我已经在 python 中培训了一个 CNN 神经网络，其中包含800个样本并在60上进行了测试。输出精度为50，现在每次使用模型时都是如此。 >预测它给了我相同的结果。
#main file - run this to train the network
import numpy as np
from keras.datasets import cifar10
from datasetFetch import DataFetch
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.constraints import maxnorm
from keras.optimizers import SGD
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
K.set_image_dim_ordering('th')
import simplejson
from matplotlib import pyplot
from scipy.misc import toimage
# load data
#(X_train, y_train), (X_test, y_test) = cifar10.load_data()
# create a grid of 3x3 images
#for i in range(0, 9):
# pyplot.subplot(3,3,1 + i)
# pyplot.imshow(toimage(X_train[i]))
# show the plot
#pyplot.show()
#init data
CONST_PHOTOS = 400 # number of photos of each type
y_train = []
#train data
data = DataFetch('orange',CONST_PHOTOS)
data1 = data.openPictures()
data = DataFetch('apple', CONST_PHOTOS)
data.removeErrorImages()
data2 = data.openPictures()
#test data
tdata = DataFetch('test-orange',30)
tdata1 = tdata.openPictures()
tdata = DataFetch('test-apple',30)
tdata2 = tdata.openPictures()
#add togheter data
X_train = data.connectData(data1,data2,'train')
y_train = data.getYtrain('train')
X_test = tdata.connectData(tdata1,tdata2,'test')
y_test = tdata.getYtrain('test')
# fix random seed for reproducibility
seed = 7
np.random.seed(seed)
# normalize inputs from 0-255 to 0.0-1.0
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train = X_train / 255.0
X_test = X_test / 255.0
#one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_train.shape[1] #number of categories
# Create the model
model = Sequential()
model.add(Conv2D(224, (11, 11), input_shape=(224, 224, 3), activation='relu', padding='same'))
model.add(Dropout(0.2))
model.add(Conv2D(55, (5, 5), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_last"))
model.add(Conv2D(13, (3, 3), activation='relu', padding='same'))
model.add(Dropout(0.5))
model.add(Conv2D(13, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_last"))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
epochs = 100
lrate = 0.01
decay = lrate/epochs
sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
#print(model.summary())
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=epochs, batch_size=10)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))
#and then we save
# serialize model to JSON
model_json = model.to_json()
with open("Data/model.json", "w") as json_file:
json_file.write(simplejson.dumps(simplejson.loads(model_json), indent=4))
# serialize weights to HDF5
model.save_weights("Data/model.h5")
print("Saved model to disk")
我使用 keras 和 tensorflow 。图像 224x224 像素，每个像素分为2类。我不太了解神经网络，这是我第一次尝试做一个这么大的工作。我听说它可能过度拟合，或者我需要一个更重要的层，或者我的批量/时期/学习率是错误的。
Edit1：种子如何影响网络的培训？ 训练后，准确度正好是50％，并且通过使用单独的.py文件，该文件仅加载模型并使用预测函数，它返回我使用的任何图像的确切输出百分比。我尝试使用用于训练的图像和外部图像。 我添加了dataFetch代码。
#preparing the photos to be 224x224 and getting them from urls saved in txt files
from PIL import Image
import requests
from io import BytesIO
import numpy as np
import socket
import random
from scipy import misc
from PIL import ImageChops
import math, operator
from functools import reduce
import glob
import os
import signal
compare = Image.open('/home/mihai/PycharmProjects/neuralnet/compare.jpg')
compare1 = Image.open('/home/mihai/PycharmProjects/neuralnet/compare1.jpg')
compare2 = Image.open('/home/mihai/PycharmProjects/neuralnet/compare2.jpg')
compare3 = Image.open('/home/mihai/PycharmProjects/neuralnet/compare3.jpg')
compare4 = Image.open('/home/mihai/PycharmProjects/neuralnet/compare4.jpg')
def rmsdiff(im1, im2):
"Calculate the root-mean-square difference between two images"
h = ImageChops.difference(im1, im2).histogram()
# calculate rms
return math.sqrt(reduce(operator.add, map(lambda h, i: h*(i**2), h, range(256))) / (float(im1.size[0]) * im1.size[1]))
class DataFetch:
chosenFile = ''
maxNumber = 0
y_train = []
y_test = []
def __init__(self, choice, number):
print('Images with '+choice+'s are being prepared')
self.chosenFile = choice
self.maxNumber = number
def getPictures(self):
imgArr = np.zeros((self.maxNumber, 224, 224, 3), dtype='uint8')
count = 0
class timeoutError(Exception):
signal.alarm(0)
def handler(signum, frame):
raise timeoutError
with open(self.chosenFile, "r") as ins:
for line in ins:
if count < self.maxNumber:
signal.signal(signal.SIGALRM, handler)
signal.alarm(3)
try:
try:
r = requests.get(line)
try:
img = Image.open(BytesIO(r.content))
ok = 0
try:
if rmsdiff(compare, img) > 1.3 and rmsdiff(compare1, img) > 1.3 and rmsdiff(compare2, img) > 1.3 and rmsdiff(compare3, img) > 1.3 and rmsdiff(compare4, img) > 1.3:
ok = 1
else:
print('Image removed from website')
except ValueError:
ok = 1
if ok == 1:
img = img.resize((224, 224))
img = img.convert('RGB')
img.save('/home/mihai/PycharmProjects/neuralnet/images/'+self.chosenFile+'/'+str(count)+".jpg", 'JPEG')
imgArr[count, :, :, :] = img
count = count + 1
print(count)
except OSError:
print('Image not Available')
except socket.error:
print('URL not available')
except timeoutError:
print("URL not available")
return imgArr
def openPictures(self):
cdir = os.getcwd()
imgArr = np.zeros((self.maxNumber, 224, 224, 3), dtype='uint8')
count = 0
for filename in glob.glob(cdir+'/images/'+self.chosenFile+'/*.jpg'):
if count < self.maxNumber:
img = Image.open(filename)
imgArr[count, :, :, :] = img
count = count + 1
return imgArr
def removeErrorImages(self):
cdir = os.getcwd()
for filename in glob.glob(cdir+'/images/'+self.chosenFile+'/*.jpg'):
img = Image.open(filename)
try:
if rmsdiff(compare, img) < 1.3 or rmsdiff(compare1, img) < 1.3 or rmsdiff(compare2, img) < 1.3 or rmsdiff(compare3, img) < 1.3 or rmsdiff(compare4, img) < 1.3:
os.remove(cdir+'/images/'+self.chosenFile+'/'+filename+'.jpg')
except ValueError:
pass
def getYtrain(self,outParam):
if outParam == 'train':
self.y_train = np.reshape(self.y_train, (len(self.y_train), 1))
return self.y_train
else:
self.y_test = np.reshape(self.y_test, (len(self.y_test), 1))
return self.y_test
def connectData(self, data1, data2, outParam):
d1c = 0
d2c = 0
outList = []
X_train = np.zeros((2 * self.maxNumber, 224, 224, 3), dtype='uint8')
for i in range(2 * self.maxNumber):
if d1c < self.maxNumber and d2c <self.maxNumber:
if random.random() <= 0.5:
X_train[d1c + d2c, :, :, :] = data1[d1c, :, :, :]
d1c = d1c + 1
outList.append(0)
else:
X_train[d1c + d2c, :, :, :] = data2[d2c, :, :, :]
d2c = d2c + 1
outList.append(1)
else:
if d1c < self.maxNumber:
X_train[d1c + d2c, :, :, :] = data1[d1c, :, :, :]
d1c = d1c + 1
outList.append(0)
else:
if d2c < self.maxNumber:
X_train[d1c + d2c, :, :, :] = data2[d2c, :, :, :]
d2c = d2c + 1
outList.append(1)
if outParam == 'train':
self.y_train = outList
else:
if outParam == 'test':
self.y_test = outList
return X_train
预测代码：
#run this to test a sample
from keras.utils import np_utils
from keras.models import model_from_json
from keras.optimizers import SGD
from datasetFetch import DataFetch
# load json and create model
json_file = open('Data/model2.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("Data/model2.h5")
print("Loaded model from disk")
epochs = 100
lrate = 0.01
decay = lrate/epochs
sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
loaded_model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
#prepare X_test
tdata = DataFetch('test-orange',int(3))
tdata1 = tdata.openPictures()
tdata = DataFetch('test-apple',int(3))
tdata2 = tdata.openPictures()
X_test = tdata.connectData(tdata1,tdata2,'test')
y_test = tdata.getYtrain('test')
X_test = X_test.astype('float32')
X_test = X_test / 255.0
y_test = np_utils.to_categorical(y_test)
print('Number of samples to be tested: '+str(X_test.shape[0]))
scores = loaded_model.evaluate(X_test, y_test, verbose=0)
print(scores[1]*100)
score = loaded_model.predict(X_test,batch_size=6, verbose=1)
print(score) #prints percentages
答案 0 :(得分：1)
图像准备是正确的。问题在于神经网络的结构加上使用的优化方法。
由于大量神经元仅用于分类2类，因此结构过于贴合，导致精确度保持在50％。
第二个问题是sgd优化器。我改为使用：
opt=keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy']
希望这对其他人也有帮助！