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capsulenet.py
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"""
Keras implementation of CapsNet in Hinton's paper Dynamic Routing Between Capsules.
Usage:
python CapsNet.py
python CapsNet.py --epochs 100
python CapsNet.py --epochs 100 --num_routing 3
... ...
"""
from keras import layers, models
from keras import backend as K
from capsulelayers import CapsuleLayer, PrimaryCap, Length, Mask
from keras.preprocessing import sequence
max_features = 5000
maxlen = 400
embed_dim = 50
def CapsNet(input_shape, n_class, num_routing):
"""
A Capsule Network on MNIST.
:param input_shape: data shape, 4d, [None, width, height, channels]
:param n_class: number of classes
:param num_routing: number of routing iterations
:return: A Keras Model with 2 inputs and 2 outputs
"""
x = layers.Input(shape=(maxlen,))
embed = layers.Embedding(max_features, embed_dim, input_length=maxlen)(x)
conv1 = layers.Conv1D(filters=256, kernel_size=9, strides=1, padding='valid', activation='relu', name='conv1')(
embed)
# Layer 2: Conv2D layer with `squash` activation, then reshape to [None, num_capsule, dim_vector]
primarycaps = PrimaryCap(conv1, dim_vector=8, n_channels=32, kernel_size=9, strides=2, padding='valid')
# Layer 3: Capsule layer. Routing algorithm works here.
digitcaps = CapsuleLayer(num_capsule=n_class, dim_vector=16, num_routing=num_routing, name='digitcaps')(primarycaps)
# Layer 4: This is an auxiliary layer to replace each capsule with its length. Just to match the true label's shape.
# If using tensorflow, this will not be necessary. :)
out_caps = Length(name='out_caps')(digitcaps)
# Decoder network.
y = layers.Input(shape=(n_class,))
masked = Mask()([digitcaps, y]) # The true label is used to mask the output of capsule layer.
x_recon = layers.Dense(512, activation='relu')(masked)
x_recon = layers.Dense(1024, activation='relu')(x_recon)
x_recon = layers.Dense(maxlen, activation='sigmoid')(x_recon)
# x_recon = layers.Reshape(target_shape=[1], name='out_recon')(x_recon)
# two-input-two-output keras Model
return models.Model([x, y], [out_caps, x_recon])
def margin_loss(y_true, y_pred):
"""
Margin loss for Eq.(4). When y_true[i, :] contains not just one `1`, this loss should work too. Not test it.
:param y_true: [None, n_classes]
:param y_pred: [None, num_capsule]
:return: a scalar loss value.
"""
L = y_true * K.square(K.maximum(0., 0.9 - y_pred)) + \
0.5 * (1 - y_true) * K.square(K.maximum(0., y_pred - 0.1))
return K.mean(K.sum(L, 1))
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)
checkpoint = callbacks.ModelCheckpoint(args.save_dir + '/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.))
# compile the model
model.compile(optimizer='adam',
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon],
metrics={'out_caps': 'accuracy'})
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], verbose=1)
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def test(model, data):
x_test, y_test = data
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])
import matplotlib.pyplot as plt
from utils import combine_images
from PIL import Image
img = combine_images(np.concatenate([x_test[:50], x_recon[:50]]))
image = img * 255
Image.fromarray(image.astype(np.uint8)).save("real_and_recon.png")
print()
print('Reconstructed images are saved to ./real_and_recon.png')
print('-' * 50)
plt.imshow(plt.imread("real_and_recon.png", ))
plt.show()
def load_imdb(maxlen=400):
from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
return (x_train, y_train), (x_test, y_test)
if __name__ == "__main__":
import numpy as np
import os
from keras import callbacks
from keras.utils.vis_utils import plot_model
# setting the hyper parameters
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', default=100, type=int)
parser.add_argument('--epochs', default=20, type=int)
parser.add_argument('--lam_recon', default=0.0005, type=float)
parser.add_argument('--num_routing', default=3, type=int) # num_routing should > 0
parser.add_argument('--shift_fraction', default=0.1, type=float)
parser.add_argument('--debug', default=0, type=int) # debug>0 will save weights by TensorBoard
parser.add_argument('--save_dir', default='./result')
parser.add_argument('--is_training', default=1, type=int)
parser.add_argument('--weights', default=None)
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_imdb()
print(x_train.shape)
print(y_train.shape)
# define model
model = CapsNet(input_shape=x_train.shape,
n_class=1,
num_routing=args.num_routing)
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 args.is_training:
train(model=model, data=((x_train, y_train), (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.')
test(model=model, data=(x_test, y_test))