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capsulenet-multi-gpu.py
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capsulenet-multi-gpu.py
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"""
Keras implementation of CapsNet in Hinton's paper Dynamic Routing Between Capsules.
The current version maybe only works for TensorFlow backend. Actually it will be straightforward to re-write to TF code.
Adopting to other backends should be easy, but I have not tested this.
Usage:
python capsulenet-multi-gpu.py
python capsulenet-multi-gpu.py --gpus 2
... ...
Result:
About 55 seconds per epoch on two GTX1080Ti GPU cards
Author: Xifeng Guo, E-mail: `guoxifeng1990@163.com`, Github: `https://github.com/XifengGuo/CapsNet-Keras`
"""
from keras import optimizers
from keras import backend as K
K.set_image_data_format('channels_last')
from capsulenet import CapsNet, margin_loss, load_mnist, manipulate_latent, test
def train(model, data, args):
"""
Training a CapsuleNet
:param model: the CapsuleNet model
:param data: a tuple containing training and testing data, like `((x_train, y_train), (x_test, y_test))`
:param args: arguments
:return: The trained model
"""
# unpacking the data
(x_train, y_train), (x_test, y_test) = data
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size, histogram_freq=args.debug)
lr_decay = callbacks.LearningRateScheduler(schedule=lambda epoch: args.lr * (0.9 ** epoch))
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon])
"""
# Training without data augmentation:
model.fit([x_train, y_train], [y_train, x_train], batch_size=args.batch_size, epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]], callbacks=[log, tb, checkpoint, lr_decay])
"""
# Begin: Training with data augmentation ---------------------------------------------------------------------#
def train_generator(x, y, batch_size, shift_fraction=0.):
train_datagen = ImageDataGenerator(width_shift_range=shift_fraction,
height_shift_range=shift_fraction) # shift up to 2 pixel for MNIST
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
yield ([x_batch, y_batch], [y_batch, x_batch])
# Training with data augmentation. If shift_fraction=0., also no augmentation.
model.fit_generator(generator=train_generator(x_train, y_train, args.batch_size, args.shift_fraction),
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, tb, lr_decay])
# End: Training with data augmentation -----------------------------------------------------------------------#
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
if __name__ == "__main__":
import numpy as np
import tensorflow as tf
import os
from keras.preprocessing.image import ImageDataGenerator
from keras import callbacks
from keras.utils.vis_utils import plot_model
from keras.utils import multi_gpu_model
# setting the hyper parameters
import argparse
parser = argparse.ArgumentParser(description="Capsule Network on MNIST.")
parser.add_argument('--epochs', default=50, type=int)
parser.add_argument('--batch_size', default=300, type=int)
parser.add_argument('--lam_recon', default=0.392, type=float,
help="The coefficient for the loss of decoder")
parser.add_argument('-r', '--routings', default=3, type=int,
help="Number of iterations used in routing algorithm. should > 0")
parser.add_argument('--shift_fraction', default=0.1, type=float,
help="Fraction of pixels to shift at most in each direction.")
parser.add_argument('--debug', default=0, type=int,
help="Save weights by TensorBoard")
parser.add_argument('--save_dir', default='./result')
parser.add_argument('-t', '--testing', action='store_true',
help="Test the trained model on testing dataset")
parser.add_argument('--digit', default=5, type=int,
help="Digit to manipulate")
parser.add_argument('-w', '--weights', default=None,
help="The path of the saved weights. Should be specified when testing")
parser.add_argument('--lr', default=0.001, type=float,
help="Initial learning rate")
parser.add_argument('--gpus', default=2, type=int)
args = parser.parse_args()
print(args)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
# load data
(x_train, y_train), (x_test, y_test) = load_mnist()
# define model
with tf.device('/cpu:0'):
model, eval_model, manipulate_model = CapsNet(input_shape=x_train.shape[1:],
n_class=len(np.unique(np.argmax(y_train, 1))),
routings=args.routings)
model.summary()
plot_model(model, to_file=args.save_dir+'/model.png', show_shapes=True)
# train or test
if args.weights is not None: # init the model weights with provided one
model.load_weights(args.weights)
if not args.testing:
# define muti-gpu model
multi_model = multi_gpu_model(model, gpus=args.gpus)
train(model=multi_model, data=((x_train, y_train), (x_test, y_test)), args=args)
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
test(model=eval_model, data=(x_test, y_test), args=args)
else: # as long as weights are given, will run testing
if args.weights is None:
print('No weights are provided. Will test using random initialized weights.')
manipulate_latent(manipulate_model, (x_test, y_test), args)
test(model=eval_model, data=(x_test, y_test), args=args)