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layers.py
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layers.py
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from inits import *
import tensorflow as tf
flags = tf.app.flags
FLAGS = flags.FLAGS
# global unique layer ID dictionary for layer name assignment
_LAYER_UIDS = {}
def get_layer_uid(layer_name=''):
"""Helper function, assigns unique layer IDs."""
if layer_name not in _LAYER_UIDS:
_LAYER_UIDS[layer_name] = 1
return 1
else:
_LAYER_UIDS[layer_name] += 1
return _LAYER_UIDS[layer_name]
def sparse_dropout(x, keep_prob, noise_shape):
"""Dropout for sparse tensors."""
random_tensor = keep_prob
random_tensor += tf.random_uniform(noise_shape)
dropout_mask = tf.cast(tf.floor(random_tensor), dtype=tf.bool)
pre_out = tf.sparse_retain(x, dropout_mask)
return pre_out * (1./keep_prob)
def dot(x, y, sparse=False):
"""Wrapper for tf.matmul (sparse vs dense)."""
if sparse:
res = tf.sparse_tensor_dense_matmul(x, y)
else:
res = tf.matmul(x, y)
return res
class Layer(object):
"""Base layer class. Defines basic API for all layer objects.
Implementation inspired by keras (http://keras.io).
# Properties
name: String, defines the variable scope of the layer.
logging: Boolean, switches Tensorflow histogram logging on/off
# Methods
_call(inputs): Defines computation graph of layer
(i.e. takes input, returns output)
__call__(inputs): Wrapper for _call()
_log_vars(): Log all variables
"""
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:
layer = self.__class__.__name__.lower()
name = layer + '_' + str(get_layer_uid(layer))
self.name = name
self.vars = {}
logging = kwargs.get('logging', False)
self.logging = logging
self.sparse_inputs = False
def _call(self, inputs):
return inputs
def __call__(self, inputs):
with tf.name_scope(self.name):
if self.logging and not self.sparse_inputs:
tf.summary.histogram(self.name + '/inputs', inputs)
outputs = self._call(inputs)
if self.logging:
for output in outputs:
tf.summary.histogram(self.name + '/outputs', output)
return outputs
def _log_vars(self):
for var in self.vars:
tf.summary.histogram(self.name + '/vars/' + var, self.vars[var])
class Dense(Layer):
"""Dense layer."""
def __init__(self, input_dim, output_dim, placeholders, dropout=0., sparse_inputs=False,
act=tf.nn.relu, bias=False, featureless=False, **kwargs):
super(Dense, self).__init__(**kwargs)
if dropout:
self.dropout = placeholders['dropout']
else:
self.dropout = 0.
self.act = act
self.sparse_inputs = sparse_inputs
self.featureless = featureless
self.bias = bias
# helper variable for sparse dropout
self.num_features_nonzero = placeholders['num_features_nonzero']
with tf.variable_scope(self.name + '_vars'):
self.vars['weights'] = glorot([input_dim, output_dim],
name='weights')
if self.bias:
self.vars['bias'] = zeros([output_dim], name='bias')
if self.logging:
self._log_vars()
def _call(self, inputs):
x = inputs
# dropout
if self.sparse_inputs:
x = sparse_dropout(x, 1-self.dropout, self.num_features_nonzero)
else:
x = tf.nn.dropout(x, 1-self.dropout)
# transform
output = dot(x, self.vars['weights'], sparse=self.sparse_inputs)
# bias
if self.bias:
output += self.vars['bias']
return self.act(output), 0
class GraphConvolution(Layer):
"""Graph convolution layer."""
def __init__(self, input_dim, output_dim, placeholders, dropout=0.,
support=None, sparse_inputs=False, act=tf.nn.relu, bias=False,
featureless=False, **kwargs):
super(GraphConvolution, self).__init__(**kwargs)
if dropout:
self.dropout = placeholders['dropout']
else:
self.dropout = 0.
self.act = act
if support is None:
self.support = placeholders['support'][0]
else:
self.support = support
self.sparse_inputs = sparse_inputs
self.featureless = featureless
self.bias = bias
# helper variable for sparse dropout
self.num_features_nonzero = placeholders['num_features_nonzero']
with tf.variable_scope(self.name + '_vars'):
for i in range(1):
self.vars['weights_' + str(i)] = glorot([input_dim, output_dim],
name='weights_' + str(i))
if self.bias:
self.vars['bias'] = zeros([output_dim], name='bias')
if self.logging:
self._log_vars()
def _call(self, inputs):
x = inputs
# dropout
if self.sparse_inputs:
x = sparse_dropout(x, 1-self.dropout, self.num_features_nonzero)
else:
x = tf.nn.dropout(x, 1-self.dropout)
if not self.featureless:
pre_sup = dot(x, self.vars['weights_0'],
sparse=self.sparse_inputs)
else:
pre_sup = self.vars['weights_0']
output = dot(self.support, pre_sup, sparse=True)
# bias
if self.bias:
output += self.vars['bias']
return self.act(output), 0
class GraphSampleConvolution(Layer):
"""Graph convolution layer."""
def __init__(self, input_dim, output_dim, placeholders,
support, #prob, self_features,
name = 'con',
sparse_support=True,
sparse_inputs=False, act=tf.nn.relu, bias=False,
dropout=False, featureless=False, **kwargs):
super(GraphSampleConvolution, self).__init__(**kwargs)
if dropout:
self.dropout = placeholders['dropout']
else:
self.dropout = 0.
self.name = name
self.act = act
self.support = support
self.sparse_support = sparse_support
self.sparse_inputs = sparse_inputs
self.featureless = featureless
self.bias = bias
# helper variable for sparse dropout
self.num_features_nonzero = placeholders['num_features_nonzero']
with tf.variable_scope(self.name + '_vars'):
self.vars['weights'] = glorot([input_dim, output_dim],
name='weights')
if self.bias:
self.vars['bias'] = zeros([output_dim], name='bias')
if self.logging:
self._log_vars()
def _call(self, inputs):
x = inputs
# dropout
if self.sparse_inputs:
x = sparse_dropout(x, 1-self.dropout, self.num_features_nonzero)
else:
x = tf.nn.dropout(x, 1-self.dropout)
if not self.featureless:
pre_sup = dot(x, self.vars['weights'], sparse=self.sparse_inputs)
else:
pre_sup = self.vars['weights']
output = dot(self.support, pre_sup, sparse=self.sparse_support)
# bias
if self.bias:
output += self.vars['bias']
return self.act(output), 0
class GraphSampleConvolutionReg(Layer):
"""Graph convolution layer."""
def __init__(self, input_dim, output_dim, placeholders,
support, #prob, self_features,
name = 'con_reg',
sparse_support=True,
sparse_inputs=False, act=tf.nn.relu, bias=False,
dropout=False, featureless=False, **kwargs):
super(GraphSampleConvolutionReg, self).__init__(**kwargs)
if dropout:
self.dropout = placeholders['dropout']
else:
self.dropout = 0.
self.name = name
self.act = act
self.support = support
self.sparse_support = sparse_support
self.sparse_inputs = sparse_inputs
self.featureless = featureless
self.bias = bias
self.output_dim = output_dim
# helper variable for sparse dropout
self.num_features_nonzero = placeholders['num_features_nonzero']
with tf.variable_scope(self.name + '_vars'):
self.vars['weights'] = glorot([input_dim, output_dim],
name='weights')
if self.bias:
self.vars['bias'] = zeros([output_dim], name='bias')
if self.logging:
self._log_vars()
def _call(self, inputs):
x = inputs
# dropout
if self.sparse_inputs:
x = sparse_dropout(x, 1-self.dropout, self.num_features_nonzero)
else:
x = tf.nn.dropout(x, 1-self.dropout)
if not self.featureless:
pre_sup = dot(x, self.vars['weights'], sparse=self.sparse_inputs)
else:
pre_sup = self.vars['weights']
output = dot(self.support, pre_sup, sparse=self.sparse_support)
n_v = tf.cast(tf.shape(self.support)[0], tf.float32)
n_u = tf.cast(tf.shape(self.support)[1], tf.float32)
mean_output = tf.reshape(tf.reduce_mean(output, axis=0), (1, -1))
if self.sparse_support:
mean_support = 1.0/tf.cast(n_v, tf.float32)*tf.sparse_reduce_sum(self.support, axis=0)
else:
mean_support = 1.0/tf.cast(n_v, tf.float32)*tf.reduce_sum(self.support, axis=0)
diff = tf.reshape(mean_support, (-1,1))*pre_sup - mean_output
reg = 1.0 / tf.cast(n_u * self.output_dim, tf.float32) * tf.reduce_sum(diff * diff)
# bias
if self.bias:
output += self.vars['bias']
return self.act(output), reg
class GraphSampleConvolutionSkip(Layer):
"""Graph convolution layer."""
def __init__(self, input_dim, output_dim, placeholders,
support, #prob, self_features,
name = 'con_reg',
sparse_support=True,
sparse_inputs=False, act=tf.nn.relu, bias=False,
dropout=False, featureless=False, **kwargs):
super(GraphSampleConvolutionSkip, self).__init__(**kwargs)
if dropout:
self.dropout = placeholders['dropout']
else:
self.dropout = 0.
self.name = name
self.act = act
self.support = support
self.sparse_support = sparse_support
self.sparse_inputs = sparse_inputs
self.featureless = featureless
self.bias = bias
self.output_dim = output_dim
# helper variable for sparse dropout
self.num_features_nonzero = placeholders['num_features_nonzero']
with tf.variable_scope('con_vars', reuse=True):
w_12 = tf.get_variable('weights', [input_dim, FLAGS.hidden1])
with tf.variable_scope('con_reg_vars', reuse=True):
w_23 = tf.get_variable('weights', [FLAGS.hidden1, output_dim])
self.vars['weights'] = tf.matmul(w_12, w_23)
if self.bias:
self.vars['bias'] = zeros([output_dim], name='bias')
if self.logging:
self._log_vars()
def _call(self, inputs):
x = inputs
# dropout
if self.sparse_inputs:
x = sparse_dropout(x, 1-self.dropout, self.num_features_nonzero)
else:
x = tf.nn.dropout(x, 1-self.dropout)
if not self.featureless:
pre_sup = dot(x, self.vars['weights'], sparse=self.sparse_inputs)
else:
pre_sup = self.vars['weights']
output = dot(self.support, pre_sup, sparse=self.sparse_support)
# bias
if self.bias:
output += self.vars['bias']
return self.act(output), 0