问题描述
我正在尝试将此tflearn DCNN示例（使用图像预处理和augmemtation）转换为keras：
I'm trying to convert this tflearn DCNN sample (using image preprocessing and augmemtation) to keras:
Tflearn示例：
Tflearn sample:
import tflearn
from tflearn.data_utils import shuffle, to_categorical
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.estimator import regression
from tflearn.data_preprocessing import ImagePreprocessing
from tflearn.data_augmentation import ImageAugmentation
# Data loading and preprocessing
from tflearn.datasets import cifar10
(X, Y), (X_test, Y_test) = cifar10.load_data()
X, Y = shuffle(X, Y)
Y = to_categorical(Y, 10)
Y_test = to_categorical(Y_test, 10)
# Real-time data preprocessing
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
# Real-time data augmentation
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=25.)
# Convolutional network building
network = input_data(shape=[None, 32, 32, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 10, activation='softmax')
network = regression(network, optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001)
# Train using classifier
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(X, Y, n_epoch=50, shuffle=True, validation_set=(X_test, Y_test),
show_metric=True, batch_size=96, run_id='cifar10_cnn')
在经过50个时间段后得出以下结果：
This yielded the following results after 50 epochs:
Training Step: 26050 | total loss: 0.35260 | time: 144.306s
| Adam | epoch: 050 | loss: 0.35260 - acc: 0.8785 | val_loss: 0.64622 - val_acc: 0.8212 -- iter: 50000/50000
然后我尝试将其转换为Keras使用相同的DCNN图层，参数和图像预处理/增强：
I then tried to convert it to Keras using the same DCNN layers, parameters and image preprocessing/augmentation:
import numpy as np
from keras.datasets import cifar10
from keras.callbacks import TensorBoard
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, UpSampling2D, AtrousConvolution2D
from keras.layers.advanced_activations import LeakyReLU, PReLU
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator
from keras import backend as K
import matplotlib
from matplotlib import pyplot as plt
np.random.seed(1337)
batch_size = 96 # how many images to process at once
nb_classes = 10 # how many types of objects we can detect in this set
nb_epoch = 50 # how long we train the system
img_rows, img_cols = 32, 32 # image dimensions
nb_filters = 32 # number of convolutional filters to use
pool_size = (2, 2) # size of pooling area for max pooling
kernel_size = (3, 3) # convolution kernel size
(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 3)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 3)
input_shape = (img_rows, img_cols, 3)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(Y_train, nb_classes)
Y_test = np_utils.to_categorical(Y_test, nb_classes)
datagen = ImageDataGenerator(featurewise_center=True,
featurewise_std_normalization=True,
horizontal_flip=True,
rotation_range=25)
datagen.fit(X_train)
model = Sequential()
model.add(Conv2D(nb_filters, kernel_size, padding='valid', input_shape=input_shape, activation='relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Conv2D(nb_filters*2, kernel_size, activation='relu'))
model.add(Conv2D(nb_filters*2, kernel_size, activation='relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# Set up TensorBoard
tb = TensorBoard(log_dir='./logs')
history = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size), epochs=nb_epoch, shuffle=True, verbose=1, validation_data=(X_test, Y_test), callbacks=[tb])
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print("Accuracy: %.2f%%" % (score[1]*100))
plt.plot(history.epoch,history.history['val_acc'],'-o',label='validation')
plt.plot(history.epoch,history.history['acc'],'-o',label='training')
plt.legend(loc=0)
plt.xlabel('epochs')
plt.ylabel('accuracy')
plt.grid(True)
plt.show()
这会产生差得多的验证准确性结果：
This yielded far worse validation accuracy results:
Epoch 50/50
521/521 [==============================] - 84s 162ms/step - loss: 0.4723 - acc: 0.8340 - val_loss: 3.2970 - val_acc: 0.2729
Test score: 3.2969648239135743
Accuracy: 27.29%
有人可以帮助我理解为什么吗？我在Keras中是否应用了错误的图像/误解了图像预处理/增强功能？
Can anyone help me understand why? Have I misapplied/misunderstood image preprocessing/augmentation in Keras?
推荐答案
在Keras模型中，您忘记了对验证数据进行标准化也一样您可以通过对训练数据计算出的 datagen.mean 和 datagen.std 来做到这一点：
In your Keras model, you have forgotten to normalize the validation data as well. You can do this either by using datagen.mean and datagen.std computed over the training data:
# normalize test data; add a small constant to avoid division by zero,
# you can alternatively use `keras.backend.epsilon()`
X_test = (X_test - datagen.mean) / (datagen.std + 1e-8)
或者您可以使用 datagen.standardize（）标准化测试数据的方法：
or you can use the datagen.standardize() method to normalize test data:
X_test = datagen.standardize(X_test)
在SO上查看此问题以获取更多信息：
Look at this question on SO for more info: How does data normalization work in keras during prediction?
别忘了您应该通过对训练数据计算出的统计数据对测试数据进行标准化。 永远不会通过其自己的统计数据对测试数据进行归一化。
Don't forget that you should normalize test data by the statistics computed over the training data. NEVER EVER normalize test data by its own statistics.
注意：似乎是标准化 ...是的，您可以在。
Caveat: It seems that standardize alters its argument as well... yes, you can confirm this in the source code.
这篇关于卷积神经网络中的图像预处理在Keras与Tflearn中产生较低的准确性的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！