def model(self):
"""
To generate a model.
:return: The estimation of race finish time of a single horse in centi second
"""
with tf.variable_scope(name_or_scope='race_predictor'):
fc_0 = fc_layer(tf.layers.flatten(self._input),
512,
training=self.training,
name='fc_0')
bi_0 = bilinear_layer(fc_0,
512,
training=self.training,
name='bi_0')
bi_1 = bilinear_layer(bi_0,
512,
training=self.training,
name='bi_1')
velocity_output = tf.layers.dense(bi_1,
units=1,
activation=None,
use_bias=False,
name='velocity_output')
alpha_output = tf.layers.dense(bi_1,
units=1,
activation=None,
use_bias=False,
name='alpha_output')
return velocity_output, alpha_output
def model(self):
"""
To generate a model.
:return: The estimation of race finish time of a single horse in centi second
"""
with tf.variable_scope(name_or_scope='race_predictor'):
fc_0 = fc_layer(tf.layers.flatten(self._input),
256,
training=self.training,
name='fc_0')
bi_0 = bilinear_layer(fc_0,
256,
training=self.training,
name='bi_0')
bi_1 = bilinear_layer(bi_0,
256,
training=self.training,
name='bi_1')
fc_1 = fc_layer(bi_1, 128, training=self.training, name='fc_1')
win_output = tf.nn.softmax(tf.layers.dense(fc_1,
units=14,
activation=None),
name='win_output')
return win_output
def model(self):
with tf.variable_scope(name_or_scope='game_rating'):
# pre-processing layers
semantic_fc_0 = fc_layer(tf.layers.flatten(self._semantic),
512,
training=self.training,
name='semantic_fc_0')
features_fc_0 = fc_layer(self._features,
128,
training=self.training,
name='features_fc_0')
# bi-linear layers
semantic_bi_0 = bilinear_layer(semantic_fc_0,
512,
training=self.training,
name='semantic_bi_0')
features_bi_0 = bilinear_layer(features_fc_0,
128,
training=self.training,
name='features_bi_0')
# post-processing layers
semantic_fc_1 = fc_layer(semantic_bi_0,
128,
training=self.training,
name='semantic_fc_1')
features_fc_1 = fc_layer(features_bi_0,
128,
training=self.training,
name='features_fc_1')
# merge user features and movie features by an add_n operation, following by a fully-connected layer
merge = tf.multiply(semantic_fc_1, features_fc_1, name='merge')
# merge = tf.add_n([semantic_fc_1, features_fc_1], name='merge')
# output layer
playtime_output = fc_layer(merge,
1,
training=self.training,
name='playtime_output')
# return
return playtime_output
def naive_model(self):
with tf.variable_scope(name_or_scope='game_rating'):
# pre-processing layers
fc_0 = fc_layer(self._features,
128,
training=self.training,
name='fc_0')
# bi-linear layers
bi_0 = bilinear_layer(fc_0,
128,
training=self.training,
name='bi_0')
# output layer
playtime_output = fc_layer(bi_0,
1,
training=self.training,
name='playtime_output')
# return
return playtime_output