class SiameseNet(object):
"""Class for Siamese Network."""
def __init__(self, inputs, arch, siam_reg, main_path, y_true):
self.orig_inputs = inputs
# set up inputs
self.inputs = {
'A': inputs['Unlabeled'],
'B': Input(shape=inputs['Unlabeled'].get_shape().as_list()[1:]),
'Labeled': inputs['Labeled'],
}
self.main_path = os.path.join(main_path, 'siemese/')
self.y_true = y_true
# generate layers
self.layers = []
self.layers += util.make_layer_list(arch, 'siamese', siam_reg)
# create the siamese net
self.outputs = stack_layers(self.inputs, self.layers)
# add the distance layer
self.distance = Lambda(affinities.euclidean_distance,
output_shape=affinities.eucl_dist_output_shape)(
[self.outputs['A'], self.outputs['B']])
# create the distance model for training
self.net = Model([self.inputs['A'], self.inputs['B']], self.distance)
# compile the siamese network
self.net.compile(loss=affinities.get_contrastive_loss(m_neg=1,
m_pos=0.05),
optimizer='rmsprop')
def train(self,
pairs_train,
dist_train,
pairs_val,
dist_val,
lr,
drop,
patience,
num_epochs,
batch_size,
dset,
load=True):
"""Train the Siamese Network."""
if load:
# load weights into model
output_path = os.path.join(self.main_path, dset)
load_model(self.net, output_path, '_siamese')
return
# create handler for early stopping and learning rate scheduling
self.lh = util.LearningHandler(lr=lr,
drop=drop,
lr_tensor=self.net.optimizer.lr,
patience=patience)
# initialize the training generator
train_gen_ = util.train_gen(pairs_train, dist_train, batch_size)
# format the validation data for keras
validation_data = ([pairs_val[:, 0], pairs_val[:, 1]], dist_val)
# compute the steps per epoch
steps_per_epoch = int(len(pairs_train) / batch_size)
# train the network
self.net.fit_generator(train_gen_,
epochs=num_epochs,
validation_data=validation_data,
steps_per_epoch=steps_per_epoch,
callbacks=[self.lh])
model_json = self.net.to_json()
output_path = os.path.join(self.main_path, dset)
save_model(self.net, model_json, output_path, '_siamese')
def predict(self, x, batch_sizes):
# compute the siamese embeddings of the input data
return train.predict(self.outputs['A'],
x_unlabeled=x,
inputs=self.orig_inputs,
y_true=self.y_true,
batch_sizes=batch_sizes)
class CncNet(object):
"""Class for CNC Network."""
def __init__(self,
inputs,
arch,
cnc_reg,
y_true,
y_train_labeled_onehot,
n_clusters,
affinity,
scale_nbr,
n_nbrs,
batch_sizes,
result_path,
dset,
siamese_net=None,
x_train=None,
lr=0.01,
temperature=1.0,
bal_reg=0.0):
self.y_true = y_true
self.y_train_labeled_onehot = y_train_labeled_onehot
self.inputs = inputs
self.batch_sizes = batch_sizes
self.result_path = result_path
self.lr = lr
self.temperature = temperature
# generate layers
self.layers = util.make_layer_list(arch[:-1], 'cnc', cnc_reg)
print('Runing with CNC loss')
self.layers += [{
'type': 'None',
'size': n_clusters,
'l2_reg': cnc_reg,
'name': 'cnc_{}'.format(len(arch))
}]
# create CncNet
self.outputs = stack_layers(self.inputs, self.layers)
self.net = Model(inputs=self.inputs['Unlabeled'],
outputs=self.outputs['Unlabeled'])
# DEFINE LOSS
# generate affinity matrix W according to params
if affinity == 'siamese':
input_affinity = tf.concat(
[siamese_net.outputs['A'], siamese_net.outputs['Labeled']],
axis=0)
x_affinity = siamese_net.predict(x_train, batch_sizes)
elif affinity in ['knn', 'full']:
input_affinity = tf.concat(
[self.inputs['Unlabeled'], self.inputs['Labeled']], axis=0)
x_affinity = x_train
# calculate scale for affinity matrix
scale = util.get_scale(x_affinity, self.batch_sizes['Unlabeled'],
scale_nbr)
# create affinity matrix
if affinity == 'full':
weight_mat = affinities.full_affinity(input_affinity, scale=scale)
elif affinity in ['knn', 'siamese']:
weight_mat = affinities.knn_affinity(input_affinity,
n_nbrs,
scale=scale,
scale_nbr=scale_nbr)
# define loss
self.tau = tf.Variable(self.temperature, name='temperature')
self.outputs['Unlabeled'] = util.gumbel_softmax(
self.outputs['Unlabeled'], self.tau)
num_nodes = self.batch_sizes['Unlabeled']
cluster_size = tf.reduce_sum(self.outputs['Unlabeled'], axis=0)
ground_truth = [num_nodes / float(n_clusters)] * n_clusters
bal = tf.losses.mean_squared_error(ground_truth, cluster_size)
degree = tf.expand_dims(tf.reduce_sum(weight_mat, axis=1), 0)
vol = tf.matmul(degree, self.outputs['Unlabeled'], name='vol')
normalized_prob = tf.divide(self.outputs['Unlabeled'],
vol[tf.newaxis, :],
name='normalized_prob')[0]
gain = tf.matmul(normalized_prob,
tf.transpose(1 - self.outputs['Unlabeled']),
name='res2')
self.loss = tf.reduce_sum(gain * weight_mat) + bal_reg * bal
# create the train step update
self.learning_rate = tf.Variable(self.lr, name='cnc_learning_rate')
self.train_step = tf.train.RMSPropOptimizer(
learning_rate=self.learning_rate).minimize(
self.loss, var_list=self.net.trainable_weights)
# initialize cnc_net variables
K.get_session().run(tf.global_variables_initializer())
K.get_session().run(
tf.variables_initializer(self.net.trainable_weights))
if affinity == 'siamese':
output_path = os.path.join(self.main_path, dset)
load_model(siamese_net, output_path, '_siamese')
def train(self,
x_train_unlabeled,
x_train_labeled,
x_val_unlabeled,
drop,
patience,
min_tem,
num_epochs,
load=False):
"""Train the CNC network."""
file_name = 'cnc_net'
if load:
# load weights into model
print('load pretrain weights of the CNC network.')
load_model(self.net, self.result_path, file_name)
return
# create handler for early stopping and learning rate scheduling
self.lh = util.LearningHandler(lr=self.lr,
drop=drop,
lr_tensor=self.learning_rate,
patience=patience,
tau=self.temperature,
tau_tensor=self.tau,
min_tem=min_tem,
gumble=True)
losses = np.empty((num_epochs, ))
val_losses = np.empty((num_epochs, ))
# begin cnc_net training loop
self.lh.on_train_begin()
for i in range(num_epochs):
# train cnc_net
losses[i] = train.train_step(return_var=[self.loss],
updates=self.net.updates +
[self.train_step],
x_unlabeled=x_train_unlabeled,
inputs=self.inputs,
y_true=self.y_true,
batch_sizes=self.batch_sizes,
x_labeled=x_train_labeled,
y_labeled=self.y_train_labeled_onehot,
batches_per_epoch=100)[0]
# get validation loss
val_losses[i] = train.predict_sum(
self.loss,
x_unlabeled=x_val_unlabeled,
inputs=self.inputs,
y_true=self.y_true,
x_labeled=x_train_unlabeled[0:0],
y_labeled=self.y_train_labeled_onehot,
batch_sizes=self.batch_sizes)
# do early stopping if necessary
if self.lh.on_epoch_end(i, val_losses[i]):
print('STOPPING EARLY')
break
# print training status
print('Epoch: {}, loss={:2f}, val_loss={:2f}'.format(
i, losses[i], val_losses[i]))
with gfile.Open(self.result_path + 'losses', 'a') as f:
f.write(
str(i) + ' ' + str(losses[i]) + ' ' + str(val_losses[i]) +
'\n')
model_json = self.net.to_json()
save_model(self.net, model_json, self.result_path, file_name)
def predict(self, x):
# test inputs do not require the 'Labeled' input
inputs_test = {'Unlabeled': self.inputs['Unlabeled']}
return train.predict(self.outputs['Unlabeled'],
x_unlabeled=x,
inputs=inputs_test,
y_true=self.y_true,
x_labeled=x[0:0],
y_labeled=self.y_train_labeled_onehot[0:0],
batch_sizes=self.batch_sizes)
