Keras -.flow_from_directory(目錄)

Question

我正在嘗試使用.flow_from_directory(directory)運行帶有 cifar10 數據集的 Resnet 示例。 下面的代碼如下：

from __future__ import print_function
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import np_utils
from keras.callbacks import ReduceLROnPlateau, CSVLogger, EarlyStopping

import numpy as np
import resnet
import os
import cv2
import csv
#import keras 

os.environ["CUDA_VISIBLE_DEVICES"] = "1"


# input image dimensions
img_rows, img_cols = 32, 32
# The CIFAR10 images are RGB.
img_channels = 3
nb_classes = 10


train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0,
        zoom_range=0,
        horizontal_flip=False,
        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.1)  # randomly shift images vertically (fraction of total height))

test_datagen = ImageDataGenerator(rescale=1./255)  

train_generator = train_datagen.flow_from_directory(
        '/home/datasets/cifar10/train',
        target_size=(32, 32),
        batch_size=32,
        shuffle=False)

validation_generator = test_datagen.flow_from_directory(
        '/home/datasets/cifar10/test',
        target_size=(32, 32),
        batch_size=32,
        shuffle=False)

model = resnet.ResnetBuilder.build_resnet_18((img_channels, img_rows, img_cols), nb_classes)
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

model.fit_generator(
        train_generator,
        steps_per_epoch=500,
        epochs=50,
        validation_data=validation_generator,
        validation_steps=250)

但是，我獲得了以下准確度值。

500/500 [==============================] - 22s - loss: 0.8139 - acc: 0.9254 - val_loss: 12.7198 - val_acc: 0.1250
Epoch 2/50
500/500 [==============================] - 19s - loss: 1.0645 - acc: 0.8856 - val_loss: 8.4179 - val_acc: 0.0560
Epoch 3/50
500/500 [==============================] - 19s - loss: 2.1014 - acc: 0.7492 - val_loss: 10.7770 - val_acc: 0.0956
Epoch 4/50
500/500 [==============================] - 19s - loss: 1.6806 - acc: 0.7772 - val_loss: 6.1023 - val_acc: 0.0741
Epoch 5/50
500/500 [==============================] - 19s - loss: 1.1798 - acc: 0.8669 - val_loss: 6.9016 - val_acc: 0.1253
Epoch 6/50
500/500 [==============================] - 19s - loss: 1.5448 - acc: 0.8369 - val_loss: 3.6371 - val_acc: 0.0370
Epoch 7/50
500/500 [==============================] - 19s - loss: 1.3763 - acc: 0.8599 - val_loss: 4.8012 - val_acc: 0.1204
Epoch 8/50
500/500 [==============================] - 19s - loss: 1.0186 - acc: 0.8891 - val_loss: 6.8395 - val_acc: 0.0912
Epoch 9/50
500/500 [==============================] - 19s - loss: 0.9477 - acc: 0.9081 - val_loss: 10.4287 - val_acc: 0.1253
Epoch 10/50
500/500 [==============================] - 19s - loss: 1.0689 - acc: 0.8686 - val_loss: 7.9931 - val_acc: 0.1253

我正在使用此 鏈接中的 Resnet。 我嘗試了許多示例來解決問題，包括官方文檔中的示例。 但是，我無法解決問題。 訓練准確度在變化，但是驗證准確度有些恆定。 有人可以指出問題嗎

Answer 1

根據 Keras 文檔。

flow_from_directory(directory) ，描述：獲取目錄的路徑，並生成成批的擴充/規范化數據。 在無限循環中無限期地產生批次。

使用shuffle = False ，它會無限期地采用同一批次。 導致這些精度值。 我更改了shuffle = True ，現在它工作正常。