def model_fn(labels, features, mode, params):
tf.set_random_seed(2019)
cont_feats = features["cont_feats"]
cate_feats = features["cate_feats"]
vector_feats = features["vector_feats"]
single_cate_feats = cate_feats[:, 0:params.cate_field_size]
multi_cate_feats = cate_feats[:, params.cate_field_size:params.cate_field_size + params.multi_feats_size]
attention_cate_feats = cate_feats[:, params.cate_field_size + params.multi_feats_size:]
# init_embedding
feats_emb = my_layer.emb_init(name='feats_emb', feat_num=params.cate_feats_size, embedding_size=params.embedding_size)
# single_category -> embedding
single_cate_emb = tf.nn.embedding_lookup(feats_emb, ids=single_cate_feats)
single_cate_emb = tf.reshape(single_cate_emb, shape=[-1, params.cate_field_size * params.embedding_size])
# attention
attention_emb = attention_alg(params, feats_emb, multi_cate_feats, single_cate_feats, attention_cate_feats)
# multi_category -> embedding
multi_cate_emb = my_layer.multi_cate_emb(params.multi_feats_range, feats_emb, multi_cate_feats)
# deep input dense
if len(params.multi_feats_range) > 0:
dense = tf.concat([cont_feats, vector_feats, single_cate_emb, multi_cate_emb, attention_emb], axis=1, name='dense_vector')
else:
dense = tf.concat([cont_feats, vector_feats, single_cate_emb, attention_emb], axis=1, name='dense_vector')
# deep
len_layers = len(params.hidden_units)
for i in range(0, len_layers):
dense = tf.layers.dense(inputs=dense, units=params.hidden_units[i], activation=tf.nn.relu)
out = tf.layers.dense(inputs=dense, units=1)
score = tf.identity(tf.nn.sigmoid(out), name='score')
model_estimator_spec = op.model_optimizer(params, mode, labels, score)
return model_estimator_spec
def model_fn(labels, features, mode, params):
use_deep = True
use_fm = True
tf.set_random_seed(2019)
cont_feats = features["cont_feats"]
cate_feats = features["cate_feats"]
vector_feats = features["vector_feats"]
single_cate_feats = cate_feats[:, 0:params.cate_field_size]
multi_cate_feats = cate_feats[:, params.cate_field_size:]
cont_feats_index = tf.Variable([[i for i in range(params.cont_field_size)]], trainable=False, dtype=tf.int64, name="cont_feats_index")
index_max_size = params.cont_field_size + params.cate_feats_size
fm_first_order_emb = my_layer.emb_init(name='fm_first_order_emb', feat_num=index_max_size, embedding_size=1)
feats_emb = my_layer.emb_init(name='feats_emb', feat_num=index_max_size, embedding_size=params.embedding_size)
with tf.name_scope('fm_part'):
input_field_size = params.cont_field_size + params.cate_field_size + len(params.multi_feats_range)
cont_index_add = tf.add(cont_feats_index, params.cate_feats_size)
# FM_first_order [?, input_field_size]
# cont
first_cont_emb = tf.nn.embedding_lookup(fm_first_order_emb, ids=cont_index_add)
first_cont_emb = tf.reshape(first_cont_emb, shape=[-1, params.cont_field_size])
first_cont_mul = tf.multiply(first_cont_emb, cont_feats)
# single_cate
first_single_cate_emb = tf.nn.embedding_lookup(fm_first_order_emb, ids=single_cate_feats)
first_single_cate_emb = tf.reshape(first_single_cate_emb, shape=[-1, params.cate_field_size])
# multi_cate
first_multi_cate_emb = my_layer.multi_cate_emb(params.multi_feats_range, fm_first_order_emb, multi_cate_feats)
# concat cont & single_cate & multi_cate
first_order_emb = tf.concat([first_cont_mul, first_single_cate_emb, first_multi_cate_emb], axis=1)
first_order = tf.nn.dropout(first_order_emb, params.dropout_keep_fm[0])
# FM_second_order [?, embedding_size]
# cont
second_cont_emb = tf.nn.embedding_lookup(feats_emb, ids=cont_index_add)
second_cont_value = tf.reshape(cont_feats, shape=[-1, params.cont_field_size, 1])
second_cont_emb = tf.multiply(second_cont_emb, second_cont_value)
# single_cate
second_single_cate_emb = tf.nn.embedding_lookup(feats_emb, ids=single_cate_feats)
# multi_cate
second_multi_cate_emb = my_layer.multi_cate_emb(params.multi_feats_range, feats_emb, multi_cate_feats)
second_multi_cate_emb = tf.reshape(second_multi_cate_emb,
shape=[-1, len(params.multi_feats_range), params.embedding_size])
# concat cont & single_cate & multi_cate
second_order_emb = tf.concat([second_cont_emb, second_single_cate_emb, second_multi_cate_emb], axis=1)
sum_emb = tf.reduce_sum(second_order_emb, 1)
sum_square_emb = tf.square(sum_emb)
square_emb = tf.square(second_order_emb)
square_sum_emb = tf.reduce_sum(square_emb, 1)
second_order = 0.5 * tf.subtract(sum_square_emb, square_sum_emb)
second_order = tf.nn.dropout(second_order, params.dropout_keep_fm[1])
# FM_res [?, self.input_field_size + embedding_size]
fm_res = tf.concat([first_order, second_order], axis=1)
with tf.name_scope('deep_part'):
# single_cate
single_cate_emb = tf.nn.embedding_lookup(feats_emb, ids=single_cate_feats)
single_cate_emb = tf.reshape(single_cate_emb, shape=[-1, params.cate_field_size * params.embedding_size])
# multi_cate
multi_cate_emb = my_layer.multi_cate_emb(params.multi_feats_range, feats_emb, multi_cate_feats)
deep_res = tf.concat([cont_feats, vector_feats, single_cate_emb, multi_cate_emb], axis=1, name='dense_vector')
# deep
len_layers = len(params.hidden_units)
for i in range(0, len_layers):
deep_res = tf.layers.dense(inputs=deep_res, units=params.hidden_units[i], activation=tf.nn.relu)
with tf.name_scope('deep_fm'):
if use_fm and use_deep:
feats_input = tf.concat([fm_res, deep_res], axis=1)
feats_input_size = input_field_size + params.embedding_size + params.hidden_units[-1]
elif use_fm:
feats_input = fm_res
feats_input_size = input_field_size + params.embedding_size
elif use_deep:
feats_input = deep_res
feats_input_size = params.hidden_units[-1]
glorot = np.sqrt(2.0 / (feats_input_size + 1))
deep_fm_weight = tf.Variable(
np.random.normal(loc=0, scale=glorot, size=(feats_input_size, 1)), dtype=np.float32)
deep_fm_bias = tf.Variable(tf.random_normal([1]))
out = tf.add(tf.matmul(feats_input, deep_fm_weight), deep_fm_bias)
score = tf.identity(tf.nn.sigmoid(out), name='score')
model_estimator_spec = op.model_optimizer(params, mode, labels, score)
return model_estimator_spec
def model_fn(labels, features, mode, params):
tf.set_random_seed(2019)
cont_feats = features["cont_feats"]
cate_feats = features["cate_feats"]
vector_feats = features["vector_feats"]
single_cate_feats = cate_feats[:, 0:params.cate_field_size]
multi_cate_feats = cate_feats[:, params.cate_field_size:]
cont_feats_index = tf.Variable([[i
for i in range(params.cont_field_size)]],
trainable=False,
dtype=tf.int64,
name="cont_feats_index")
cont_index_add = tf.add(cont_feats_index, params.cate_feats_size)
index_max_size = params.cont_field_size + params.cate_feats_size
feats_emb = my_layer.emb_init(name='feats_emb',
feat_num=index_max_size,
embedding_size=params.embedding_size)
# cont_feats -> Embedding
with tf.name_scope("cont_feat_emb"):
ori_cont_emb = tf.nn.embedding_lookup(feats_emb,
ids=cont_index_add,
name="ori_cont_emb")
cont_value = tf.reshape(cont_feats,
shape=[-1, params.cont_field_size, 1],
name="cont_value")
cont_emb = tf.multiply(ori_cont_emb, cont_value)
cont_emb = tf.reshape(
cont_emb,
shape=[-1, params.cont_field_size * params.embedding_size],
name="cont_emb")
# single_category -> Embedding
with tf.name_scope("single_cate_emb"):
cate_emb = tf.nn.embedding_lookup(feats_emb, ids=single_cate_feats)
cate_emb = tf.reshape(
cate_emb,
shape=[-1, params.cate_field_size * params.embedding_size])
# multi_category -> Embedding
with tf.name_scope("multi_cate_emb"):
multi_cate_emb = my_layer.multi_cate_emb(params.multi_feats_range,
feats_emb, multi_cate_feats)
# deep input dense
dense_input = tf.concat([cont_emb, vector_feats, cate_emb, multi_cate_emb],
axis=1,
name='dense_vector')
# experts
experts_weight = tf.get_variable(
name='experts_weight',
dtype=tf.float32,
shape=(dense_input.get_shape()[1], params.experts_units,
params.experts_num),
initializer=tf.contrib.layers.xavier_initializer())
experts_bias = tf.get_variable(
name='expert_bias',
dtype=tf.float32,
shape=(params.experts_units, params.experts_num),
initializer=tf.contrib.layers.xavier_initializer())
# gates
gate1_weight = tf.get_variable(
name='gate1_weight',
dtype=tf.float32,
shape=(dense_input.get_shape()[1], params.experts_num),
initializer=tf.contrib.layers.xavier_initializer())
gate1_bias = tf.get_variable(
name='gate1_bias',
dtype=tf.float32,
shape=(params.experts_num, ),
initializer=tf.contrib.layers.xavier_initializer())
gate2_weight = tf.get_variable(
name='gate2_weight',
dtype=tf.float32,
shape=(dense_input.get_shape()[1], params.experts_num),
initializer=tf.contrib.layers.xavier_initializer())
gate2_bias = tf.get_variable(
name='gate2_bias',
dtype=tf.float32,
shape=(params.experts_num, ),
initializer=tf.contrib.layers.xavier_initializer())
# f_{i}(x) = activation(W_{i} * x + b), where activation is ReLU according to the paper
experts_output = tf.tensordot(dense_input, experts_weight, axes=1)
use_experts_bias = True
if use_experts_bias:
experts_output = tf.add(experts_output, experts_bias)
experts_output = tf.nn.relu(experts_output)
# g^{k}(x) = activation(W_{gk} * x + b), where activation is softmax according to the paper
gate1_output = tf.matmul(dense_input, gate1_weight)
gate2_output = tf.matmul(dense_input, gate2_weight)
user_gate_bias = True
if user_gate_bias:
gate1_output = tf.add(gate1_output, gate1_bias)
gate2_output = tf.add(gate2_output, gate2_bias)
gate1_output = tf.nn.softmax(gate1_output)
gate2_output = tf.nn.softmax(gate2_output)
# f^{k}(x) = sum_{i=1}^{n}(g^{k}(x)_{i} * f_{i}(x))
label1_input = tf.multiply(experts_output,
tf.expand_dims(gate1_output, axis=1))
label1_input = tf.reduce_sum(label1_input, axis=2)
label1_input = tf.reshape(label1_input, [-1, params.experts_units])
label2_input = tf.multiply(experts_output,
tf.expand_dims(gate2_output, axis=1))
label2_input = tf.reduce_sum(label2_input, axis=2)
label2_input = tf.reshape(label2_input, [-1, params.experts_units])
len_layers = len(params.hidden_units)
with tf.variable_scope('ctr_deep'):
dense_ctr = tf.layers.dense(inputs=label1_input,
units=params.hidden_units[0],
activation=tf.nn.relu)
for i in range(1, len_layers):
dense_ctr = tf.layers.dense(inputs=dense_ctr,
units=params.hidden_units[i],
activation=tf.nn.relu)
ctr_out = tf.layers.dense(inputs=dense_ctr, units=1)
with tf.variable_scope('cvr_deep'):
dense_cvr = tf.layers.dense(inputs=label2_input,
units=params.hidden_units[0],
activation=tf.nn.relu)
for i in range(1, len_layers):
dense_cvr = tf.layers.dense(inputs=dense_cvr,
units=params.hidden_units[i],
activation=tf.nn.relu)
cvr_out = tf.layers.dense(inputs=dense_cvr, units=1)
ctr_score = tf.identity(tf.nn.sigmoid(ctr_out), name='ctr_score')
cvr_score = tf.identity(tf.nn.sigmoid(cvr_out), name='cvr_score')
ctcvr_score = ctr_score * cvr_score
ctcvr_score = tf.identity(ctcvr_score, name='ctcvr_score')
score = tf.add(ctr_score * params.label1_weight,
cvr_score * params.label2_weight)
score = tf.identity(score, name='score')
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=score)
else:
ctr_labels = tf.identity(labels['label'], name='ctr_labels')
ctcvr_labels = tf.identity(labels['label2'], name='ctcvr_labels')
ctr_auc = tf.metrics.auc(labels=ctr_labels,
predictions=ctr_score,
name='auc')
ctcvr_auc = tf.metrics.auc(labels=ctcvr_labels,
predictions=ctcvr_score,
name='auc')
metrics = {'ctr_auc': ctr_auc, 'ctcvr_auc': ctcvr_auc}
# ctr_loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(labels=ctr_labels, logits=ctr_out))
ctr_loss = tf.reduce_mean(
tf.losses.log_loss(labels=ctr_labels, predictions=ctr_score))
ctcvr_loss = tf.reduce_mean(
tf.losses.log_loss(labels=ctcvr_labels, predictions=ctcvr_score))
loss = ctr_loss + ctcvr_loss
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.AdamOptimizer(params.learning_rate)
train_op = optimizer.minimize(
loss=loss, global_step=tf.train.get_global_step())
else:
train_op = None
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
eval_metric_ops=metrics,
train_op=train_op)
def model_fn(labels, features, mode, params):
tf.set_random_seed(2019)
cont_feats = features["cont_feats"]
cate_feats = features["cate_feats"]
vector_feats = features["vector_feats"]
single_cate_feats = cate_feats[:, 0:params.cate_field_size]
multi_cate_feats = cate_feats[:, params.cate_field_size:]
feats_emb = my_layer.emb_init(name='feats_emb', feat_num=params.cate_feats_size,
embedding_size=params.embedding_size)
# single_category -> Embedding
cate_emb = tf.nn.embedding_lookup(feats_emb, ids=single_cate_feats)
cate_emb = tf.reshape(cate_emb, shape=[-1, params.cate_field_size * params.embedding_size])
# multi_category -> Embedding
multi_cate_emb = my_layer.multi_cate_emb(params.multi_feats_range, feats_emb, multi_cate_feats)
# deep input dense
dense_input = tf.concat([cont_feats, vector_feats, cate_emb, multi_cate_emb], axis=1, name='dense_vector')
len_layers = len(params.hidden_units)
with tf.variable_scope('ctr_deep'):
dense_ctr = tf.layers.dense(inputs=dense_input, units=params.hidden_units[0], activation=tf.nn.relu)
for i in range(1, len_layers):
dense_ctr = tf.layers.dense(inputs=dense_ctr, units=params.hidden_units[i], activation=tf.nn.relu)
ctr_out = tf.layers.dense(inputs=dense_ctr, units=1)
with tf.variable_scope('cvr_deep'):
dense_cvr = tf.layers.dense(inputs=dense_input, units=params.hidden_units[0], activation=tf.nn.relu)
for i in range(1, len_layers):
dense_cvr = tf.layers.dense(inputs=dense_cvr, units=params.hidden_units[i], activation=tf.nn.relu)
cvr_out = tf.layers.dense(inputs=dense_cvr, units=1)
ctr_score = tf.identity(tf.nn.sigmoid(ctr_out), name='ctr_score')
cvr_score = tf.identity(tf.nn.sigmoid(cvr_out), name='cvr_score')
ctcvr_score = ctr_score * cvr_score
ctcvr_score = tf.identity(ctcvr_score, name='ctcvr_score')
ctr_pow = 0.5
cvr_pow = 1
score = tf.multiply(tf.pow(ctr_score, ctr_pow), tf.pow(cvr_score, cvr_pow))
score = tf.identity(score, name='score')
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=score)
else:
ctr_labels = tf.identity(labels['label'], name='ctr_labels')
ctcvr_labels = tf.identity(labels['label2'], name='ctcvr_labels')
ctr_auc = tf.metrics.auc(labels=ctr_labels, predictions=ctr_score, name='auc')
ctcvr_auc = tf.metrics.auc(labels=ctcvr_labels, predictions=ctcvr_score, name='auc')
metrics = {
'ctr_auc': ctr_auc,
'ctcvr_auc': ctcvr_auc
}
# ctr_loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(labels=ctr_labels, logits=ctr_out))
ctr_loss = tf.reduce_mean(tf.losses.log_loss(labels=ctr_labels, predictions=ctr_score))
ctcvr_loss = tf.reduce_mean(tf.losses.log_loss(labels=ctcvr_labels, predictions=ctcvr_score))
loss = ctr_loss + ctcvr_loss
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.AdamOptimizer(params.learning_rate)
train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())
else:
train_op = None
return tf.estimator.EstimatorSpec(
mode=mode,
loss=loss,
eval_metric_ops=metrics,
train_op=train_op)
def model_fn(labels, features, mode, params):
tf.set_random_seed(2019)
cont_feats = features["cont_feats"]
cate_feats = features["cate_feats"]
vector_feats = features["vector_feats"]
single_cate_feats = cate_feats[:, 0:params.cate_field_size]
multi_cate_feats = cate_feats[:, params.cate_field_size:]
cont_feats_index = tf.Variable([[i
for i in range(params.cont_field_size)]],
trainable=False,
dtype=tf.int64,
name="cont_feats_index")
cont_index_add = tf.add(cont_feats_index, params.cate_feats_size)
index_max_size = params.cont_field_size + params.cate_feats_size
feats_emb = my_layer.emb_init(name='feats_emb',
feat_num=index_max_size,
embedding_size=params.embedding_size)
# cont_feats -> Embedding
with tf.name_scope("cont_feat_emb"):
ori_cont_emb = tf.nn.embedding_lookup(feats_emb,
ids=cont_index_add,
name="ori_cont_emb")
cont_value = tf.reshape(cont_feats,
shape=[-1, params.cont_field_size, 1],
name="cont_value")
cont_emb = tf.multiply(ori_cont_emb, cont_value)
autoint_cont = cont_emb
cont_emb = tf.reshape(
cont_emb,
shape=[-1, params.cont_field_size * params.embedding_size],
name="cont_emb")
# single_category -> Embedding
with tf.name_scope("single_cate_emb"):
cate_emb = tf.nn.embedding_lookup(feats_emb, ids=single_cate_feats)
autoint_cate = cate_emb
cate_emb = tf.reshape(
cate_emb,
shape=[-1, params.cate_field_size * params.embedding_size])
# multi_category -> Embedding
with tf.name_scope("multi_cate_emb"):
multi_cate_emb = my_layer.multi_cate_emb(params.multi_feats_range,
feats_emb, multi_cate_feats)
autoint_multi_cate = tf.reshape(
multi_cate_emb,
shape=[-1,
len(params.multi_feats_range), params.embedding_size])
# AutoInteracting
with tf.name_scope("AutoInt"):
autoint_input = tf.concat(
[autoint_cont, autoint_cate, autoint_multi_cate], axis=1)
for i in range(params.autoint_layer_num):
autoint_input = my_layer.InteractingLayer(
num_layer=i,
att_emb_size=params.autoint_emb_size,
seed=2020,
head_num=params.autoint_head_num,
use_res=params.autoint_use_res)(autoint_input)
autoint_output = tf.layers.Flatten()(autoint_input)
# deep input dense
dense = tf.concat([cont_emb, vector_feats, cate_emb, multi_cate_emb],
axis=1,
name='dense_vector')
# deep
len_layers = len(params.hidden_units)
for i in range(0, len_layers):
dense = tf.layers.dense(inputs=dense,
units=params.hidden_units[i],
activation=tf.nn.relu)
final_input = tf.concat([autoint_output, dense], axis=1)
out = tf.layers.dense(inputs=final_input, units=1)
score = tf.identity(tf.nn.sigmoid(out), name='score')
model_estimator_spec = op.model_optimizer(params, mode, labels, score)
return model_estimator_spec
def model_fn(labels, features, mode, params):
tf.set_random_seed(2019)
cont_feats = features["cont_feats"]
cate_feats = features["cate_feats"]
vector_feats = features["vector_feats"]
single_cate_feats = cate_feats[:, 0:params.cate_field_size]
multi_cate_feats = cate_feats[:, params.cate_field_size:]
cont_feats_index = tf.Variable([[i
for i in range(params.cont_field_size)]],
trainable=False,
dtype=tf.int64,
name="cont_feats_index")
cont_index_add = tf.add(cont_feats_index, params.cate_feats_size)
index_max_size = params.cont_field_size + params.cate_feats_size
feats_emb = my_layer.emb_init(name='feats_emb',
feat_num=index_max_size,
embedding_size=params.embedding_size)
# cont_feats -> Embedding
ori_cont_emb = tf.nn.embedding_lookup(feats_emb,
ids=cont_index_add,
name="ori_cont_emb")
cont_value = tf.reshape(cont_feats,
shape=[-1, params.cont_field_size, 1],
name="cont_value")
cont_emb = tf.multiply(ori_cont_emb, cont_value)
cont_emb = tf.reshape(
cont_emb,
shape=[-1, params.cont_field_size * params.embedding_size],
name="cont_emb")
# print(ori_cont_emb)
# print(cont_value)
# print(cont_emb)
# single_category -> Embedding
cate_emb = tf.nn.embedding_lookup(feats_emb, ids=single_cate_feats)
cate_emb = tf.reshape(
cate_emb, shape=[-1, params.cate_field_size * params.embedding_size])
# print(cate_emb)
# multi_category -> Embedding
multi_cate_emb = my_layer.multi_cate_emb(params.multi_feats_range,
feats_emb, multi_cate_feats)
# deep input dense
dense = tf.concat([cont_emb, vector_feats, cate_emb, multi_cate_emb],
axis=1,
name='dense_vector')
# deep
len_layers = len(params.hidden_units)
for i in range(0, len_layers):
dense = tf.layers.dense(inputs=dense,
units=params.hidden_units[i],
activation=tf.nn.relu)
out = tf.layers.dense(inputs=dense, units=1)
score = tf.identity(tf.nn.sigmoid(out), name='score')
model_estimator_spec = op.model_optimizer(params, mode, labels, score)
return model_estimator_spec
