Keras Val_acc很好，但是对相同数据的预测很差 [英] Keras Val_acc is good but prediction for same data is poor

问题描述

我正在使用Keras进行CNN两类分类.训练时，我的val_acc高于95％.但是，当我为相同的验证数据预测结果时，acc小于60％，那有可能吗?这是我的代码:

I am using Keras for a CNN two class classification. While training my val_acc is above 95 percent. But when I predict result for the same validation data the acc is less than 60 percent, is that even possible? This is my Code:

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
from keras import backend as K
from keras.callbacks import TensorBoard
from keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(1337) # for reproducibility
%matplotlib inline

img_width, img_height = 230,170

train_data_dir = 'data/Train'
validation_data_dir = 'data/Validation'
nb_train_samples =  13044
nb_validation_samples = 200
epochs =14
batch_size = 32

if K.image_data_format() == 'channels_first':
    input_shape = (1, img_width, img_height)
else:
    input_shape = (img_width, img_height, 1)

model = Sequential()

model.add(Convolution2D(32, (3, 3),data_format='channels_first' , input_shape=(1,230,170))) 
convout1 = Activation('relu')
model.add(convout1)
convout2 = MaxPooling2D(pool_size=(2,2 ), strides= None , padding='valid', data_format='channels_first')
model.add(convout2)

model.add(Convolution2D(32, (3, 3),data_format='channels_first'))
convout3 = Activation('relu')
model.add(convout3)
model.add(MaxPooling2D(pool_size=(2, 2), data_format='channels_first'))

model.add(Convolution2D(64, (3, 3),data_format='channels_first'))
convout4 = Activation('relu')
model.add(convout4)
convout5 = MaxPooling2D(pool_size=(2, 2), data_format='channels_first')
model.add(convout5)

model.add(Flatten())
model.add(Dense(64))
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'])

train_datagen = ImageDataGenerator(rescale=1. / 255, 
                                   shear_range=0, 
                                   zoom_range=0.2, 
                                   horizontal_flip=False, 
                                   data_format='channels_first')

test_datagen = ImageDataGenerator(rescale=1. / 255, 
                                  data_format='channels_first')
train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary',
    color_mode= "grayscale",
    shuffle=True
)
validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary',
    color_mode= "grayscale",
    shuffle=True
)
model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size,
    shuffle=True
    )

第37/37集

407/407 [==============]-1775s 4s/step-损耗:0.12-acc:0.96-val_loss:0.02-val_acc:0.99

407/407[==============] - 1775s 4s/step - loss: 0.12 - acc: 0.96 - val_loss: 0.02 - val_acc: 0.99

#Prediction:
test_data_dir='data/test'
validgen = ImageDataGenerator(horizontal_flip=False, data_format='channels_first')
test_gen = validgen.flow_from_directory(
         test_data_dir,
         target_size=(img_width, img_height),
         batch_size=1,
         class_mode='binary',
         shuffle=False,
         color_mode= "grayscale")

preds = model.predict_generator(test_gen)

在下面的输出中，约有7张图像属于0类.我对0类验证数据的所有100张图像尝试了相同的图像，只有15张图像被预测为0类，其余图像被预测为1类.

In the below output about 7 images belong to class 0. I tried the same for all 100 images of the class 0 validation data and only 15 images were predicted as class 0 and remaining was predicted as class 1

Found 10 images belonging to 1 classes.
[[ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 1.]
 [ 0.]
 [ 0.]
 [ 1.]]

推荐答案

您没有像训练和验证图像中那样按1./255缩放测试图像.理想情况下，测试数据的统计信息应与培训数据相似.

You are not scaling your test images by 1./255 as you have in your training and validation images. Ideally, the statistics of your test data should be similar to the training data.

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