def get_layer(self, load, name):
"""
Get the actual values in array_like form from an abstract tensor.
:param load: the loader object to iterate over
:param name: the name of the tensor to evaluate for each point
"""
tensor_name = "{}:0".format(name)
tensor = tbn(tensor_name)
layer = []
labels = []
for batch in load.iter_batches():
feed = {
tbn('x:0'): batch[0],
tbn('y:0'): batch[0],
tbn('is_training:0'): False
}
if len(batch) == 2:
feed[tbn('batches:0')] = batch[1]
labels.append(batch[1])
[act] = self.sess.run([tensor], feed_dict=feed)
layer.append(act)
layer = np.concatenate(layer, axis=0)
if labels:
labels = np.concatenate(labels, axis=0)
return layer, labels
else:
return layer
def train(self, load, steps, batch_size=256):
"""
Train SAUCIE.
:param load: the loader object to yield batches from
:param steps: the number of steps to scripts for
:param batch_size: the number of points to scripts on in each step
"""
start = self.iteration
while (self.iteration - start) < steps:
self.iteration += 1
batch = load.next_batch(batch_size=batch_size)
feed = {
tbn('x:0'): batch[0],
tbn('y:0'): batch[0],
tbn('is_training:0'): True,
tbn('learning_rate_tensor:0'): self.learning_rate
}
if len(batch) == 2:
feed[tbn('batches:0')] = batch[1]
# if using batch-correction, must have labels
if (self.lambda_b and len(batch) < 2):
raise Exception(
"If using lambda_b (batch correction), you must provide each point's batch as a label"
)
ops = [obn('train_op')]
self.sess.run(ops, feed_dict=feed)
def get_loss(self, load, batch_size=256):
"""
Get the current losses over the dataset.
:param load: the loader object to iterate over
"""
losses = None
for i, batch in enumerate(load.iter_batches(batch_size=batch_size)):
feed = {
tbn('x:0'): batch[0],
tbn('y:0'): batch[0],
tbn('is_training:0'): False
}
if len(batch) == 2:
feed[tbn('batches:0')] = batch[1]
batch_losses = self.sess.run(tf.get_collection('losses'),
feed_dict=feed)
if not losses:
losses = batch_losses
else:
losses = [
loss + batch_loss
for loss, batch_loss in zip(losses, batch_losses)
]
losses = [loss / float(i + 1) for loss in losses]
lstring = ' '.join(['{:.3f}'.format(loss) for loss in losses])
return lstring
def _build_losses(self):
"""Build all the loss ops for the network."""
self.loss_recon = 0.
if self.lambda_b:
with tf.variable_scope('reconstruction_mmd'):
self._build_reconstruction_loss_mmd(self.reconstructed, self.x)
with tf.variable_scope('batchcorrection'):
self._build_reg_b()
else:
with tf.variable_scope('reconstruction'):
self._build_reconstruction_loss(self.reconstructed, self.x)
if self.lambda_c:
with tf.variable_scope('clustering'):
self.loss_c = 0
act = tbn('layer_c:0')
act = act / tf.reduce_max(act)
self._build_reg_c(act)
if self.lambda_d:
with tf.variable_scope('intracluster_distances'):
self._build_reg_d(act)
self._build_total_loss()