: 5.9515 - mse_angle: 92.9902 - val_loss: 6.0726 - val_mse_angle: 87.7812
Epoch 10/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9554 - mse_angle: 89.0434 - val_loss: 6.0980 - val_mse_angle: 81.9757
Epoch 11/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9761 - mse_angle: 90.9699 - val_loss: 6.1573 - val_mse_angle: 99.1910
Epoch 12/50
16/16 [==============================] - 1s 83ms/step - loss: 5.9674 - mse_angle: 87.5254 - val_loss: 6.1502 - val_mse_angle: 91.5312
Epoch 13/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9457 - mse_angle: 90.9098 - val_loss: 6.1447 - val_mse_angle: 89.7708
Epoch 14/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9803 - mse_angle: 92.3281 - val_loss: 6.1520 - val_mse_angle: 97.5417
Epoch 15/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9663 - mse_angle: 91.3766 - val_loss: 6.1332 - val_mse_angle: 81.1562
Epoch 16/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9707 - mse_angle: 89.2891 - val_loss: 6.0442 - val_mse_angle: 88.7361
Epoch 17/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9691 - mse_angle: 87.9980 - val_loss: 5.8971 - val_mse_angle: 81.1562
Epoch 18/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9675 - mse_angle: 87.8605 - val_loss: 5.9070 - val_mse_angle: 81.1562
Epoch 19/50
16/16 [==============================] - 1s 81ms/step - loss: 5.9816 - mse_angle: 88.3820 - val_loss: 6.0384 - val_mse_angle: 90.0694
Epoch 20/50
16/16 [==============================] - 1s 82ms/step - loss: 6.0144 - mse_angle: 91.3855 - val_loss: 6.1066 - val_mse_angle: 90.0694
Epoch 21/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9556 - mse_angle: 92.5727 - val_loss: 6.2307 - val_mse_angle: 86.2465
Epoch 22/50
16/16 [==============================] - 1s 83ms/step - loss: 5.9522 - mse_angle: 90.1418 - val_loss: 6.1750 - val_mse_angle: 81.9062
Epoch 23/50
16/16 [==============================] - 1s 81ms/step - loss: 5.9603 - mse_angle: 88.3703 - val_loss: 6.0286 - val_mse_angle: 81.9062
Epoch 24/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9608 - mse_angle: 90.1531 - val_loss: 5.9816 - val_mse_angle: 97.9549
Epoch 25/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9764 - mse_angle: 88.8660 - val_loss: 6.0606 - val_mse_angle: 89.0174
Epoch 26/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9771 - mse_angle: 90.2336 - val_loss: 6.0759 - val_mse_angle: 83.8507
Epoch 27/50
16/16 [==============================] - 1s 82ms/step - loss: 6.0073 - mse_angle: 90.3863 - val_loss: 6.0298 - val_mse_angle: 83.8507
Epoch 28/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9664 - mse_angle: 89.0832 - val_loss: 5.9718 - val_mse_angle: 83.5972
Epoch 29/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9445 - mse_angle: 88.3340 - val_loss: 5.9844 - val_mse_angle: 82.4306
Epoch 30/50
16/16 [==============================] - 1s 81ms/step - loss: 5.9596 - mse_angle: 90.2934 - val_loss: 5.8805 - val_mse_angle: 83.0521
Epoch 31/50
16/16 [==============================] - 1s 83ms/step - loss: 5.9729 - mse_angle: 91.9238 - val_loss: 5.9500 - val_mse_angle: 84.4444
Epoch 32/50
16/16 [==============================] - 1s 83ms/step - loss: 5.9743 - mse_angle: 90.0250 - val_loss: 6.0221 - val_mse_angle: 97.5556
Epoch 33/50
16/16 [==============================] - 1s 81ms/step - loss: 5.9469 - mse_angle: 86.5922 - val_loss: 6.0201 - val_mse_angle: 87.6076
Epoch 34/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9822 - mse_angle: 93.8836 - val_loss: 5.9119 - val_mse_angle: 81.3472
Epoch 35/50
16/16 [==============================] - 1s 81ms/step - loss: 5.9751 - mse_angle: 88.9707 - val_loss: 5.9052 - val_mse_angle: 99.3993
Epoch 36/50
16/16 [==============================] - 1s 83ms/step - loss: 5.9564 - mse_angle: 89.6219 - val_loss: 5.9162 - val_mse_angle: 92.5278
Epoch 37/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9864 - mse_angle: 94.1816 - val_loss: 5.9559 - val_mse_angle: 90.5278
Epoch 38/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9566 - mse_angle: 88.3102 - val_loss: 6.0087 - val_mse_angle: 99.3993
Epoch 39/50
16/16 [==============================] - 1s 83ms/step - loss: 5.9639 - mse_angle: 91.0492 - val_loss: 5.9907 - val_mse_angle: 94.2361
Epoch 40/50
16/16 [==============================] - 1s 83ms/step - loss: 5.9792 - mse_angle: 88.0059 - val_loss: 5.8827 - val_mse_angle: 94.3056
Epoch 41/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9297 - mse_angle: 92.0566 - val_loss: 5.8013 - val_mse_angle: 94.6319
Epoch 42/50
16/16 [==============================] - 1s 84ms/step - loss: 5.9666 - mse_angle: 88.4168 - val_loss: 5.8768 - val_mse_angle: 99.4826
Epoch 43/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9887 - mse_angle: 90.3191 - val_loss: 5.9197 - val_mse_angle: 96.8611
Epoch 44/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9889 - mse_angle: 87.8867 - val_loss: 5.8738 - val_mse_angle: 96.6875
Epoch 45/50
16/16 [==============================] - 1s 83ms/step - loss: 5.9694 - mse_angle: 92.4437 - val_loss: 5.8639 - val_mse_angle: 98.7222
Epoch 46/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9560 - mse_angle: 89.9125 - val_loss: 5.8387 - val_mse_angle: 82.4965
Epoch 47/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9468 - mse_angle: 89.7066 - val_loss: 5.9525 - val_mse_angle: 87.1632
Epoch 48/50
16/16 [==============================] - 1s 83ms/step - loss: 6.0111 - mse_angle: 89.5977 - val_loss: 5.9091 - val_mse_angle: 96.6875
Epoch 49/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9648 - mse_angle: 89.0430 - val_loss: 5.9656 - val_mse_angle: 92.8368
Epoch 50/50
16/16 [==============================] - 1s 82ms/step - loss: 5.9234 - mse_angle: 91.1891 - val_loss: 5.9717 - val_mse_angle: 99.2257
for image 0 angle: 312, pred: 46
for image 1 angle: 202, pred: 46
for image 2 angle: 235, pred: 46
for image 3 angle: 286, pred: 46
for image 4 angle: 226, pred: 46
for image 5 angle: 76, pred: 46
for image 6 angle: 91, pred: 46
for image 7 angle: 91, pred: 46
for image 8 angle: 97, pred: 46
for image 9 angle: 263, pred: 46

要运行代码，需要将其放置在一个包含三个空文件夹（val、train、test）的目录中，并创建_test_images.py：

import random
import string
from PIL import Image, ImageDraw, ImageFont

def get_random_string(stringLength):
    characters = 10*string.ascii_letters + 100*' ' + string.punctuation*2 + string.digits
    return ''.join(random.choice(characters) for i in range(stringLength))

def get_random_text(lines_min, lines_max, char_min, char_max, newline_min, newline_max):
    lines = ''
    for line in range(random.randint(lines_min, lines_max+1)):
        lines += get_random_string(random.randint( char_min, char_max+1))
        lines += '\n' * random.randint(newline_min, newline_max+1)
    return lines

def create_random_image(directory, file_name, paragraphs_min, paragraphs_max, fontsize_min, fontsize_max,
                      lines_min, lines_max, char_min, char_max, newline_min, newline_max):
    img = Image.new('RGB', (876, 876), color = 'white')
    img.alpha_channel = False
    d = ImageDraw.Draw(img)
    for i in range(random.randint(paragraphs_min, paragraphs_max+1)):
        fnt = ImageFont.truetype('Roboto-Black.ttf', random.randint(fontsize_min, fontsize_max+1))
        d.text((50,100+random.uniform(300, 500)*i),
               get_random_text(lines_min, lines_max, char_min, char_max, newline_min, newline_max),
               fill='black', font=fnt) 
    img.save('{0}/{1}.png'.format(directory, file_name))

def create_data(directory, count):
    for i in range(0, count):
        create_random_image(directory, i, 3, 6, 30, 70, 1, 3, 10, 100, 1, 3)

如果有任何提示，我将不胜感激

---

编辑：删除了我评论中指出的两行未使用的代码，使用您提供的代码，我可以重现您的问题，并将其从260个类的分类问题重新表述为回归问题

因此，我将输出神经元的数量更改为只有一个具有sigmoid激活的神经元，将标签更改为连续数字，并将其标准化（除以360），使其数字介于0和1之间，将损失函数更改为MSE，并使用优化器的默认值

经过这些修改，经过10个阶段的训练，我得到了这个结果：

for image 0 angle: 0.7416666666666667, pred: [0.7266706]
for image 1 angle: 0.8111111111111111, pred: [0.8449749]
for image 2 angle: 0.7777777777777778, pred: [0.84269005]
for image 3 angle: 0.12222222222222222, pred: [0.14173588]
for image 4 angle: 0.7388888888888889, pred: [0.730219]
for image 5 angle: 0.9694444444444444, pred: [0.9117564]
for image 6 angle: 0.075, pred: [0.07597628]
for image 7 angle: 0.29444444444444445, pred: [0.1829494]
for image 8 angle: 0.10277777777777777, pred: [0.12209181]
for image 9 angle: 0.21388888888888888, pred: [0.31544465]

---

您是否尝试过将问题重新表述为回归问题，即预测角度本身为连续数，而不是分类问题？如果您有2000个样本和360个类，那么平均每个类有5-6个图像，这是不够的。如果你运气不好，你没有一些课程的例子。如果使用更少的类会发生什么？例如，仅预测角度是否在0°-90°-90°-180°之间，……样本数少不是很好，因为它通过旋转所有样本图片为每个历元生成一个新旋转的x数据集？不管怎样，谢谢你的提示，我会尝试一下并发布结果！我刚刚尝试了一个回归模型，但它预测了每一个类的1.0，以及一个有4个类的模型，它也预测了每一张图片的同一个类。我觉得我的数据可能有问题，但我不知道是什么。当我在rotate_图片中显示（）图像并打印旋转时，它们看起来很好。您是否将输出维度更改为只有一个神经元，并使用ReLU激活而不是softmax，以及是否使用MSE作为损失函数？对于这种情况，标签也必须是连续的数字。顺便说一句，试着坚持优化器的默认值，除非你有一个工作模型，否则不值得对它们进行优化。是的，对于回归，我使用了ReLU激活、MSE损失、1输出类，并在将其附加到y数组之前将旋转转换为float（）。你的回归建议最终对我有效，我只是没有使用正确的学习速率（它现在使用的是0.00001），并且忘记了在最后删除预测的argmax（）。非常感谢。我不能投票，因为我没有足够的分数

for image 0 angle: 0.7416666666666667, pred: [0.7266706]
for image 1 angle: 0.8111111111111111, pred: [0.8449749]
for image 2 angle: 0.7777777777777778, pred: [0.84269005]
for image 3 angle: 0.12222222222222222, pred: [0.14173588]
for image 4 angle: 0.7388888888888889, pred: [0.730219]
for image 5 angle: 0.9694444444444444, pred: [0.9117564]
for image 6 angle: 0.075, pred: [0.07597628]
for image 7 angle: 0.29444444444444445, pred: [0.1829494]
for image 8 angle: 0.10277777777777777, pred: [0.12209181]
for image 9 angle: 0.21388888888888888, pred: [0.31544465]