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models.py
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models.py
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from layers import *
from metrics import *
flags = tf.app.flags
FLAGS = flags.FLAGS
class Model(object):
def __init__(self, **kwargs):
allowed_kwargs = {'name', 'logging', 'sparse_fea'}
for kwarg in kwargs.keys():
assert kwarg in allowed_kwargs, 'Invalid keyword argument: ' + kwarg
name = kwargs.get('name')
if not name:
name = self.__class__.__name__.lower()
self.name = name
logging = kwargs.get('logging', False)
self.logging = logging
sparse_fea = kwargs.get('sparse_fea', True)
self.sparse_fea = sparse_fea
self.vars = {}
self.placeholders = {}
self.layers = []
self.activations = []
self.inputs = None
self.outputs = None
self.loss = 0
self.accuracy = 0
self.optimizer = None
self.opt_op = None
def _build(self):
raise NotImplementedError
def build(self):
""" Wrapper for _build() """
with tf.variable_scope(self.name):
self._build()
# Build sequential layer model
self.activations.append(self.inputs)
for layer in self.layers:
if isinstance(layer, tf.layers.BatchNormalization):
hidden = layer(self.activations[-1], training=self.placeholders['training'])
else:
hidden = layer(self.activations[-1])
self.activations.append(hidden)
self.outputs = self.activations[-1]
# Store model variables for easy access
variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name)
self.vars = {var.name: var for var in variables}
# Build metrics
self._loss()
self._accuracy()
self.opt_op = self.optimizer.minimize(self.loss)
def predict(self):
pass
def _loss(self):
raise NotImplementedError
def _accuracy(self):
raise NotImplementedError
def save(self, sess=None):
if not sess:
raise AttributeError("TensorFlow session not provided.")
saver = tf.train.Saver(self.vars)
save_path = saver.save(sess, "tmp/%s.ckpt" % self.name)
print("Model saved in file: %s" % save_path)
def load(self, sess=None):
if not sess:
raise AttributeError("TensorFlow session not provided.")
saver = tf.train.Saver(self.vars)
save_path = "tmp/%s.ckpt" % self.name
saver.restore(sess, save_path)
print("Model restored from file: %s" % save_path)
'''
This is the exact model implements the Cross-GCN described in the paper.
'''
class GCNMI(Model):
def __init__(self, placeholders, input_dim, **kwargs):
super(GCNMI, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = input_dim
# self.input_dim = self.inputs.get_shape().as_list()[1] # To be supported in future Tensorflow versions
self.output_dim = placeholders['labels'].get_shape().as_list()[1]
self.placeholders = placeholders
self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
self.build()
def _loss(self):
# # Weight decay loss
# for var in self.layers[0].vars.values():
# self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var)
# Weight decay loss
for layer in self.layers:
for var in layer.vars.values():
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var)
# Cross entropy error
self.loss += masked_softmax_cross_entropy(self.outputs, self.placeholders['labels'],
self.placeholders['labels_mask'])
def _accuracy(self):
self.accuracy = masked_accuracy(self.outputs, self.placeholders['labels'],
self.placeholders['labels_mask'])
def _build(self):
self.layers.append(GCMapInter(input_dim=self.input_dim,
alpha=FLAGS.alpha1 if hasattr(FLAGS, 'alpha1') else 1.,
output_dim=FLAGS.hidden1,
placeholders=self.placeholders,
act=tf.nn.relu,
dropout=True,
sparse_inputs=self.sparse_fea,
logging=self.logging))
self.layers.append(GCMapInter(input_dim=FLAGS.hidden1,
alpha=FLAGS.alpha2 if hasattr(FLAGS, 'alpha2') else 1.,
output_dim=self.output_dim,
placeholders=self.placeholders,
act=lambda x: x,
dropout=True,
logging=self.logging))
def predict(self):
return tf.nn.softmax(self.outputs)
# single layer
class GCNMI1L(Model):
def __init__(self, placeholders, input_dim, **kwargs):
super(GCNMI1L, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = input_dim
# self.input_dim = self.inputs.get_shape().as_list()[1] # To be supported in future Tensorflow versions
self.output_dim = placeholders['labels'].get_shape().as_list()[1]
self.placeholders = placeholders
self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
self.build()
def _loss(self):
# Weight decay loss
for var in self.layers[0].vars.values():
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var)
# Cross entropy error
self.loss += masked_softmax_cross_entropy(self.outputs, self.placeholders['labels'],
self.placeholders['labels_mask'])
def _accuracy(self):
self.accuracy = masked_accuracy(self.outputs, self.placeholders['labels'],
self.placeholders['labels_mask'])
def _build(self):
self.layers.append(GCMapInter(input_dim=self.input_dim,
alpha=FLAGS.alpha1 if hasattr(FLAGS, 'alpha1') else 1.,
output_dim=self.output_dim,
placeholders=self.placeholders,
act=lambda x: x,
dropout=True,
sparse_inputs=self.sparse_fea,
logging=self.logging))
def predict(self):
return tf.nn.softmax(self.outputs)
'''
Compared to GCNMI, GCNMIA has an additional parameter alpha updated by gradient.
'''
class GCNMIA(Model):
def __init__(self, placeholders, input_dim, **kwargs):
super(GCNMIA, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = input_dim
# self.input_dim = self.inputs.get_shape().as_list()[1] # To be supported in future Tensorflow versions
self.output_dim = placeholders['labels'].get_shape().as_list()[1]
self.placeholders = placeholders
self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
self.build()
def _loss(self):
# # Weight decay loss
# for var in self.layers[0].vars.values():
# self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var)
# Weight decay loss
for layer in self.layers:
for var in layer.vars.values():
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var)
# Cross entropy error
self.loss += masked_softmax_cross_entropy(self.outputs, self.placeholders['labels'],
self.placeholders['labels_mask'])
def _accuracy(self):
self.accuracy = masked_accuracy(self.outputs, self.placeholders['labels'],
self.placeholders['labels_mask'])
def _build(self):
self.layers.append(GCMapInterAda(input_dim=self.input_dim,
alpha=FLAGS.alpha1 if hasattr(FLAGS, 'alpha1') else 1.,
output_dim=FLAGS.hidden1,
placeholders=self.placeholders,
act=tf.nn.relu,
dropout=True,
sparse_inputs=self.sparse_fea,
logging=self.logging))
self.layers.append(GCMapInterAda(input_dim=FLAGS.hidden1,
alpha=FLAGS.alpha2 if hasattr(FLAGS, 'alpha2') else 1.,
output_dim=self.output_dim,
placeholders=self.placeholders,
act=lambda x: x,
dropout=True,
logging=self.logging))
def predict(self):
return tf.nn.softmax(self.outputs)
# single layer
class GCNMIA1L(Model):
def __init__(self, placeholders, input_dim, **kwargs):
super(GCNMIA1L, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = input_dim
# self.input_dim = self.inputs.get_shape().as_list()[1] # To be supported in future Tensorflow versions
self.output_dim = placeholders['labels'].get_shape().as_list()[1]
self.placeholders = placeholders
self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
self.build()
def _loss(self):
# Weight decay loss
for var in self.layers[0].vars.values():
self.loss += FLAGS.weight_decay * tf.nn.l2_loss(var)
# Cross entropy error
self.loss += masked_softmax_cross_entropy(self.outputs, self.placeholders['labels'],
self.placeholders['labels_mask'])
def _accuracy(self):
self.accuracy = masked_accuracy(self.outputs, self.placeholders['labels'],
self.placeholders['labels_mask'])
def _build(self):
self.layers.append(GCMapInterAda(input_dim=self.input_dim,
alpha=FLAGS.alpha1 if hasattr(FLAGS, 'alpha1') else 1.,
output_dim=self.output_dim,
placeholders=self.placeholders,
act=lambda x: x,
dropout=True,
sparse_inputs=self.sparse_fea,
logging=self.logging))
def predict(self):
return tf.nn.softmax(self.outputs)