我有2个课程。 0 =狗，1 =非狗。 训练中的8800张图像（150,150像素）和4400张图像用于验证。 训练4400只狗，4400只狗。验证了2200只狗，2200只非狗。 Nondog图像包含船只，树木，钢琴等的随机图像。 我已经训练了我的网络，准确率高达87％+。 图： AccvsValAcc - http://imgur.com/a/6y6DG LossVSValLoss - http://imgur.com/a/QGZQx
我的网络：
#model dog/nondog
model = Sequential()
model.add(Convolution2D(16, 3, 3, input_shape=(3, img_width, img_height)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(16, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
我在模型末尾有一个节点，因为我理解我处理二进制分类问题，如果我只需要从非狗分类狗。
我面临的问题是当我model.predict看到一张看不见的狗图片时，它总是把它归类为非狗。我是否错误地解决了这个问题？如果我的准确度如此之高，有人可以向我解释为什么它永远不会将狗图片归类为狗？您可以向我的网络或方法推荐任何更改吗？
编辑： 最初我已经训练过70x70的图像。刚刚完成150x150图像的再培训。而不是model.predict我现在使用model.predict_classes。但它仍然是同样的问题。在我尝试的每张图片上，结果始终是非狗的结果。 ：（
EDIT2：完整代码：
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 26 16:21:36 2017
@author: PoLL
"""
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from PIL import Image
import numpy as np
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import PIL
from PIL import Image
#draw rect
import matplotlib.patches as patches
#########################################################################################################
#VALUES
# dimensions of images.
img_width, img_height = 150,150
train_data_dir = 'data1/train'
validation_data_dir = 'data1/validation'
nb_train_samples = 8800 #1000 cats/dogs
nb_validation_samples = 4400 #400cats/dogs
nb_epoch = 20
#########################################################################################################
#model dog/nondog
model = Sequential()
model.add(Convolution2D(16, 3, 3, input_shape=(3, img_width, img_height)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(16, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
#augmentation configuration for training
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
############################################################################################
#PRINT MODEL
from keras.utils.visualize_util import plot
plot(model, to_file='C:\Users\PoLL\Documents\Python Scripts\catdog\model.png')
##########################################################################################################
#TEST AUGMENTATION
img = load_img('data/train/cats/cat.0.jpg') # this is a PIL image
x = img_to_array(img) # this is a Numpy array with shape (3, 150, 150)
x = x.reshape((1,) + x.shape) # this is a Numpy array with shape (1, 3, 150, 150)
# the .flow() command below generates batches of randomly transformed images
# and saves the results to the `preview/` directory
i = 0
for batch in train_datagen.flow(x, batch_size=1,
save_to_dir='data/TEST AUGMENTATION', save_prefix='cat', save_format='jpeg'):
i += 1
if i > 20:
break # otherwise the generator would loop indefinitely
##########################################################################################################
# only rescaling
test_datagen = ImageDataGenerator(rescale=1./255)
#PREPARE TRAINING DATA
train_generator = train_datagen.flow_from_directory(
train_data_dir, #data/train
target_size=(img_width, img_height), #RESIZE to 150/150
batch_size=32,
class_mode='binary') #since we are using binarycrosentropy need binary labels
#PREPARE VALIDATION DATA
validation_generator = test_datagen.flow_from_directory(
validation_data_dir, #data/validation
target_size=(img_width, img_height), #RESIZE 150/150
batch_size=32,
class_mode='binary')
#START model.fit
history =model.fit_generator(
train_generator, #train data
samples_per_epoch=nb_train_samples,
nb_epoch=nb_epoch,
validation_data=validation_generator, #validation data
nb_val_samples=nb_validation_samples)
############################################################################################
#LOAD WEIGHTS
model.load_weights('savedweights2.h5')
############################################################################################
#check labels 0=cat 1=dog
#dog = 0, nondog =1
labels = (train_generator.class_indices)
print(labels)
############################################################################################
#TESTING
#load test DOG
img=load_img('data/prediction/catordog/dog.1234.jpg')
#reshape to 1,3,150,150
img = np.array(img).reshape((1,3,img_width, img_height))
plt.imshow(img.reshape((150, 150, 3)))
print(model.predict_classes(img))
#load test CAT
img2=load_img('data/prediction/catordog/cat.187.jpg')
#reshape to 1,3,150,150
img2 = np.array(img2).reshape((1,3,img_width, img_height))
plt.imshow(img2.reshape((150, 150, 3)))
print(model.predict_classes(img))
print(model.predict_classes(img2))
############################################################################################
#RESIZE IMAGES
baseheight = 70
basewidth = 70
img = Image.open('data/prediction/catordog/dog.1297.jpg')
wpercent = (basewidth / float(img.size[0]))
hsize = int((float(img.size[1]) * float(wpercent)))
img = img.resize((basewidth, hsize), PIL.Image.ANTIALIAS)
img.save('resized_dog.jpg')
############################################################################################
#load test DOG
img=load_img('resized_dog.jpg')
#reshape to 1,3,150,150
img = np.array(img).reshape((1,3,img_width, img_height))
plt.imshow(img.reshape((70, 70, 3)))
print(model.predict(img))
#plt.imshow(image)
print(img.shape)
############################################################################################
##### WINDOW BOX TO GO THROUGH THIS IMAGE
image=load_img('finddog/findadog2.jpg')
image= np.array(image).reshape((600,1050,3))
plt.imshow(image)
print(image.shape)
############################################################################################
############################################################################################
#OBJECT IS HERE
#object x,y,w,h,
object0 = (140, 140, 150,150)
object1 = (340, 340, 150,150)
#object2 = (130,130,150,150)
objloc = []
objloc.append(object0)
objloc.append(object1)
#objloc.append(object2)
#SLIDING WINDOW
def find_a_dog(image, step=20, window_sizes=[70]):
boxCATDOG = 0
locations = []
for win_size in window_sizes:
#top =y, left =x
for Y in range(0, image.shape[0] - win_size + 1, step):
for X in range(0, image.shape[1] - win_size + 1, step):
# compute the (top, left, bottom, right) of the bounding box
box = (Y, X, Y + win_size, X + win_size)
# crop
cropped_img = image[box[0]:box[2], box[1]:box[3]]
#reshape cropped image by window
cropped_img = np.array(cropped_img).reshape((1,3,70,70))
#classify it
boxCATDOG = predict_function(cropped_img)
if boxCATDOG ==0:
# print('box classified as dog')
#save location of it
locations.append(box)
print("found dog")
return locations
############################################################################################
#FUNCTIONS #
def predict_function(x):
result = model.predict_classes(x)
if result==1:
return 1
else:
return 0
#SHOW CROPPED IMAGE
def show_image(im):
plt.imshow(im.reshape((150,150,3)))
#SHOW INPUT IMAGE
def show_ori_image(im):
plt.imshow(im.reshape((600,1050,3)))
def draw_obj_loc(image,objectloc):
fix,ax = plt.subplots(1)
ax.imshow(image)
for l in objloc:
rectG = patches.Rectangle((l[0],l[1]),l[2],l[3],linewidth=1,edgecolor='G',facecolor='none')
ax.add_patch(rectG)
print len(objectloc)
#draw box when classifies as dog
def draw_boxes(image, locations):
fix,ax = plt.subplots(1)
ax.imshow(image)
for l in locations:
print l
rectR = patches.Rectangle((l[1],l[0]),150,150,linewidth=1,edgecolor='R',facecolor='none')
ax.add_patch(rectR)
print len(locations)
def draw_both(image, locations,objectloc):
fix,ax = plt.subplots(1)
ax.imshow(image)
for l in objloc:
rectG = patches.Rectangle((l[0],l[1]),l[2],l[3],linewidth=1,edgecolor='G',facecolor='none')
ax.add_patch(rectG)
for l in locations:
print l
rectR = patches.Rectangle((l[1],l[0]),150,150,linewidth=1,edgecolor='R',facecolor='none')
ax.add_patch(rectR)
#check if overlaps
def check_overlapping(image,locations,objloc):
for ol in objloc:
objX = (ol[0])
objY = (ol[1])
objW = (ol[2])
objH = (ol[3])
for ok in locations:
X=(ok[0])
Y=(ok[1])
# for l in locations:
# if (objX+objW<X or X+150<objX or objY+objH<Y or Y+150<objY):
if (objX+objW<X or X+150<objX or objY+objH<Y or Y+150<objY):
# Intersection = Empty
#no overlapping, false positive
print('THERES NO OVERLAPPING :',objloc.index(ol))
#
else:
#Intersection = Not Empty
print('THERE IS OVERLAPPING WITH OBJECT: ',objloc.index(ol), 'WITH BOX NUMBER: ',locations.index(ok))
############################################################################################
#get locations from image
locations = find_a_dog(image)
#show where windowslide classifed as positive
draw_boxes(image,locations)
#show where objects actually are
draw_obj_loc(image,objloc)
#check for overlapping between slider classification and actual
check_overlapping(image,locations,objloc)
#drawboth boxes
draw_both(image, locations,objloc)
#GREEN RECT
# X,Y X+W,Y
######
# #
# #
######
# X,Y+H X+W,Y+H
#WINDOW
# Y1,X1 Y1+W,X1
######
# #
# #
######
# Y1,X+H Y1+W,X1+H
###REMOVED FUNCTIONS
##DRAW RECT RED
def draw_rect(im,Y,X):
fig,ax = plt.subplots(1)
ax.imshow(im)
rect = patches.Rectangle((Y,X),150,150,linewidth=1,edgecolor='r',facecolor='none')
ax.add_patch(rect)
# im =plt.savefig('rect.jpg')
######OBJECT LOCATION AND H W GREEN
def draw_box_object(im,X,Y,W,H):
fig,ax = plt.subplots(1)
ax.imshow(im)
rect = patches.Rectangle((X,Y),W,H,linewidth=1,edgecolor='G',facecolor='none')
ax.add_patch(rect)
# im = plt.savefig('boxfordog.jpg')
################################################################################################
#PLOT
#ACC VS VAL_ACC
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy ACC VS VAL_ACC')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
#LOSS VS VAL_LOSS
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss LOSS vs VAL_LOSS')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
################################################################################################
#SAVE WEIGHTS
model.save_weights('savedweights.h5')
#70x70
model.save_weights('savedweights2.h5')
#150x150
model.save_weights('savedweights3.h5')
我为混乱的代码道歉，经常发生很多变化..
答案 0 :(得分：0)
您在哪个数据集上测量准确度？我建议使用学习曲线和其他性能指标虱子精确度和召回来执行“机器学习诊断”，这将帮助您确定您是否正在遭受过度拟合并给出一些指导。
同时执行“错误分析”，举一些例子，你的模型出错了，看看是否有任何模式。