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models_tf.py
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from layers_tf import *
from metrics_tf import *
flags = tf.app.flags
FLAGS = flags.FLAGS
class Model(object):
def __init__(self, **kwargs):
allowed_kwargs = {'name', 'logging'}
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
self.vars = {}
self.placeholders = {}
self.layers = []
self.activations = []
self.inputs = None
self.outputs = None
self.entropy = None
self.loss = 0
self.accuracy = 0
self.optimizer = None
self.opt_op = None
self.update_ops=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:
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._entropy()
self.update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(self.update_ops):
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)
class MLP(Model):
def __init__(self, placeholders, input_dim, **kwargs):
super(MLP, 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 _entropy(self):
self.entropy= masked_softmax_cross_entropy(self.outputs, self.placeholders['labels'],
self.placeholders['labels_mask'])
def _build(self):
self.layers.append(Dense(input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
placeholders=self.placeholders,
act=tf.nn.relu,
dropout=True,
sparse_inputs=True,
logging=self.logging))
self.layers.append(Dense(input_dim=FLAGS.hidden1,
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)
class GCN(Model):
def __init__(self, placeholders, input_dim, **kwargs):
super(GCN, 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 _entropy(self):
self.entropy= 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(GraphConvolution(input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
placeholders=self.placeholders,
act=tf.nn.relu,
dropout=True,
sparse_inputs=True,
logging=self.logging))
self.layers.append(GraphConvolution(input_dim=FLAGS.hidden1,
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)
class DSGCNN(Model):
def __init__(self, placeholders, input_dim, readout='mean', nkernel=1, **kwargs):
super(DSGCNN, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = input_dim
self.output_dim = placeholders['labels'].get_shape().as_list()[1]
self.placeholders = placeholders
self.nkernel=nkernel
self.optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
self.readout=readout
self.build()
def _loss(self):
# Weight decay loss
self.loss=0
tmp=FLAGS.weight_decay
if tmp>0:
for layers in self.layers:
for var in layers.vars.values():
self.loss += tmp * tf.nn.l2_loss(var)
# Cross entropy error
self.loss += softmax_cross_entropy(self.outputs, self.placeholders['labels'])
def _entropy(self):
self.entropy= softmax_cross_entropy(self.outputs, self.placeholders['labels'])
def _accuracy(self):
self.accuracy = inductive_multiaccuracy(self.outputs, self.placeholders['labels'])
def _build(self):
self.layers.append(GraphConvolutionBatch(input_dim=self.input_dim,
output_dim=FLAGS.hidden1,
placeholders=self.placeholders,
act=tf.nn.relu,
nkernel=self.nkernel,
bias=True,
dropout=False,
logging=self.logging))
self.layers.append(GraphConvolutionBatch(input_dim=FLAGS.hidden1,
output_dim=FLAGS.hidden2,
placeholders=self.placeholders,
act=tf.nn.relu,
nkernel=self.nkernel,
bias=True,
dropout=True,
logging=self.logging))
self.layers.append(GraphConvolutionBatch(input_dim=FLAGS.hidden2,
output_dim=FLAGS.hidden3,
placeholders=self.placeholders,
act=tf.nn.relu,
nkernel=self.nkernel,
bias=True,
dropout=True,
logging=self.logging))
self.layers.append(ReadoutLayer(method=self.readout,placeholders=self.placeholders))
self.layers.append(Dense(input_dim=FLAGS.dense,
output_dim=FLAGS.dense2,
placeholders=self.placeholders,
act=tf.nn.relu,
dropout=True,
bias=True,
logging=self.logging))
self.layers.append(Dense(input_dim=FLAGS.dense2,
output_dim=self.output_dim,
placeholders=self.placeholders,
act=lambda x: x,
bias=True,
dropout=False,
logging=self.logging))
def predict(self):
return tf.nn.softmax(self.outputs)