def misconception_fishing_2(input,
window_size,
depths,
strides,
objective_function,
is_training,
pre_count=128,
post_count=128,
post_layers=1,
keep_prob=0.5,
internal_keep_prob=0.5,
other_objectives=()):
dt = tf.exp(input[:, 0, :, 0]) - 1
dt = tf.maximum(dt, 12 * 60 * 60)
dt = 0.5 * (dt[:, 1:] + dt[:, :-1])
_, layers = misconception_model(input,
window_size,
depths,
strides,
other_objectives,
is_training,
sub_count=post_count,
sub_layers=2)
expanded_layers = []
for i, lyr in enumerate(layers):
lyr = slim.conv2d(lyr,
pre_count, [1, 1],
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params={'is_training': is_training})
expanded_layers.append(utility.repeat_tensor(lyr, 2**i))
embedding = tf.add_n(expanded_layers)
for _ in range(post_layers - 1):
embedding = slim.conv2d(embedding,
post_count, [1, 1],
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params={'is_training': is_training})
embedding = slim.conv2d(embedding,
post_count, [1, 1],
activation_fn=tf.nn.relu,
normalizer_fn=None)
embedding = slim.dropout(embedding, keep_prob, is_training=is_training)
fishing_outputs = tf.squeeze(slim.conv2d(embedding,
1, [1, 1],
activation_fn=None,
normalizer_fn=None),
squeeze_dims=[1, 3])
return objective_function.build(fishing_outputs, dt)
def misconception_with_fishing_ranges(self, input, mmsis, is_training):
""" A misconception tower with additional fishing range classification.
Args:
input: a tensor of size [batch_size, 1, width, depth].
window_size: the width of the conv and pooling filters to apply.
stride: the downsampling to apply when filtering.
depth: the depth of the output tensor.
levels: The height of the tower in misconception layers.
Returns:
a tensor of size [batch_size, num_classes].
"""
with slim.arg_scope([slim.fully_connected], activation_fn=tf.nn.elu):
net = input
# Then a tower for classification.
multiscale_layers = []
for i in range(self.levels):
with tf.variable_scope("layer_%d" % i):
multiscale_layers.append(utility.repeat_tensor(net, 2**i))
net = layers.misconception_with_bypass(
net, self.window_size, self.stride, self.feature_depth,
is_training)
# TODO: We currently don't use the last year for fishing classification
# Since we don't use this for vessel classification currently, perhaps
# we should rememdy that...
net = slim.flatten(net)
net = slim.dropout(net, 0.5, is_training=is_training)
net = slim.fully_connected(net, 100)
net = slim.dropout(net, 0.5, is_training=is_training)
concatenated_multiscale_embedding = tf.concat(3, multiscale_layers)
fishing_outputs = tf.squeeze(slim.conv2d(
concatenated_multiscale_embedding,
1, [1, 1],
activation_fn=None),
squeeze_dims=[1, 3])
for of in self.classification_training_objectives:
of.build(net)
self.fishing_localisation_objective.build(fishing_outputs)