def test(epoch, mode=1):
import matplotlib.pyplot as plt
from PIL import Image
from utils.helper_function import combine_images
if mode == 1:
num_classes = 10
_, (x_test, y_test) = load_cifar_10()
else:
num_classes = 100
_, (x_test, y_test) = load_cifar_100()
model = CapsNetv1(input_shape=[32, 32, 3], n_class=num_classes, n_route=3)
model.load_weights('weights/capsule_weights/capsule-cifar-' +
str(num_classes) + 'weights-{:02d}.h5'.format(epoch))
print("Weights loaded, start validation")
# model.load_weights('weights/capsule-weights-{:02d}.h5'.format(epoch))
y_pred, x_recon = model.predict([x_test, y_test], batch_size=100)
print('-' * 50)
# Test acc: 0.7307
print(
'Test acc:',
np.sum(np.argmax(y_pred, 1) == np.argmax(y_test, 1)) / y_test.shape[0])
img = combine_images(np.concatenate([x_test[:50], x_recon[:50]]))
image = img * 255
Image.fromarray(image.astype(np.uint8)).save("results/real_and_recon.png")
print('Reconstructed images are saved to ./results/real_and_recon.png')
print('-' * 50)
plt.imshow(plt.imread("results/real_and_recon.png", ))
plt.show()
def test(epoch, mode=1):
import matplotlib.pyplot as plt
from PIL import Image
from utils.helper_function import combine_images
if mode == 1:
num_classes =10
_,(x_test,y_test) = load_cifar_10()
else:
num_classes = 100
_,(x_test,y_test) = load_cifar_100()
model = CapsNet(input_shape=[32, 32, 3],
n_class=num_classes,
n_route=3)
model.load_weights('weights/capsule-cifar-'+str(num_classes)+'weights-{:02d}.h5'.format(epoch))
print("Weights loaded, start validation")
# model.load_weights('weights/capsule-weights-{:02d}.h5'.format(epoch))
y_pred, x_recon = model.predict([x_test, y_test], batch_size=100)
print('-'*50)
print('Test acc:', np.sum(np.argmax(y_pred, 1) == np.argmax(y_test, 1))/y_test.shape[0])
img = combine_images(np.concatenate([x_test[:50],x_recon[:50]]))
image = img*255
Image.fromarray(image.astype(np.uint8)).save("results/real_and_recon.png")
print()
print('Reconstructed images are saved to ./results/real_and_recon.png')
print('-'*50)
plt.imshow(plt.imread("results/real_and_recon.png", ))
plt.show()
def train(epochs=200, batch_size=64, mode=1):
import numpy as np
import os
from keras import callbacks
from keras.utils.vis_utils import plot_model
if mode == 1:
num_classes = 10
(x_train, y_train), (x_test, y_test) = load_cifar_10()
else:
num_classes = 100
(x_train, y_train), (x_test, y_test) = load_cifar_100()
model = CapsNetv1(input_shape=[32, 32, 3], n_class=num_classes, n_route=3)
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
model.summary()
log = callbacks.CSVLogger('results/capsule-cifar-' + str(num_classes) +
'-log.csv')
tb = callbacks.TensorBoard(log_dir='results/tensorboard-capsule-cifar-' +
str(num_classes) + '-logs',
batch_size=batch_size,
histogram_freq=True)
checkpoint = callbacks.ModelCheckpoint(
'weights/capsule-cifar-' + str(num_classes) + 'weights-{epoch:02d}.h5',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: 0.001 * np.exp(-epoch / 10.))
plot_model(model,
to_file='models/capsule-cifar-' + str(num_classes) + '.png',
show_shapes=True)
model.compile(optimizer=optimizers.Adam(lr=0.001),
loss=[margin_loss, 'mse'],
loss_weights=[1., 0.1],
metrics={
'output_recon': 'accuracy',
'output': 'accuracy'
})
from utils.helper_function import data_generator
generator = data_generator(x_train, y_train, batch_size)
# Image generator significantly increase the accuracy and reduce validation loss
model.fit_generator(generator,
steps_per_epoch=x_train.shape[0] // batch_size,
validation_data=([x_test, y_test], [y_test, x_test]),
epochs=epochs,
verbose=1,
max_q_size=100,
callbacks=[log, tb, checkpoint, lr_decay])
def test():
num_classes = 10
_, (x_test, y_test) = load_cifar_10()
model = ResnetBuilder.build_resnet_18((32, 32, 3), 10)
epoch = 44
model.load_weights('weights/resnet_weights/resnet-cifar-' +
str(num_classes) + 'weights-{:02d}.h5'.format(epoch))
print("Weights loaded, start validation")
# model.load_weights('weights/capsule-weights-{:02d}.h5'.format(epoch))
y_pred = model.predict(x_test, batch_size=100)
print('-' * 50)
# Test acc: 0.8231
print(
'Test acc:',
np.sum(np.argmax(y_pred, 1) == np.argmax(y_test, 1)) / y_test.shape[0])
def train(epochs=200,batch_size=64,mode=1):
import numpy as np
import os
from keras import callbacks
from keras.utils.vis_utils import plot_model
if mode==1:
num_classes = 10
(x_train,y_train),(x_test,y_test) = load_cifar_10()
else:
num_classes = 100
(x_train,y_train),(x_test,y_test) = load_cifar_100()
model = CapsNetv1(input_shape=[32, 32, 3],
n_class=num_classes,
n_route=3)
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
model.summary()
log = callbacks.CSVLogger('results/capsule-cifar-'+str(num_classes)+'-log.csv')
tb = callbacks.TensorBoard(log_dir='results/tensorboard-capsule-cifar-'+str(num_classes)+'-logs',
batch_size=batch_size, histogram_freq=True)
checkpoint = callbacks.ModelCheckpoint('weights/capsule-cifar-'+str(num_classes)+'weights-{epoch:02d}.h5',
save_best_only=True, save_weights_only=True, verbose=1)
lr_decay = callbacks.LearningRateScheduler(schedule=lambda epoch: 0.001 * np.exp(-epoch / 10.))
plot_model(model, to_file='models/capsule-cifar-'+str(num_classes)+'.png', show_shapes=True)
model.compile(optimizer=optimizers.Adam(lr=0.001),
loss=[margin_loss, 'mse'],
loss_weights=[1., 0.1],
metrics={'output_recon':'accuracy','output':'accuracy'})
from utils.helper_function import data_generator
generator = data_generator(x_train,y_train,batch_size)
model.fit_generator(generator,
steps_per_epoch=x_train.shape[0] // batch_size,
validation_data=([x_test, y_test], [y_test, x_test]),
epochs=epochs, verbose=1, max_q_size=100,
callbacks=[log,tb,checkpoint,lr_decay])
def train(epochs=150, batch_size=32, mode=1):
import numpy as np
import os
from keras import callbacks
from keras.utils.vis_utils import plot_model
import keras
from keras.preprocessing.image import ImageDataGenerator
if mode == 1:
num_classes = 10
(x_train, y_train), (x_test, y_test) = load_cifar_10()
else:
num_classes = 100
(x_train, y_train), (x_test, y_test) = load_cifar_100()
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=
False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=
0, # randomly rotate images in the range (degrees, 0 to 180)
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)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
# Compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied).
datagen.fit(x_train)
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
'''
Total params: 21,311,754
Trainable params: 21,296,522
Non-trainable params: 15,232
'''
model = ResnetBuilder.build_resnet_18((32, 32, 3), 10)
model.summary()
# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
model.summary()
log = callbacks.CSVLogger('results/resnet-cifar-' + str(num_classes) +
'-log.csv')
tb = callbacks.TensorBoard(log_dir='results/tensorboard-resnet-cifar-' +
str(num_classes) + '-logs',
batch_size=batch_size,
histogram_freq=True)
checkpoint = callbacks.ModelCheckpoint(
'weights/resnet-cifar-' + str(num_classes) + 'weights-{epoch:02d}.h5',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_reducer = callbacks.ReduceLROnPlateau(factor=np.sqrt(0.1),
cooldown=0,
patience=5,
min_lr=0.5e-6)
plot_model(model,
to_file='models/resnet-cifar-' + str(num_classes) + '.png',
show_shapes=True)
model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
steps_per_epoch=x_train.shape[0] // batch_size,
validation_data=(x_test, y_test),
epochs=epochs,
verbose=1,
max_q_size=100,
callbacks=[log, tb, checkpoint, lr_reducer])