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layers.py
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layers.py
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import tensorflow as tf
from inits import zeros
import configure as conf
_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]
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()
"""
def __init__(self, **kwargs):
allowed_kwargs = {'name', 'logging', 'model_size'}
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):
outputs = self._call(inputs)
return outputs
class Dense(Layer):
"""Dense layer."""
def __init__(self, input_dim, output_dim, dropout=0.,
act=tf.nn.relu, placeholders=None, bias=True, featureless=False,
sparse_inputs=False, name='', **kwargs):
super(Dense, self).__init__(**kwargs)
self.dropout = dropout
self.name = name
self.act = act
self.featureless = featureless
self.bias = bias
self.input_dim = input_dim
self.output_dim = output_dim
# helper variable for sparse dropout
self.sparse_inputs = sparse_inputs
if sparse_inputs:
self.num_features_nonzero = placeholders['num_features_nonzero']
with tf.variable_scope(self.name + '_vars'):
self.vars['weights'] = tf.get_variable('weights', shape=(input_dim, output_dim),
dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer(),
regularizer=tf.contrib.layers.l2_regularizer(conf.weight_decay))
if self.bias:
self.vars['bias'] = zeros([output_dim], name='bias')
def _call(self, inputs):
x = inputs
# x = tf.nn.dropout(x, self.dropout)
# transform
output = tf.matmul(x, self.vars['weights'])
# bias
if self.bias:
output += self.vars['bias']
return self.act(output)