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我有2個分類。 0 =狗，1 =非狗。 8800圖像（150,150像素）在訓練和4400圖像驗證。 4400只狗，4400non訓練犬。 2200只狗，2200只非狗在驗證中。 Nondog圖像包含船隻，樹木，鋼琴等的隨機圖像。 我訓練了我的網絡，精度達到了87％+。 圖： AccvsValAcc - http://imgur.com/a/6y6DG LossVSValLoss - http://imgur.com/a/QGZQxKeras，Python。高精度模型始終將錯誤分類
我的網絡：
#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1個的結束點，因爲我明白林處理二進制分類問題，如果我需要的狗分類只有非狗。
我面對的問題是當我看到一個看不見的狗圖片系統時，它總是將它歸類爲非狗狗。我錯誤地處理了這個問題嗎？如果我的準確性如此之高，任何人都可以向我解釋爲什麼從來沒有把狗的照片歸類爲狗？有沒有可能推薦給我的網絡或方法的更改？
編輯： 本來我訓練了70x70圖像。剛剛完成150x150圖像的再培訓。此外，而不是model.predict Im現在使用model.predict_classes。但它仍然是同樣的問題。在我嘗試過的每個圖像上，結果總是非狗的結果。 :(
EDIT2：全碼：？
# -*- 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appologise爲亂碼，很多的變化經常發生..
訓練集中狗與非狗的比例是多少？ – cel
4400dogs，4400nondogs。 1：1 – Powisss
你做過任何預處理嗎？很容易忘記預處理新輸入的方式（完全）與預處理訓練輸入（和驗證）的方式相同。 – maz