def call(self, input_tensor, training=None):
inputs_hat = self.get_predictions(input_tensor)
b = tf.zeros(
shape=[K.shape(inputs_hat)[0], self.num_caps, self.num_in_caps])
assert self.routings > 0, 'routing should be > 0.'
for i in range(self.routings):
# c.shape=[batch_size, num_caps, num_in_caps]
c = tf.nn.softmax(b, axis=1)
activations = squash(K.batch_dot(c, inputs_hat,
[2, 2])) # [None, 10, 16]
return activations
def build_network(self, x):
# Building network...
with tf.variable_scope('CapsNet'):
# Layer 1: A 2D conv layer
conv1 = layers.Conv2D(filters=256, kernel_size=9, strides=1,
padding='valid', activation='relu', name='conv1')(x)
# Layer 2: Primary Capsule Layer; simply a 2D conv + reshaping
primary_caps = layers.Conv2D(filters=256, kernel_size=9, strides=2,
padding='valid', activation='relu', name='primary_caps')(conv1)
_, H, W, dim = primary_caps.get_shape()
num_caps = H.value * W.value * dim.value / self.conf.prim_caps_dim
primary_caps_reshaped = layers.Reshape((num_caps, self.conf.prim_caps_dim))(primary_caps)
caps1_output = squash(primary_caps_reshaped)
# Layer 3: Digit Capsule Layer; Here is where the routing takes place
self.digit_caps = FCCapsuleLayer(num_caps=self.conf.num_cls, caps_dim=self.conf.digit_caps_dim,
routings=3, name='digit_caps')(caps1_output)
# [?, 10, 16]
self.mask()
self.decoder()
def build_network(self, x):
# Building network...
with tf.variable_scope('CapsNet'):
# Layer 1: A 3D conv layer
conv1 = layers.Conv3D(filters=256,
kernel_size=9,
strides=1,
padding='valid',
activation='relu',
name='conv1')(x)
# Layer 2: Primary Capsule Layer; simply a 3D conv + reshaping
primary_caps = layers.Conv3D(filters=256,
kernel_size=9,
strides=2,
padding='valid',
activation='relu',
name='primary_caps')(conv1)
_, H, W, D, dim = primary_caps.get_shape()
primary_caps_reshaped = layers.Reshape(
(H.value * W.value * D.value, dim.value))(primary_caps)
caps1_output = squash(primary_caps_reshaped)
# [?, 512, 256]
# Layer 3: Digit Capsule Layer; Here is where the routing takes place
digitcaps_layer = FCCapsuleLayer(num_caps=self.conf.num_cls,
caps_dim=self.conf.digit_caps_dim,
routings=3,
name='digit_caps')
self.digit_caps = digitcaps_layer(caps1_output) # [?, 2, 16]
u_hat = digitcaps_layer.get_predictions(
caps1_output) # [?, 2, 512, 16]
u_hat_shape = u_hat.get_shape().as_list()
self.img_s = int(round(u_hat_shape[2]**(1. / 3)))
self.u_hat = layers.Reshape(
(self.conf.num_cls, self.img_s, self.img_s, self.img_s, 1,
self.conf.digit_caps_dim))(u_hat)
# self.u_hat = tf.transpose(u_hat, perm=[1, 0, 2, 3, 4, 5, 6])
# u_hat: [?, 2, 8, 8, 8, 1, 16]
self.decoder()