def model_fn_dense(features, labels, mode, params):
dense_feature, sparse_feature = build_features()
dense_input = tf.feature_column.input_layer(features, dense_feature)
sparse_input = tf.feature_column.input_layer(features, sparse_feature)
# Linear part
with tf.variable_scope('Linear_component'):
linear_output = tf.layers.dense(sparse_input, units=1)
add_layer_summary('linear_output', linear_output)
# Deep part
dense_output = stack_dense_layer(dense_input,
params['hidden_units'],
params['dropout_rate'],
params['batch_norm'],
mode,
add_summary=True)
# CIN part
emb_size = dense_feature[0].variable_shape.as_list()[-1]
field_size = len(dense_feature)
embedding_matrix = tf.reshape(
dense_input,
[-1, field_size, emb_size]) # batch * field_size * emb_size
add_layer_summary('embedding_matrix', embedding_matrix)
cin_output = cin_layer(embedding_matrix, params['cin_layer_size'],
emb_size, field_size)
with tf.variable_scope('output'):
y = tf.concat([dense_output, cin_output, linear_output], axis=1)
y = tf.layers.dense(y, units=1)
add_layer_summary('output', y)
return y
def model_fn_dense(features, labels, mode, params):
dense_feature, sparse_feature = build_features()
dense = tf.feature_column.input_layer(features, dense_feature)
sparse = tf.feature_column.input_layer(features, sparse_feature)
with tf.variable_scope('FM_component'):
with tf.variable_scope('Linear'):
linear_output = tf.layers.dense(sparse, units=1)
add_layer_summary('linear_output', linear_output)
with tf.variable_scope('second_order'):
# reshape (batch_size, n_feature * emb_size) -> (batch_size, n_feature, emb_size)
emb_size = dense_feature[0].variable_shape.as_list()[
0] # all feature has same emb dimension
embedding_matrix = tf.reshape(dense,
(-1, len(dense_feature), emb_size))
add_layer_summary('embedding_matrix', embedding_matrix)
# Compared to FM embedding here is flatten(x * v) not v
sum_square = tf.pow(tf.reduce_sum(embedding_matrix, axis=1), 2)
square_sum = tf.reduce_sum(tf.pow(embedding_matrix, 2), axis=1)
fm_output = tf.reduce_sum(tf.subtract(sum_square, square_sum) *
0.5,
axis=1,
keepdims=True)
add_layer_summary('fm_output', fm_output)
with tf.variable_scope('Deep_component'):
for i, unit in enumerate(params['hidden_units']):
dense = tf.layers.dense(dense,
units=unit,
activation='relu',
name='dense{}'.format(i))
dense = tf.layers.batch_normalization(
dense,
center=True,
scale=True,
trainable=True,
training=(mode == tf.estimator.ModeKeys.TRAIN))
dense = tf.layers.dropout(
dense,
rate=params['dropout_rate'],
training=(mode == tf.estimator.ModeKeys.TRAIN))
add_layer_summary(dense.name, dense)
with tf.variable_scope('output'):
y = dense + fm_output + linear_output
add_layer_summary('output', y)
return y
def model_fn_dense(features, labels, mode, params):
dense_feature, sparse_feature = build_features()
dense_input = tf.feature_column.input_layer(features, dense_feature)
sparse_input = tf.feature_column.input_layer(features, sparse_feature)
# Linear part
with tf.variable_scope('Linear_component'):
linear_output = tf.layers.dense(sparse_input, units=1)
add_layer_summary('linear_output', linear_output)
field_size = len(dense_feature)
emb_size = dense_feature[0].variable_shape.as_list()[-1]
embedding_matrix = tf.reshape(dense_input, [-1, field_size, emb_size])
# SENET_layer to get new embedding matrix
senet_embedding_matrix = SENET_layer(embedding_matrix,
field_size,
emb_size,
pool_op=params['pool_op'],
ratio=params['senet_ratio'])
# combination layer & BI_interaction
BI_org = Bilinear_layer(embedding_matrix,
field_size,
emb_size,
type=params['model_type'],
name='org')
BI_senet = Bilinear_layer(senet_embedding_matrix,
field_size,
emb_size,
type=params['model_type'],
name='senet')
combination_layer = tf.concat([BI_org, BI_senet], axis=1)
# Deep part
dense_output = stack_dense_layer(combination_layer,
params['hidden_units'],
params['dropout_rate'],
params['batch_norm'],
mode,
add_summary=True)
with tf.variable_scope('output'):
y = dense_output + linear_output
add_layer_summary('output', y)
return y