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ffm-baseline
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test

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eda/esmm/Model_pipline/train.py
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......@@ -49,232 +49,13 @@ tf.app.flags.DEFINE_string("task_type", 'train', "task type {train, infer, eval,
tf.app.flags.DEFINE_boolean("clear_existing_model", False, "clear existing model or not")
#40362692,0,0,216:9342395:1.0 301:9351665:1.0 205:7702673:1.0 206:8317829:1.0 207:8967741:1.0 508:9356012:2.30259 210:9059239:1.0 210:9042796:1.0 210:9076972:1.0 210:9103884:1.0 210:9063064:1.0 127_14:3529789:2.3979 127_14:3806412:2.70805
# def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
# print('Parsing', filenames)
# def _parse_fn(record):
# features = {
# "y": tf.FixedLenFeature([], tf.float32),
# "z": tf.FixedLenFeature([], tf.float32),
# "ids": tf.FixedLenFeature([FLAGS.field_size], tf.int64),
# "app_list": tf.VarLenFeature(tf.int64),
# "level2_list": tf.VarLenFeature(tf.int64),
# "level3_list": tf.VarLenFeature(tf.int64),
# "tag1_list": tf.VarLenFeature(tf.int64),
# "tag2_list": tf.VarLenFeature(tf.int64),
# "tag3_list": tf.VarLenFeature(tf.int64),
# "tag4_list": tf.VarLenFeature(tf.int64),
# "tag5_list": tf.VarLenFeature(tf.int64),
# "tag6_list": tf.VarLenFeature(tf.int64),
# "tag7_list": tf.VarLenFeature(tf.int64),
# "number": tf.VarLenFeature(tf.int64),
# "uid": tf.VarLenFeature(tf.string),
# "city": tf.VarLenFeature(tf.string),
# "cid_id": tf.VarLenFeature(tf.string)
# }
# parsed = tf.parse_single_example(record, features)
# y = parsed.pop('y')
# z = parsed.pop('z')
# return parsed, {"y": y, "z": z}
#
# # Extract lines from input files using the Dataset API, can pass one filename or filename list
# # dataset = tf.data.TFRecordDataset(filenames).map(_parse_fn, num_parallel_calls=8).prefetch(500000) # multi-thread pre-process then prefetch
#
# # Randomizes input using a window of 256 elements (read into memory)
# # if perform_shuffle:
# # dataset = dataset.shuffle(buffer_size=256)
#
# # epochs from blending together.
# # dataset = dataset.repeat(num_epochs)
# # dataset = dataset.batch(batch_size) # Batch size to use
#
# files = tf.data.Dataset.list_files(filenames)
# dataset = files.apply(
# tf.data.experimental.parallel_interleave(
# lambda file: tf.data.TFRecordDataset(file),
# cycle_length=8
# )
# )
#
# dataset = dataset.apply(tf.data.experimental.map_and_batch(map_func=_parse_fn, batch_size=batch_size, num_parallel_calls=8))
# dataset = dataset.prefetch(10000)
#
#
# # dataset = dataset.padded_batch(batch_size, padded_shapes=({"feeds_ids": [None], "feeds_vals": [None], "title_ids": [None]}, [None])) #不定长补齐
# #return dataset.make_one_shot_iterator()
# iterator = dataset.make_one_shot_iterator()
# batch_features, batch_labels = iterator.get_next()
# #return tf.reshape(batch_ids,shape=[-1,field_size]), tf.reshape(batch_vals,shape=[-1,field_size]), batch_labels
# #print("-"*100)
# #print(batch_features,batch_labels)
# return batch_features, batch_labels
# def model_fn(features, labels, mode, params):
# """Bulid Model function f(x) for Estimator."""
# #------hyperparameters----
# field_size = params["field_size"]
# feature_size = params["feature_size"]
# embedding_size = params["embedding_size"]
# l2_reg = params["l2_reg"]
# learning_rate = params["learning_rate"]
# #optimizer = params["optimizer"]
# layers = list(map(int, params["deep_layers"].split(',')))
# dropout = list(map(float, params["dropout"].split(',')))
# ctr_task_wgt = params["ctr_task_wgt"]
# common_dims = field_size*embedding_size
#
# #------bulid weights------
# Feat_Emb = tf.get_variable(name='embeddings', shape=[feature_size, embedding_size], initializer=tf.glorot_normal_initializer())
#
# feat_ids = features['ids']
# app_list = features['app_list']
# level2_list = features['level2_list']
# level3_list = features['level3_list']
# tag1_list = features['tag1_list']
# tag2_list = features['tag2_list']
# tag3_list = features['tag3_list']
# tag4_list = features['tag4_list']
# tag5_list = features['tag5_list']
# tag6_list = features['tag6_list']
# tag7_list = features['tag7_list']
# number = features['number']
# uid = features['uid']
# city = features['city']
# cid_id = features['cid_id']
#
# if FLAGS.task_type != "infer":
# y = labels['y']
# z = labels['z']
#
# #------build f(x)------
# with tf.variable_scope("Shared-Embedding-layer"):
# embedding_id = tf.nn.embedding_lookup(Feat_Emb,feat_ids)
# app_id = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=app_list, sp_weights=None, combiner="sum")
# level2 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=level2_list, sp_weights=None, combiner="sum")
# level3 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=level3_list, sp_weights=None, combiner="sum")
# tag1 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag1_list, sp_weights=None, combiner="sum")
# tag2 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag2_list, sp_weights=None, combiner="sum")
# tag3 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag3_list, sp_weights=None, combiner="sum")
# tag4 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag4_list, sp_weights=None, combiner="sum")
# tag5 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag5_list, sp_weights=None, combiner="sum")
# tag6 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag6_list, sp_weights=None, combiner="sum")
# tag7 = tf.nn.embedding_lookup_sparse(Feat_Emb, sp_ids=tag7_list, sp_weights=None, combiner="sum")
#
# # x_concat = tf.reshape(embedding_id,shape=[-1, common_dims]) # None * (F * K)
# x_concat = tf.concat([tf.reshape(embedding_id, shape=[-1, common_dims]), app_id, level2, level3, tag1,
# tag2, tag3, tag4, tag5, tag6, tag7], axis=1)
#
# sample_id = tf.sparse.to_dense(number)
# uid = tf.sparse.to_dense(uid,default_value="")
# city = tf.sparse.to_dense(city,default_value="")
# cid_id = tf.sparse.to_dense(cid_id,default_value="")
#
# with tf.name_scope("CVR_Task"):
# if mode == tf.estimator.ModeKeys.TRAIN:
# train_phase = True
# else:
# train_phase = False
# x_cvr = x_concat
# for i in range(len(layers)):
# x_cvr = tf.contrib.layers.fully_connected(inputs=x_cvr, num_outputs=layers[i], \
# weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='cvr_mlp%d' % i)
#
# if FLAGS.batch_norm:
# x_cvr = batch_norm_layer(x_cvr, train_phase=train_phase, scope_bn='cvr_bn_%d' %i) #放在RELU之后 https://github.com/ducha-aiki/caffenet-benchmark/blob/master/batchnorm.md#bn----before-or-after-relu
# if mode == tf.estimator.ModeKeys.TRAIN:
# x_cvr = tf.nn.dropout(x_cvr, keep_prob=dropout[i]) #Apply Dropout after all BN layers and set dropout=0.8(drop_ratio=0.2)
#
# y_cvr = tf.contrib.layers.fully_connected(inputs=x_cvr, num_outputs=1, activation_fn=tf.identity, \
# weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='cvr_out')
# y_cvr = tf.reshape(y_cvr,shape=[-1])
#
# with tf.name_scope("CTR_Task"):
# if mode == tf.estimator.ModeKeys.TRAIN:
# train_phase = True
# else:
# train_phase = False
#
# x_ctr = x_concat
# for i in range(len(layers)):
# x_ctr = tf.contrib.layers.fully_connected(inputs=x_ctr, num_outputs=layers[i], \
# weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='ctr_mlp%d' % i)
#
# if FLAGS.batch_norm:
# x_ctr = batch_norm_layer(x_ctr, train_phase=train_phase, scope_bn='ctr_bn_%d' %i) #放在RELU之后 https://github.com/ducha-aiki/caffenet-benchmark/blob/master/batchnorm.md#bn----before-or-after-relu
# if mode == tf.estimator.ModeKeys.TRAIN:
# x_ctr = tf.nn.dropout(x_ctr, keep_prob=dropout[i]) #Apply Dropout after all BN layers and set dropout=0.8(drop_ratio=0.2)
#
# y_ctr = tf.contrib.layers.fully_connected(inputs=x_ctr, num_outputs=1, activation_fn=tf.identity, \
# weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='ctr_out')
# y_ctr = tf.reshape(y_ctr,shape=[-1])
#
# with tf.variable_scope("MTL-Layer"):
# pctr = tf.sigmoid(y_ctr)
# pcvr = tf.sigmoid(y_cvr)
# pctcvr = pctr*pcvr
#
#
# predictions={"pctcvr": pctcvr, "sample_id": sample_id, "uid":uid, "city":city, "cid_id":cid_id}
# export_outputs = {tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: tf.estimator.export.PredictOutput(predictions)}
# # Provide an estimator spec for `ModeKeys.PREDICT`
# if mode == tf.estimator.ModeKeys.PREDICT:
# return tf.estimator.EstimatorSpec(
# mode=mode,
# predictions=predictions,
# export_outputs=export_outputs)
#
# if FLAGS.task_type != "infer":
# #------bulid loss------
# ctr_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_ctr, labels=y))
# #cvr_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_ctcvr, labels=z))
# cvr_loss = tf.reduce_mean(tf.losses.log_loss(predictions=pctcvr, labels=z))
# loss = ctr_task_wgt * ctr_loss + (1 -ctr_task_wgt) * cvr_loss + l2_reg * tf.nn.l2_loss(Feat_Emb)
#
# tf.summary.scalar('ctr_loss', ctr_loss)
# tf.summary.scalar('cvr_loss', cvr_loss)
#
# # Provide an estimator spec for `ModeKeys.EVAL`
# eval_metric_ops = {
# # "CTR_AUC": tf.metrics.auc(y, pctr),
# #"CTR_F1": tf.contrib.metrics.f1_score(y,pctr),
# #"CTR_Precision": tf.metrics.precision(y,pctr),
# #"CTR_Recall": tf.metrics.recall(y,pctr),
# # "CVR_AUC": tf.metrics.auc(z, pcvr),
# "CTCVR_AUC": tf.metrics.auc(z, pctcvr)
# }
# if mode == tf.estimator.ModeKeys.EVAL:
# return tf.estimator.EstimatorSpec(
# mode=mode,
# predictions=predictions,
# loss=loss,
# eval_metric_ops=eval_metric_ops)
#
# #------bulid optimizer------
# if FLAGS.optimizer == 'Adam':
# optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-8)
# elif FLAGS.optimizer == 'Adagrad':
# optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate, initial_accumulator_value=1e-8)
# elif FLAGS.optimizer == 'Momentum':
# optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.95)
# elif FLAGS.optimizer == 'ftrl':
# optimizer = tf.train.FtrlOptimizer(learning_rate)
#
# train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
#
# # Provide an estimator spec for `ModeKeys.TRAIN` modes
# if mode == tf.estimator.ModeKeys.TRAIN:
# return tf.estimator.EstimatorSpec(
# mode=mode,
# predictions=predictions,
# loss=loss,
# train_op=train_op)
def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
print('Parsing', filenames)
def _parse_fn(record):
features = {
"y": tf.FixedLenFeature([], tf.float32),
"z": tf.FixedLenFeature([], tf.float32),
"ids": tf.FixedLenFeature([15], tf.int64),
"ids": tf.FixedLenFeature([FLAGS.field_size], tf.int64),
"app_list": tf.VarLenFeature(tf.int64),
"level2_list": tf.VarLenFeature(tf.int64),
"level3_list": tf.VarLenFeature(tf.int64),
......@@ -296,17 +77,29 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
return parsed, {"y": y, "z": z}
# Extract lines from input files using the Dataset API, can pass one filename or filename list
dataset = tf.data.TFRecordDataset(filenames).map(_parse_fn, num_parallel_calls=10).prefetch(500000) # multi-thread pre-process then prefetch
# dataset = tf.data.TFRecordDataset(filenames).map(_parse_fn, num_parallel_calls=8).prefetch(500000) # multi-thread pre-process then prefetch
# Randomizes input using a window of 256 elements (read into memory)
if perform_shuffle:
dataset = dataset.shuffle(buffer_size=256)
# if perform_shuffle:
# dataset = dataset.shuffle(buffer_size=256)
# epochs from blending together.
dataset = dataset.repeat(num_epochs)
dataset = dataset.batch(batch_size) # Batch size to use
# dataset = dataset.padded_batch(batch_size, padded_shapes=({"feeds_ids": [None], "feeds_vals": [None], "title_ids": [None]}, [None])) #不定长补齐
# dataset = dataset.repeat(num_epochs)
# dataset = dataset.batch(batch_size) # Batch size to use
files = tf.data.Dataset.list_files(filenames)
dataset = files.apply(
tf.data.experimental.parallel_interleave(
lambda file: tf.data.TFRecordDataset(file),
cycle_length=8
)
)
dataset = dataset.apply(tf.data.experimental.map_and_batch(map_func=_parse_fn, batch_size=batch_size, num_parallel_calls=8))
dataset = dataset.prefetch(10000)
# dataset = dataset.padded_batch(batch_size, padded_shapes=({"feeds_ids": [None], "feeds_vals": [None], "title_ids": [None]}, [None])) #不定长补齐
#return dataset.make_one_shot_iterator()
iterator = dataset.make_one_shot_iterator()
batch_features, batch_labels = iterator.get_next()
......@@ -348,6 +141,9 @@ def model_fn(features, labels, mode, params):
city = features['city']
cid_id = features['cid_id']
if FLAGS.task_type != "infer":
y = labels['y']
z = labels['z']
#------build f(x)------
with tf.variable_scope("Shared-Embedding-layer"):
......@@ -381,6 +177,12 @@ def model_fn(features, labels, mode, params):
for i in range(len(layers)):
x_cvr = tf.contrib.layers.fully_connected(inputs=x_cvr, num_outputs=layers[i], \
weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='cvr_mlp%d' % i)
if FLAGS.batch_norm:
x_cvr = batch_norm_layer(x_cvr, train_phase=train_phase, scope_bn='cvr_bn_%d' %i) #放在RELU之后 https://github.com/ducha-aiki/caffenet-benchmark/blob/master/batchnorm.md#bn----before-or-after-relu
if mode == tf.estimator.ModeKeys.TRAIN:
x_cvr = tf.nn.dropout(x_cvr, keep_prob=dropout[i]) #Apply Dropout after all BN layers and set dropout=0.8(drop_ratio=0.2)
y_cvr = tf.contrib.layers.fully_connected(inputs=x_cvr, num_outputs=1, activation_fn=tf.identity, \
weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='cvr_out')
y_cvr = tf.reshape(y_cvr,shape=[-1])
......@@ -395,6 +197,12 @@ def model_fn(features, labels, mode, params):
for i in range(len(layers)):
x_ctr = tf.contrib.layers.fully_connected(inputs=x_ctr, num_outputs=layers[i], \
weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='ctr_mlp%d' % i)
if FLAGS.batch_norm:
x_ctr = batch_norm_layer(x_ctr, train_phase=train_phase, scope_bn='ctr_bn_%d' %i) #放在RELU之后 https://github.com/ducha-aiki/caffenet-benchmark/blob/master/batchnorm.md#bn----before-or-after-relu
if mode == tf.estimator.ModeKeys.TRAIN:
x_ctr = tf.nn.dropout(x_ctr, keep_prob=dropout[i]) #Apply Dropout after all BN layers and set dropout=0.8(drop_ratio=0.2)
y_ctr = tf.contrib.layers.fully_connected(inputs=x_ctr, num_outputs=1, activation_fn=tf.identity, \
weights_regularizer=tf.contrib.layers.l2_regularizer(l2_reg), scope='ctr_out')
y_ctr = tf.reshape(y_ctr,shape=[-1])
......@@ -404,7 +212,8 @@ def model_fn(features, labels, mode, params):
pcvr = tf.sigmoid(y_cvr)
pctcvr = pctr*pcvr
predictions={"pcvr": pcvr, "pctr": pctr, "pctcvr": pctcvr, "sample_id": sample_id, "uid":uid, "city":city, "cid_id":cid_id}
predictions={"pctcvr": pctcvr, "sample_id": sample_id, "uid":uid, "city":city, "cid_id":cid_id}
export_outputs = {tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: tf.estimator.export.PredictOutput(predictions)}
# Provide an estimator spec for `ModeKeys.PREDICT`
if mode == tf.estimator.ModeKeys.PREDICT:
......@@ -413,6 +222,52 @@ def model_fn(features, labels, mode, params):
predictions=predictions,
export_outputs=export_outputs)
if FLAGS.task_type != "infer":
#------bulid loss------
ctr_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_ctr, labels=y))
#cvr_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_ctcvr, labels=z))
cvr_loss = tf.reduce_mean(tf.losses.log_loss(predictions=pctcvr, labels=z))
loss = ctr_task_wgt * ctr_loss + (1 -ctr_task_wgt) * cvr_loss + l2_reg * tf.nn.l2_loss(Feat_Emb)
tf.summary.scalar('ctr_loss', ctr_loss)
tf.summary.scalar('cvr_loss', cvr_loss)
# Provide an estimator spec for `ModeKeys.EVAL`
eval_metric_ops = {
# "CTR_AUC": tf.metrics.auc(y, pctr),
#"CTR_F1": tf.contrib.metrics.f1_score(y,pctr),
#"CTR_Precision": tf.metrics.precision(y,pctr),
#"CTR_Recall": tf.metrics.recall(y,pctr),
# "CVR_AUC": tf.metrics.auc(z, pcvr),
"CTCVR_AUC": tf.metrics.auc(z, pctcvr)
}
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
eval_metric_ops=eval_metric_ops)
#------bulid optimizer------
if FLAGS.optimizer == 'Adam':
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-8)
elif FLAGS.optimizer == 'Adagrad':
optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate, initial_accumulator_value=1e-8)
elif FLAGS.optimizer == 'Momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.95)
elif FLAGS.optimizer == 'ftrl':
optimizer = tf.train.FtrlOptimizer(learning_rate)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
# Provide an estimator spec for `ModeKeys.TRAIN` modes
if mode == tf.estimator.ModeKeys.TRAIN:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op)
def batch_norm_layer(x, train_phase, scope_bn):
bn_train = tf.contrib.layers.batch_norm(x, decay=FLAGS.batch_norm_decay, center=True, scale=True, updates_collections=None, is_training=True, reuse=None, scope=scope_bn)
bn_infer = tf.contrib.layers.batch_norm(x, decay=FLAGS.batch_norm_decay, center=True, scale=True, updates_collections=None, is_training=False, reuse=True, scope=scope_bn)
......
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