This is a TensorFlow implementation of the following paper:
DropBlock: A regularization method for convolutional networks
arXiv. https://arxiv.org/abs/1810.12890
Graph Execution
import numpy as np
import tensorflow as tf
from nets.dropblock import DropBlock
# only support `channels_last` data format
a = tf.placeholder(tf.float32, [None, 10, 10, 3])
keep_prob = tf.placeholder(tf.float32)
training = tf.placeholder(tf.bool)
drop_block = DropBlock(keep_prob=keep_prob, block_size=3)
b = drop_block(a, training)
sess = tf.Session()
feed_dict = {a: np.ones([2, 10, 10, 3]), keep_prob: 0.8, training: True}
c = sess.run(b, feed_dict=feed_dict)
print(c[0, :, :, 0])Eager Execution
import tensorflow as tf
from nets.dropblock import DropBlock
tf.enable_eager_execution()
# only support `channels_last` data format
a = tf.ones([2, 10, 10, 3])
drop_block = DropBlock(keep_prob=0.8, block_size=3)
b = drop_block(a, training=True)
print(b[0, :, :, 0])
# update keep probability
drop_block.set_keep_prob(0.1)
b = drop_block(a, training=True)
print(b[0, :, :, 0])