我必须做一个CNN来诊断第4阶段（二进制分类-或0（non4thStage-nonPdr）或1（4thStage-pdr））的糖尿病性视网膜病变。我正在使用vgg16和gaussianBlur进行更好的分类。我有1400张测试图像（每节课700张），这是我的train.py：
#import tensorflow as tf
import cv2
import os
import numpy as np
from keras.layers.core import Flatten, Dense, Dropout, Reshape
from keras.models import Model
from keras.layers import Input, ZeroPadding2D, Dropout
from keras import optimizers
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import EarlyStopping
from keras.applications.vgg16 import VGG16
TRAIN_DIR = 'train/'
TEST_DIR = 'test/'
v = 'v/'
BATCH_SIZE = 32
NUM_EPOCHS = 5
def ReadImages(Path):
LabelList = list()
ImageCV = list()
classes = ["nonPdr", "pdr"]
# Get all subdirectories
FolderList = [f for f in os.listdir(Path) if not f.startswith('.')]
# Loop over each directory
for File in FolderList:
for index, Image in enumerate(os.listdir(os.path.join(Path, File))):
# Convert the path into a file
ImageCV.append(cv2.resize(cv2.imread(os.path.join(Path, File) + os.path.sep + Image), (224,224)))
LabelList.append(classes.index(os.path.splitext(File)[0]))
ImageCV[index] = cv2.addWeighted (ImageCV[index],4,cv2.GaussianBlur(ImageCV[index] , (0,0) , 10) ,-4 ,128)
return ImageCV, LabelList
data, labels = ReadImages(TRAIN_DIR)
valid, vlabels = ReadImages(TEST_DIR)
vgg16_model = VGG16(weights="imagenet", include_top=True)
# (2) remove the top layer
base_model = Model(input=vgg16_model.input,
output=vgg16_model.get_layer("block5_pool").output)
# (3) attach a new top layer
base_out = base_model.output
base_out = Reshape((25088,))(base_out)
top_fc1 = Dense(64, activation="relu")(base_out)
top_fc1 = Dropout(0.50)(base_out)
# output layer: (None, 5)
top_preds = Dense(1, activation="sigmoid")(top_fc1)
# (4) freeze weights until the last but one convolution layer (block4_pool)
for layer in base_model.layers[0:14]:
layer.trainable = False
# (5) create new hybrid model
model = Model(input=base_model.input, output=top_preds)
# (6) compile and train the model
sgd = SGD(lr=0.000001, momentum=0.9)
model.compile(optimizer=sgd, loss="binary_crossentropy", metrics=["accuracy"])
data = np.asarray(data)
valid = np.asarray(valid)
data = data.astype('float32')
valid = valid.astype('float32')
data /= 255
valid /= 255
labels = np.array(labels)
datagen = ImageDataGenerator(
featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True)
# compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied)
datagen.fit(data)
mean = datagen.mean
std = datagen.std
print(mean, "mean")
print(std, "std")
es = EarlyStopping(monitor='val_loss', verbose=1)
# fits the model on batches with real-time data augmentation:
model.fit_generator(datagen.flow(data, np.array(labels), batch_size=32),
steps_per_epoch=len(data) / 32, epochs=50,
validation_data=(valid, np.array(vlabels)),
nb_val_samples=72, callbacks=[es])
model.save('model.h5')
这将返回以下内容：
Epoch 1/50
44/43 [==============================] - 475s 11s/step - loss: 0.9671 - acc: 0.4789 - val_loss: 0.6808 - val_acc: 0.6389
Epoch 2/50
44/43 [==============================] - 467s 11s/step - loss: 0.8427 - acc: 0.5007 - val_loss: 0.6364 - val_acc: 0.6389
Epoch 3/50
44/43 [==============================] - 468s 11s/step - loss: 0.7703 - acc: 0.5204 - val_loss: 0.6136 - val_acc: 0.6806
Epoch 4/50
44/43 [==============================] - 466s 11s/step - loss: 0.7324 - acc: 0.5512 - val_loss: 0.5941 - val_acc: 0.7500
Epoch 5/50
44/43 [==============================] - 466s 11s/step - loss: 0.7074 - acc: 0.5679 - val_loss: 0.5758 - val_acc: 0.7639
Epoch 6/50
44/43 [==============================] - 461s 10s/step - loss: 0.6640 - acc: 0.6146 - val_loss: 0.5584 - val_acc: 0.8194
Epoch 7/50
44/43 [==============================] - 455s 10s/step - loss: 0.6562 - acc: 0.6077 - val_loss: 0.5418 - val_acc: 0.8333
Epoch 8/50
44/43 [==============================] - 458s 10s/step - loss: 0.6076 - acc: 0.6700 - val_loss: 0.5263 - val_acc: 0.8889
Epoch 9/50
44/43 [==============================] - 456s 10s/step - loss: 0.5743 - acc: 0.7005 - val_loss: 0.5119 - val_acc: 0.9167
Epoch 10/50
44/43 [==============================] - 457s 10s/step - loss: 0.5649 - acc: 0.7041 - val_loss: 0.4981 - val_acc: 0.9306
Epoch 11/50
44/43 [==============================] - 452s 10s/step - loss: 0.5654 - acc: 0.7088 - val_loss: 0.4855 - val_acc: 0.9444
Epoch 12/50
44/43 [==============================] - 458s 10s/step - loss: 0.5046 - acc: 0.7616 - val_loss: 0.4740 - val_acc: 0.9444
Epoch 13/50
44/43 [==============================] - 465s 11s/step - loss: 0.5002 - acc: 0.7808 - val_loss: 0.4633 - val_acc: 0.9444
Epoch 14/50
44/43 [==============================] - 459s 10s/step - loss: 0.4694 - acc: 0.7924 - val_loss: 0.4514 - val_acc: 0.9583
Epoch 15/50
44/43 [==============================] - 463s 11s/step - loss: 0.4482 - acc: 0.8184 - val_loss: 0.4432 - val_acc: 0.9444
Epoch 16/50
44/43 [==============================] - 456s 10s/step - loss: 0.4326 - acc: 0.8343 - val_loss: 0.4330 - val_acc: 0.9583
Epoch 17/50
44/43 [==============================] - 454s 10s/step - loss: 0.4291 - acc: 0.8303 - val_loss: 0.4233 - val_acc: 0.9583
Epoch 18/50
44/43 [==============================] - 457s 10s/step - loss: 0.4060 - acc: 0.8376 - val_loss: 0.4145 - val_acc: 0.9583
Epoch 19/50
44/43 [==============================] - 457s 10s/step - loss: 0.3933 - acc: 0.8686 - val_loss: 0.4069 - val_acc: 0.9583
Epoch 20/50
44/43 [==============================] - 455s 10s/step - loss: 0.3786 - acc: 0.8684 - val_loss: 0.3985 - val_acc: 0.9583
Epoch 21/50
44/43 [==============================] - 456s 10s/step - loss: 0.3661 - acc: 0.8774 - val_loss: 0.3902 - val_acc: 0.9583
Epoch 22/50
44/43 [==============================] - 454s 10s/step - loss: 0.3493 - acc: 0.8956 - val_loss: 0.3833 - val_acc: 0.9583
Epoch 23/50
44/43 [==============================] - 456s 10s/step - loss: 0.3355 - acc: 0.9065 - val_loss: 0.3765 - val_acc: 0.9444
Epoch 24/50
44/43 [==============================] - 456s 10s/step - loss: 0.3332 - acc: 0.9053 - val_loss: 0.3680 - val_acc: 0.9583
Epoch 25/50
44/43 [==============================] - 457s 10s/step - loss: 0.3236 - acc: 0.9160 - val_loss: 0.3625 - val_acc: 0.9444
Epoch 26/50
44/43 [==============================] - 458s 10s/step - loss: 0.3097 - acc: 0.9181 - val_loss: 0.3559 - val_acc: 0.9583
Epoch 27/50
44/43 [==============================] - 469s 11s/step - loss: 0.2915 - acc: 0.9242 - val_loss: 0.3517 - val_acc: 0.9444
Epoch 28/50
44/43 [==============================] - 473s 11s/step - loss: 0.2832 - acc: 0.9368 - val_loss: 0.3454 - val_acc: 0.9583
Epoch 29/50
44/43 [==============================] - 468s 11s/step - loss: 0.2747 - acc: 0.9418 - val_loss: 0.3416 - val_acc: 0.9583
Epoch 30/50
44/43 [==============================] - 470s 11s/step - loss: 0.2627 - acc: 0.9508 - val_loss: 0.3350 - val_acc: 0.9722
Epoch 31/50
44/43 [==============================] - 469s 11s/step - loss: 0.2517 - acc: 0.9638 - val_loss: 0.3311 - val_acc: 0.9722
Epoch 32/50
44/43 [==============================] - 470s 11s/step - loss: 0.2517 - acc: 0.9484 - val_loss: 0.3266 - val_acc: 0.9722
Epoch 33/50
44/43 [==============================] - 490s 11s/step - loss: 0.2348 - acc: 0.9560 - val_loss: 0.3211 - val_acc: 0.9722
Epoch 34/50
44/43 [==============================] - 461s 10s/step - loss: 0.2427 - acc: 0.9517 - val_loss: 0.3158 - val_acc: 0.9722
Epoch 35/50
44/43 [==============================] - 467s 11s/step - loss: 0.2260 - acc: 0.9616 - val_loss: 0.3109 - val_acc: 0.9722
Epoch 36/50
44/43 [==============================] - 459s 10s/step - loss: 0.2243 - acc: 0.9706 - val_loss: 0.3064 - val_acc: 0.9722
Epoch 37/50
44/43 [==============================] - 456s 10s/step - loss: 0.2099 - acc: 0.9687 - val_loss: 0.3029 - val_acc: 0.9722
Epoch 38/50
44/43 [==============================] - 457s 10s/step - loss: 0.2094 - acc: 0.9733 - val_loss: 0.2994 - val_acc: 0.9722
Epoch 39/50
44/43 [==============================] - 465s 11s/step - loss: 0.2014 - acc: 0.9744 - val_loss: 0.2941 - val_acc: 0.9722
Epoch 40/50
44/43 [==============================] - 465s 11s/step - loss: 0.1924 - acc: 0.9709 - val_loss: 0.2915 - val_acc: 0.9722
Epoch 41/50
44/43 [==============================] - 457s 10s/step - loss: 0.1908 - acc: 0.9735 - val_loss: 0.2897 - val_acc: 0.9722
Epoch 42/50
44/43 [==============================] - 463s 11s/step - loss: 0.1864 - acc: 0.9709 - val_loss: 0.2861 - val_acc: 0.9722
Epoch 43/50
44/43 [==============================] - 464s 11s/step - loss: 0.1787 - acc: 0.9773 - val_loss: 0.2822 - val_acc: 0.9722
Epoch 44/50
44/43 [==============================] - 468s 11s/step - loss: 0.1820 - acc: 0.9744 - val_loss: 0.2794 - val_acc: 0.9722
Epoch 45/50
44/43 [==============================] - 469s 11s/step - loss: 0.1646 - acc: 0.9818 - val_loss: 0.2763 - val_acc: 0.9722
Epoch 46/50
44/43 [==============================] - 469s 11s/step - loss: 0.1689 - acc: 0.9820 - val_loss: 0.2730 - val_acc: 0.9722
Epoch 47/50
44/43 [==============================] - 471s 11s/step - loss: 0.1495 - acc: 0.9879 - val_loss: 0.2711 - val_acc: 0.9722
Epoch 48/50
44/43 [==============================] - 469s 11s/step - loss: 0.1578 - acc: 0.9858 - val_loss: 0.2676 - val_acc: 0.9722
Epoch 49/50
44/43 [==============================] - 462s 10s/step - loss: 0.1557 - acc: 0.9858 - val_loss: 0.2643 - val_acc: 0.9722
Epoch 50/50
44/43 [==============================] - 454s 10s/step - loss: 0.1501 - acc: 0.9794 - val_loss: 0.2612 - val_acc: 0.9722
这是我的预报。
from keras.models import load_model
import cv2
import os
import numpy as np
from keras.preprocessing import image
TEST_DIR = 'v/'
pdr = 0
nonPdr = 0
model = load_model('model.h5')
def normalize(x, mean, std):
x[..., 0] -= mean[0]
x[..., 1] -= mean[1]
x[..., 2] -= mean[2]
x[..., 0] /= std[0]
x[..., 1] /= std[1]
x[..., 2] /= std[2]
return x
for filename in os.listdir(r'v/'):
if filename.endswith(".jpg") or filename.endswith(".ppm") or filename.endswith(".jpeg") or filename.endswith(".png"):
ImageCV = cv2.resize(cv2.imread(os.path.join(TEST_DIR) + filename), (224,224))
ImageCV = cv2.addWeighted (ImageCV,4,cv2.GaussianBlur(ImageCV , (0,0) , 10) ,-4 ,128)
ImageCV = np.asarray(ImageCV)
ImageCV = ImageCV.astype('float32')
ImageCV /= 255
x = ImageCV
x = np.expand_dims(x, axis=0)
x = normalize(x, [0.23883381, 0.23883381, 0.23883381], [0.24483591, 0.24579705, 0.2510857])
prob = model.predict(x)
if prob <= 0.75: #.75 = 80% | .70=79% >>>> .70 = 82% | .75 = 79%
print("nonPDR >>>", filename)
nonPdr += 1
else:
print("PDR >>>", filename)
pdr += 1
print(prob)
print("Number of retinas with PDR: ",pdr)
print("Number of retinas without PDR: ",nonPdr)
问题是：火车返回约97％的准确度后，我所有的预测都是错误的...例如，这3张图像必须是PDR（class1）：
nonPDR >>> 16_left.jpeg
[[0.07062916]]
nonPDR >>> 16_right.jpeg
[[0.09434311]]
nonPDR >>> 217_left.jpeg
[[0.14126943]]
如果我要测试与火车基地中放置的相同的图像，则该模型也无法正确预测...
我已经尝试过在没有高斯模糊的情况下进行训练，但是准确性很差。
我做错了什么？拜托，谢谢您的帮助！
答案 0 :(得分：1)
可以尝试的几件事：我建议不要使用数据扩充，除非您对自己的训练过程有一定的信心，即使最初的表现并不理想。仔细检查一下，您可能希望在model.fit之后直接使用训练数据进行预测，只是为了验证最终的准确性是否与训练中的结果相同。您可能会在导致网络性能下降的测试数据处理中有一些细微的差异，因此这是一个很好的第一步，要使自己确信训练部分可以，然后您可以专注于测试部分。我希望这会有所帮助。