def _residual(self, h, channels, strides, keep_prob, is_train):
h0 = h
h1 = F.conv(F.activation(F.batch_norm(self, 'bn1', h0, is_train)), channels, strides)
h1 = F.dropout(h1, keep_prob, is_train)
h2 = F.conv(F.activation(F.batch_norm(self, 'bn2', h1, is_train)), channels)
if F.volume(h0) == F.volume(h2):
h = h0 + h2
else :
h4 = F.conv(h0, channels, strides)
h = h2 + h4
return h
def _residual(self, h, channels, strides, keep_prob, is_train):
h0 = h
with tf.variable_scope('residual_first'):
h1 = F.conv(F.activation(F.batch_norm(h0, is_train)), channels, strides)
h1 = F.dropout(h1, keep_prob, is_train)
with tf.variable_scope('residual_second'):
h2 = F.conv(F.activation(F.batch_norm(h1, is_train)), channels)
if F.volume(h0) == F.volume(h2):
h = h0 + h2
else :
h4 = F.conv(h0, channels, strides)
h = h2 + h4
return h
def _residual(self, h, channels, strides, keep_prob):
h0 = h
h1 = F.dropout(
F.conv(F.activation(F.batch_normalization(h0)), channels, strides),
keep_prob)
h2 = F.conv(F.activation(F.batch_normalization(h1)), channels)
# c.f. http://gitxiv.com/comments/7rffyqcPLirEEsmpX
if F.volume(h0) == F.volume(h2):
h = h2 + h0
else:
h4 = F.conv(h0, channels, strides)
h = h2 + h4
return h
def _residual(self, h, channels, strides):
h0 = h
h1 = F.activation(
F.batch_normalization(
F.conv(h0, channels, strides, bias_term=False)))
h2 = F.batch_normalization(F.conv(h1, channels, bias_term=False))
if F.volume(h0) == F.volume(h2):
h = h2 + h0
else:
h3 = F.avg_pool(h0)
h4 = tf.pad(h3,
[[0, 0], [0, 0], [0, 0], [channels / 4, channels / 4]])
h = h2 + h4
return h
def _residual(self, h, channels, strides):
h0 = h
h1 = F.activation(
F.batch_normalization(
F.conv(h0, channels, strides, bias_term=False)))
h2 = F.batch_normalization(F.conv(h1, channels, bias_term=False))
# c.f. http://gitxiv.com/comments/7rffyqcPLirEEsmpX
if F.volume(h0) == F.volume(h2):
h = h2 + h0
else:
h3 = F.avg_pool(h0)
h4 = tf.pad(h3,
[[0, 0], [0, 0], [0, 0], [channels / 4, channels / 4]])
h = h2 + h4
return F.activation(h)
def esitmator_function(self, r):
total_sum = 0
n = len(self.all_sd) # number of super-droplets
for super_droplet in self.all_sd:
total_sum += 1 / V * super_droplet.ksi * volume(
super_droplet.r) * rho * w(
math.log(r) - math.log(super_droplet.r), n)
return total_sum
