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add masknet variable name prefix #478

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add masknet variable name prefix #478

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20 changes: 11 additions & 9 deletions easy_rec/python/layers/keras/mask_net.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,7 @@ def __init__(self, params, name='mask_block', reuse=None, **kwargs):
self._projection_dim = params.get_or_default('projection_dim', None)
self.reuse = reuse
self.final_relu = Activation('relu', name='relu')
self._name = name

def build(self, input_shape):
assert len(input_shape) >= 2, 'MaskBlock must has at least two inputs'
Expand All @@ -49,30 +50,30 @@ def build(self, input_shape):
activation='relu',
kernel_initializer='he_uniform',
kernel_regularizer=self.l2_reg,
name='aggregation')
name='%s/aggregation' % self._name)
self.weight_layer = Dense(input_dim, name='weights')
if self._projection_dim is not None:
self.project_layer = Dense(
self._projection_dim,
kernel_regularizer=self.l2_reg,
use_bias=False,
name='project')
name='%s/project' % self._name)
if self.config.input_layer_norm:
# 推荐在调用MaskBlock之前做好 layer norm，否则每一次调用都需要对input做ln
if tf.__version__ >= '2.0':
self.input_layer_norm = tf.keras.layers.LayerNormalization(
name='input_ln')
name='%s/input_ln' % self._name)
else:
self.input_layer_norm = LayerNormalization(name='input_ln')
self.input_layer_norm = LayerNormalization(name='%s/input_ln' % self._name)

if self.config.HasField('output_size'):
self.output_layer = Dense(
self.config.output_size, use_bias=False, name='output')
self.config.output_size, use_bias=False, name='%s/output' % self._name)
if tf.__version__ >= '2.0':
self.output_layer_norm = tf.keras.layers.LayerNormalization(
name='output_ln')
else:
self.output_layer_norm = LayerNormalization(name='output_ln')
self.output_layer_norm = LayerNormalization(name='%s/output_ln' % self._name)

def call(self, inputs, **kwargs):
net, mask_input = inputs
Expand Down Expand Up @@ -104,6 +105,7 @@ class MaskNet(Layer):

def __init__(self, params, name='mask_net', reuse=None, **kwargs):
super(MaskNet, self).__init__(name, **kwargs)
self._name = name
self.reuse = reuse
self.params = params
self.config = params.get_pb_config()
Expand All @@ -118,15 +120,15 @@ def __init__(self, params, name='mask_net', reuse=None, **kwargs):
for i, block_conf in enumerate(self.config.mask_blocks):
params = Parameter.make_from_pb(block_conf)
params.l2_regularizer = self.params.l2_regularizer
mask_layer = MaskBlock(params, name='block_%d' % i, reuse=self.reuse)
mask_layer = MaskBlock(params, name='%s/block_%d' % (self._name, i), reuse=self.reuse)
self.mask_layers.append(mask_layer)

if self.config.input_layer_norm:
if tf.__version__ >= '2.0':
self.input_layer_norm = tf.keras.layers.LayerNormalization(
name='input_ln')
name='%s/input_ln' % self._name)
else:
self.input_layer_norm = LayerNormalization(name='input_ln')
self.input_layer_norm = LayerNormalization(name='%s/input_ln' % self._name)

def call(self, inputs, training=None, **kwargs):
if self.config.input_layer_norm:
Expand Down
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