我正在使用 CNN 对两种类型的花粉进行分类:sugi 和 hinoki。当我使用在可见光下拍摄的图像作为数据时，它预测所有测试图像都是“sugi”。另一方面，当我使用紫外线拍摄的图像作为数据时，它为测试集中的所有图片预测了“hinoki”。我多次更改纪元数、过滤器大小、批量大小、 channel 数，但结果是一样的。我该怎么办？
这是我的代码:
训练计划:
import os
from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, Model
from keras.layers import Input, Activation, Dropout, Flatten, Dense, Conv2D, MaxPool2D
#from keras.callbacks import EarlyStoppingByLossVal
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
import numpy as np
import time
from PIL import Image
import csv
import shutil
#import numpy.distutils.system_info as sysinfo
import scipy
import scipy.misc
import matplotlib.pyplot as plt
import pandas as pd
# kaneko
from keras.callbacks import TensorBoard
#sysinfo.get_info('lapack')
# 分類するクラス
classes = ['sugi', 'hinoki']
nb_classes = len(classes)
img_width, img_height = 100, 100
# トレーニング用とバリデーション用の画像格納先
train_data_dir = 'cut.kashi/train'
validation_data_dir = 'cut.kashi/validation'
# 今回はトレーニング用に200枚、バリデーション用に50枚の画像を用意した。
nb_train_samples = 1362
nb_validation_samples = 337
#nb_train_samples = 2171
#nb_validation_samples = 528
#batch_size = 64
nb_epoch = 50
gen_tr_batches = 4
folder = './output'
result_dir = 'results'
if not os.path.exists(result_dir):
os.mkdir(result_dir)
train_imagelist = os.listdir(train_data_dir)
test_list = "./test.train"
font = cv2.FONT_HERSHEY_COMPLEX
def vgg_model_maker():
model = Sequential()
model.add(Conv2D(32,5,input_shape=(img_width, img_height,3)))
model.add(Activation('relu'))
#model.add(Conv2D(32,5))
#model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Conv2D(64,5))
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(200))
model.add(Activation('relu'))
#model.add(Dropout(1.0))
model.add(Dense(nb_classes, activation='softmax'))
return model
def image_generator():
""" ディレクトリ内の画像を読み込んでトレーニングデータとバリデーションデータの作成 """
train_datagen = ImageDataGenerator(
rescale=1.0 / 255,
zoom_range=0.2,
horizontal_flip=True,
rotation_range = 180)
validation_datagen = ImageDataGenerator(rescale=1.0 / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
color_mode='rgb',
classes=classes,
class_mode='categorical',
batch_size=batch_size,
shuffle=True)
validation_generator = validation_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
color_mode='rgb',
classes=classes,
class_mode='categorical',
batch_size=batch_size,
shuffle=True)
return (train_generator,validation_generator)
def global_contrast_normalization(filename, s, lmda, epsilon):
X = numpy.array(Image.open(filename))
# replacement for the loop
X_average = numpy.mean(X)
print('Mean: ', X_average)
X = X - X_average
# `su` is here the mean, instead of the sum
contrast = numpy.sqrt(lmda + numpy.mean(X**2))
X = s * X / max(contrast, epsilon)
# scipy can handle it
scipy.misc.imsave('result.jpg', X)
# Generator for the network's training generator.
# Actual generator for the network's training.
if __name__ == '__main__':
start = time.time()
for the_file in os.listdir(folder):
file_path = os.path.join(folder, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
#elif os.path.isdir(file_path): shutil.rmtree(file_path)
except Exception as e:
print(e)
# kaneko
tensorboard = TensorBoard(log_dir="./kaneko", histogram_freq=0, batch_size= batch_size,write_graph=True)
# モデル作成
vgg_model = vgg_model_maker()
# 最後のconv層の直前までの層をfreeze
#for layer in vgg_model.layers[:15]:
#layer.trainable = False
# 多クラス分類を指定
vgg_model.compile(loss='categorical_crossentropy',
optimizer=optimizers.SGD(lr=1e-3, momentum=0.9),
metrics=['accuracy'])
# 画像のジェネレータ生成
train_generator,validation_generator = image_generator()
# Fine-tuning
history_callback = vgg_model.fit_generator(
train_generator,
samples_per_epoch=nb_train_samples,
nb_epoch=nb_epoch,
validation_data = validation_generator,
nb_val_samples=nb_validation_samples,
callbacks=[tensorboard])
loss_history = history_callback.history["loss"]
accuracy_history = history_callback.history["acc"]
val_loss_history = history_callback.history["val_loss"]
val_accuracy_history = history_callback.history["val_acc"]
numpy_loss_history = np.array(loss_history)
numpy_accuracy_history = np.array(accuracy_history)
numpy_val_loss_history = np.array(val_loss_history)
numpy_val_accuracy_history = np.array(val_accuracy_history)
f = open("results/result.csv","w")
writer = csv.writer(f)
writer.writerow(["loss","accuracy","validation loss","validation accuracy"])
for j in range(len(numpy_loss_history)):
writer.writerow([numpy_loss_history[j],numpy_accuracy_history[j],numpy_val_loss_history[j],numpy_val_accuracy_history[j]])
epochnum = range(len(numpy_loss_history))
print(len(epochnum))
#plt.plot(epochnum,numpy_loss_history, label = "loss")
#plt.legend()
plt.plot(loss_history)
plt.plot(val_loss_history)
plt.legend(['loss', 'val_loss'])
plt.show()
#plt.savefig("./Documents/Ghi1/shigaisen_loss.png")
plt.clf()
plt.plot(epochnum,numpy_accuracy_history, label = "accuracy")
plt.show()
#plt.savefig(".../Documents/Ghi1/shigaisen_accuracy.png")
plt.clf()
vgg_model.save_weights(os.path.join(result_dir, 'finetuning.h5'))
process_time = (time.time() - start) / 60
print(u'学習終了。かかった時間は', process_time, u'分です。')
测试程序:
import os, sys
import numpy as np
import cv2
from keras.applications.vgg16 import VGG16
from keras.models import Sequential, Model
from keras.layers import Input, Activation, Dropout, Flatten, Dense, Conv2D,MaxPool2D
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from datetime import datetime
classes = ['sugi', 'hinoki']
nb_classes = len(classes)
img_width, img_height = 100, 100
DataShape = (100,100,3)
result_dir = 'results'
#test_list = "./testfile"
test_list = "./test.train"
font = cv2.FONT_HERSHEY_COMPLEX
# このディレクトリにテストしたい画像を格納しておく
test_data_dir = 'cut/test'
folder = './output'
def model_load():
# VGG16, FC層は不要なので include_top=False
model = Sequential()
model.add(Conv2D(32,5,input_shape=(img_width, img_height,3)))
model.add(Activation('relu'))
#model.add(Conv2D(32,5))
#model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Conv2D(64,5))
model.add(Activation('relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(200))
model.add(Activation('relu'))
#model.add(Dropout(1.0))
model.add(Dense(nb_classes, activation='softmax'))
#adam = Adam(lr=1e-4)
# 学習済みの重みをロード
model.load_weights(os.path.join(result_dir, 'finetuning.h5'))
# 多クラス分類を指定
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.SGD(lr=1e-3, momentum=0.9),
metrics=['accuracy'])
return model
def image_generator():
""" ディレクトリ内の画像を読み込んでトレーニングデータとバリデーションデータの作成 """
test_datagen = ImageDataGenerator(
rescale=1.0 / 255,
zoom_range=0.2,
horizontal_flip=True,
rotation_range = 180)
#validation_datagen = ImageDataGenerator(rescale=1.0 / 255)
test_generator = test_datagen.flow_from_directory(
test_data_dir,
target_size=(img_width, img_height),
color_mode='rgb',
classes=classes,
class_mode='categorical',
batch_size=batch_size,
shuffle=True)
def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA):
# initialize the dimensions of the image to be resized and
# grab the image size
dim = None
(h, w) = image.shape[:2]
# if both the width and height are None, then return the
# original image
if width is None and height is None:
return image
# check to see if the width is None
if width is None:
# calculate the ratio of the height and construct the
# dimensions
r = height / float(h)
dim = (int(w * r), height)
# otherwise, the height is None
else:
# calculate the ratio of the width and construct the
# dimensions
r = width / float(w)
dim = (width, int(h * r))
# resize the image
resized = cv2.resize(image, dim, interpolation = inter)
# return the resized image
return resized
def test(model,path,filename,sugi):
test_imagelist = []
# テスト用画像取得
#test_imagelist = os.listdir(test_data_dir)
#test_imagelist = os.listdir(test_data_dir)
iml = cv2.imread(path,cv2.IMREAD_COLOR)
img = image_resize(iml,height=960)
img_array = np.array(img)
cimg = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cimg = cv2.medianBlur(cimg,5)
#_,cimg = cv2.threshold(cimg,0,255,cv2.THRESH_BINARY| cv2.THRESH_OTSU)
#cv2.imwrite(datetime.now().strftime('%s')+"binary.jpg",cimg)
#sys.exit()
circles = cv2.HoughCircles(cimg,cv2.HOUGH_GRADIENT,1,10,param1=15,param2=20,minRadius=10,maxRadius=25)
circles = np.uint16(np.around(circles))[0,:]
print (len(circles))
center = []
predict = []
for i in circles:
half = DataShape[0]//2
zoom_data = img_array[i[1]-half:i[1]+half,i[0]-half:i[0]+half,:]
if zoom_data.shape!=DataShape : continue
czoom = cv2.cvtColor(zoom_data, cv2.COLOR_BGR2GRAY)
czoomarr = np.array(zoom_data)
cen = czoom[half,half]
#edge = czoom[0,0]
if cen != 0:
#if cen < 255:
#if czoom[30,30] < 80:
test_imagelist.append(zoom_data)
center.append(i)
label_num = len(test_imagelist)
print(len(center))
print(label_num)
for im in test_imagelist:
x = image.img_to_array(im)
x = np.expand_dims(x, axis=0)
# 学習時に正規化してるので、ここでも正規化
x = x / 255
pred = model.predict(x)[0]
print(pred)
predict.append(pred)
TP = 0
TN = 0
FN = 0
FP = 0
for j in range(label_num):
if predict[j][0] > predict[j][1]:
if sugi == 1:
#TP+=1
TN+=1
else:
#FP+=1
FN+=1
#cv2.circle(img,(center[j][0],center[j][1]),center[j][2],(0,255,0),2)
cv2.putText(img,'S',(center[j][0],center[j][1]), font, 0.5,(0,255,0),1,cv2.LINE_AA)
if predict[j][0] < predict[j][1]:
#cv2.circle(img,(center[j][0],center[j][1]),center[j][2],(0,0,255),2)
if sugi == 1:
#FN+=1
FP+=1
else:
#TN+=1
TP+=1
cv2.putText(img,'H',(center[j][0],center[j][1]), font,0.5,(0,0,255),1,cv2.LINE_AA)
cv2.imwrite("output/"+"output"+filename,img)
return TP, FP, FN, TN
if __name__ == '__main__':
# モデルのロード
TP,FP,FN,TN = 0,0,0,0
print(TP,FP,FN,TN)
sugi = 0
c = "ス"
model = model_load()
for the_file in os.listdir(folder):
file_path = os.path.join(folder, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
#elif os.path.isdir(file_path): shutil.rmtree(file_path)
except Exception as e:
print(e)
for the_file in os.listdir(test_list):
#print(the_file)
if c in the_file:
sugi = 1
else:
sugi = 0
file_path = os.path.join(test_list, the_file)
tp1,fp1,fn1,tn1 = test(model,file_path,the_file,sugi)
TP += tp1
FP += fp1
FN += fn1
TN += tn1
precision = TP/(TP + FP)
recall = TP/(TP + FN)
F = (2*recall*precision)/(recall + precision)
#cv2.imwrite("output/" + "result.jpg",img)
print("TP = %lf, TN = %lf, FN = %lf, FP = %lf" %(TP,TN,FN,FP))
print("precision = %lf, recall = %lf" %(precision,recall))
print("F measure = %lf" %(F))
最佳答案
我能看到的一个问题是 x = x/255 在 test 方法中。您需要获取 float 值以进行正确的规范化。我遇到了同样的问题，适当的缩放使其正常工作。这是 link
希望对您有所帮助。
编辑:我的回答是考虑 python 2。
关于python - CNN 返回相同的分类结果(keras)，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/50968321/