Я пытаюсь преобразовать этот образец DCNN tflearn (используя предварительную обработку и аугметацию изображения) в керасы:
Tflearn образец:
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')
Это дало следующие результаты после 50 эпох:
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
Затем я попытался преобразовать его в Keras, используя те же слои DCNN, параметры и предварительную обработку / увеличение изображения:
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()
Это дало гораздо худшие результаты точности проверки:
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%
Может кто-нибудь помочь мне понять, почему? Я неправильно применил / неправильно понял предварительную обработку / увеличение изображения в Keras?