我希望我的模型使用自行生成的文本图片来检测角度（360个类别）。 为了获得更多信息进行训练，每个新的训练循环图片都会以新的随机旋转生成。 但是，该模型似乎不是在学习，因为它为每张图片预测相同的类。我尝试过更改批处理大小，优化器，学习率，更复杂的模型，但是没有任何解决方法。
在此示例中，我正在使用 500个训练样本，50个验证样本和10个测试样本。我已经尝试了多达2000个训练样本，但是出现了同样的问题。
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,224,3) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None,222,32) 896
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None,111,32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None,109,64) 18496
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None,54,64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None,52,128) 73856
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None,26,128) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None,24,128) 147584
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None,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: 9,208
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),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),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),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),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),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
这是我的模型。py：
import numpy as np
from keras import backend as K
from keras.layers.convolutional import Conv2D,MaxPooling2D
from keras.layers import Input,Dense,flatten
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing import image as keras_image
from keras.utils import Sequence
from keras.utils.np_utils import to_categorical
from PIL import Image
import math
from random import randint
import os
from numpy import argmax
from create_text_images import create_data
def get_dataset(directory,name):
"""
Resize the pictures in the directory and return as a numpy array.
"""
X_train = []
for i,img_name in enumerate(os.listdir(directory)):
img_path = os.path.join(directory,img_name)
with Image.open(img_path) as img:
img = img.resize((262,262))
x = keras_image.img_to_array(img)
X_train.append(x)
X_train = np.array(X_train)
return X_train
def rotate_pictures(X_images):
"""
Randomly rotate the picture,then crop it to size 224x224.
Return the image as x normalized /255
and the rotation (converted to 360 categories) as y.
"""
X_train,y_train = [],[]
for i,img in enumerate(X_images):
img = keras_image.array_to_img(img)
rotation = randint(0,359)
img = img.rotate(rotation,resample=Image.BICUBIC)
w,h = img.size
img = img.crop(((w//2 - 112),(h//2 - 112),(w//2 + 112),(h//2 + 112)))
x = keras_image.img_to_array(img)/255.0
X_train.append(x)
y_train.append(rotation)
y_train = to_categorical(y_train,num_classes=360)
X_train = np.array(X_train)
y_train = np.array(y_train)
return X_train,y_train
class data_generator(Sequence):
"""
On initiation,create x and y data with the rotated pictures and their rotation.
If the dataset is 'train',then rotate original pictures again after every epoch.
"""
def __init__(self,images,name,batch_size):
self.images = images
self.name = name
self.x,self.y = rotate_pictures(self.images)
self.batch_size = batch_size
self.indices = np.arange(self.x.shape[0])
self.on_epoch_end()
def __len__(self):
return math.ceil(self.x.shape[0] / self.batch_size)
def __getitem__(self,idx):
inds = self.indices[idx * self.batch_size:(idx + 1) * self.batch_size]
batch_x = self.x[inds]
batch_y = self.y[inds]
return batch_x,batch_y
def on_epoch_end(self):
if self.name == "train":
self.x,self.y = rotate_pictures(self.images)
def mse_angle(y_true,y_pred):
"""
Calculate the mean difference between the true angles
and the predicted angles. Each angle is represented
as a binary vector.
"""
a = K.argmax(y_true)
b = K.argmax(y_pred)
diff = 180 - abs(abs(a - b) - 180)
return K.mean(K.cast(K.abs(diff),K.floatx()))
train_dir = "train/"
val_dir = "val/"
test_dir = "test/"
number_of_epochs = 50
number_of_classes = 360
input_shape = (224,3)
activation_fn = 'softmax'
batch_size = 32
create_data(train_dir,500)
X_train = get_dataset(train_dir,"train")
train_generator = data_generator(X_train,"train",batch_size)
create_data(val_dir,50)
X_val = get_dataset(val_dir,"val")
val_generator = data_generator(X_val,"val",batch_size)
create_data(test_dir,10)
X_test = get_dataset(test_dir,"test")
X_test,y_test = rotate_pictures(X_test)
input_tensor = Input(shape=input_shape)
x = Conv2D(32,(3,3),activation='relu')(input_tensor)
x = MaxPooling2D((2,2),strides=(2,2))(x)
x = Conv2D(64,activation='relu')(x)
x = MaxPooling2D((2,2))(x)
x = Conv2D(128,2))(x)
x = flatten()(x)
x = Dense(512,activation='relu')(x)
output_tensor = Dense(number_of_classes,activation=activation_fn)(x)
model = Model(input_tensor,output_tensor)
model.summary()
model.compile(
loss='categorical_crossentropy',optimizer=Adam(lr=0.1),metrics=[mse_angle]
)
history = model.fit(
train_generator,epochs=number_of_epochs,validation_data=val_generator
)
model.save_weights('model_weights.h5')
predictions = model.predict(X_test)
for i,prediction in enumerate(predictions):
angle = argmax(y_test[i])
pred = argmax(prediction)
print("for image {0} angle: {1},pred: {2}".format(i,angle,pred))
要运行代码，需要将其放置在具有三个空文件夹（val，train，test）和create_test_images.py的目录中：
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,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,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,3,6,30,70,1,10,100,3)
非常感谢您提供任何提示！
编辑：删除了两行未使用的代码